def main():
kernel = c.COSINE
# training parameter
result_path = 'results/PB2_spam.acc'
model_name = 'digits_' + kernel
tr_data_path = 'data\\digits\\tr_f_l_10.pickle'
te_data_path = 'data\\digits\\te_f_l_10.pickle'
# laod and preprocess training data
tr_data = loader.load_pickle_file(tr_data_path)
te_data = loader.load_pickle_file(te_data_path)
# transpose label
tr_data[1] = np.transpose(tr_data[1])[0]
te_data[1] = np.transpose(te_data[1])[0]
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, tr_data[0])
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, te_data[0])
# start training
st = time.time()
# start training
print('{:.2f} Start training.'.format(time.time() - st))
for r in (0.15, 0.1):
clf = kNN.kNN(kernel=kernel, dataset=c.DS_DIGITS)
clf.fit(tr_data[0], tr_data[1])
tr_pred = clf.predict(tr_data[0], r=r)
te_pred = clf.predict(te_data[0], r=r)
tr_acc = (tr_data[1] == tr_pred).sum() / tr_data[0].shape[0]
te_acc = (te_data[1] == te_pred).sum() / te_data[0].shape[0]
print('{} Final results with kernel {} and r={}. Train acc: {}, Test acc: {}'.format(time.time() - st, kernel, r, tr_acc, te_acc))
def main():
target = 'v2'
# training parameter
k = 10 # fold
layer_thresh = 2
T = 50
threshes_path = 'data/spambase.threshes'
# laod and preprocess training data
training_data = loader.load_dataset('data/spambase.data')
# load thresholds
threshes = loader.load_pickle_file(threshes_path)
# start training
k_folds = Preprocess.prepare_k_folds(training_data, k)
tr_data, te_data = Preprocess.get_i_fold(k_folds, 0)
f_cur = [x[0] for x in tr_data[0]]
t = dt.DecisionTree()
if target == 'v1':
for i in range(100):
h_y = t.compute_entropy(tr_data[1])
thresh = threshes[0][30]
ig = t.compute_ig(f_cur, tr_data[1], thresh, h_y)
else:
h_y = t.compute_entropy_v2(tr_data[1])
thresh = threshes[0][0]
ig = t.compute_ig_v2(f_cur, tr_data[1], thresh, h_y)
def main():
# training parameter
result_path = 'results/housingLiR_1.mse'
model_name = 'housing_shiftAndScale'
# normalization = Preprocess.zero_mean_unit_var
normalization = Preprocess.shift_and_scale
# cols_not_norm = (0,7,12)
cols_not_norm = []
# laod and preprocess training data
training_data = loader.load_dataset('data/housing_train.txt')
testing_data = loader.load_dataset('data/housing_test.txt')
Preprocess.normalize_features_all(normalization, training_data[0], testing_data[0], cols_not_norm)
# start training
model = rm.LinearRegression()
model.build(training_data[0], training_data[1])
training_mse = model.test(training_data[0], training_data[1], util.mse)
testing_mse = model.test(testing_data[0], testing_data[1], util.mse)
print 'Error for training data is:'
print training_mse
print 'Error for testing data is:'
print testing_mse
result = {}
result['TrainingMSE'] = str(training_mse)
result['TestingMSE'] = str(testing_mse)
result['Theta'] = str(model.theta)
# log the training result to file
util.write_result_to_file(result_path, model_name, result)
def main():
is_sklearn = False
# kernel = c.COSINE
# kernel = c.GAUSSIAN
kernel = c.POLY
# training parameter
result_path = 'results/PB2_spam.acc'
model_name = 'digits_' + kernel
model_path = 'data/PB1_B_digits_sk_Gaussian_1.model'
# tr_data_path = 'data\\digits\\tr_f_l.pickle'
# te_data_path = 'data\\digits\\te_f_l.pickle'
tr_data_path = 'data\\digits\\tr_f_l_10.pickle'
te_data_path = 'data\\digits\\te_f_l_10.pickle'
# laod and preprocess training data
tr_data = loader.load_pickle_file(tr_data_path)
te_data = loader.load_pickle_file(te_data_path)
# transpose label
tr_data[1] = np.transpose(tr_data[1])[0]
te_data[1] = np.transpose(te_data[1])[0]
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, tr_data[0])
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, te_data[0])
# start training
models = []
st = time.time()
# start training
print('{:.2f} Start training.'.format(time.time() - st))
for k in (1, 3, 7):
if not is_sklearn:
clf = kNN.kNN(kernel=kernel)
clf.fit(tr_data[0], tr_data[1])
tr_pred = clf.predict(tr_data[0], k=k)
te_pred = clf.predict(te_data[0], k=k)
else:
clf = KNeighborsClassifier(n_neighbors=k, metric=cosine_distances)
clf.fit(tr_data[0], tr_data[1])
tr_pred = clf.predict(tr_data[0])
te_pred = clf.predict(te_data[0])
tr_acc = (tr_data[1] == tr_pred).sum() / tr_data[0].shape[0]
te_acc = (te_data[1] == te_pred).sum() / te_data[0].shape[0]
models.append(clf)
print('{} Final results with kernel {} and k={}. Train acc: {}, Test acc: {}'.format(time.time() - st, kernel, k, tr_acc, te_acc))
def get_tf_idf(query, src="google"):
tokens = get_tokens(query, src)
#converts into a dictionary
query_dictionary = Preprocess.list_to_dict(tokens,{})
#print query_dictionary
#creates a dictionary from a random wikipedia corpus
#dictionary = Preprocess.get_corpus(num_articles)
#loads in the dictionary of existing tf_idf words
dictionary = load_idf()
tf_idf_dictionary = {}
#calculates the tfidf for each key, storing it in a new dictionary
for key in query_dictionary.keys():
tf = query_dictionary[key]
if key in dictionary:
idf = dictionary[key]
else:
idf = math.log(18,10)
tf_idf_dictionary[key] = (float(tf)*float(idf))
#print tf_idf_dictionary
#sorts the dictionary based on the tfidf value, returning it as a list
sorted_dictionary = sorted(tf_idf_dictionary.iteritems(), key=operator.itemgetter(1), reverse = True)
return sorted_dictionary
def detectPlatesInScene(imgOriginalScene):
possiblePlates = []
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
cv2.destroyAllWindows()
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene)
possibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(possibleCharsInScene)
for matchingChars in listOfListsOfMatchingCharsInScene:
possiblePlate = extractPlate(imgOriginalScene, matchingChars)
if possiblePlate.imgPlate is not None:
possiblePlates.append(possiblePlate)
print "\n" + str(len(possiblePlates)) + " possible plates found"
return possiblePlates
def main():
# training parameter
is_sklearn = True
k = 10 # fold
result_path = 'results/PB2_spam.acc'
model_name = 'spam_' + str(k) + 'fold'
data_path = 'data/spam/data.pickle'
# laod and preprocess training data
training_data = loader.load_pickle_file(data_path)
# TODO convert labels from {0, 1} to {-1, 1}
# util.replace_zero_label_with_neg_one(training_data)
# Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, training_data[0])
# training_data[0] = preprocessing.scale(training_data[0])
# start training
training_errs = []
testing_errs = []
print('Preparing k fold data.')
k_folds = Preprocess.prepare_k_folds(training_data, k)
for i in (0,):
st = time.time()
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
# start training
print('{:.2f} Start training.'.format(time.time() - st))
kernel = c.EUCLIDEAN
# kernel = c.GAUSSIAN
f_select = True
best_features_num = 5
clf = kNN.kNN(kernel=kernel)
clf.fit(tr_data[0], tr_data[1], f_select=f_select, best_f=best_features_num)
print("Best features: {}".format(clf.best_f_indices))
for kk in (1, 2, 3, 7):
tr_pred = clf.predict(tr_data[0], k=kk)
te_pred = clf.predict(te_data[0], k=kk)
tr_acc = (tr_data[1] == tr_pred).sum() / tr_data[0].shape[0]
te_acc = (te_data[1] == te_pred).sum() / te_data[0].shape[0]
print('{} Final results with kernel {}, k={}. Train acc: {}, Test acc: {}'.format(time.time() - st, kernel, kk, tr_acc, te_acc))
def main():
# training parameter
k = 8 # fold
result_path = 'results/PB2_spam.acc'
model_name = 'spam_' + str(k) + 'fold'
data_path = 'data/spam/data.pickle'
# laod and preprocess training data
training_data = loader.load_pickle_file(data_path)
# TODO convert labels from {0, 1} to {-1, 1}
# util.replace_zero_label_with_neg_one(training_data)
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, training_data[0])
# Preprocess.normalize_features_all(Preprocess.shifiat_and_scale, training_data[0])
# start training
training_accs = []
testing_accs = []
print('Preparing k fold data.')
k_folds = Preprocess.prepare_k_folds(training_data, k)
kernel = c.EUCLIDEAN
sst = time.time()
for i in (1,):
st = time.time()
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
# start training
print('{:.2f} Start training.'.format(time.time() - st))
for r in (2.5, 2.7):
clf = kNN.kNN(kernel=kernel)
# clf.fit(training_data[0], training_data[1])
clf.fit(tr_data[0], tr_data[1])
# tr_pred = clf.predict(training_data[0], r=r)
tr_pred = clf.predict(tr_data[0], r=r)
te_pred = clf.predict(te_data[0], r=r)
# tr_acc = (training_data[1] == tr_pred).sum() / training_data[0].shape[0]
tr_acc = (tr_data[1] == tr_pred).sum() / tr_data[0].shape[0]
te_acc = (te_data[1] == te_pred).sum() / te_data[0].shape[0]
testing_accs.append(te_acc)
print('{} {}-fold results with kernel {}, r={}. Train acc: {}, Test acc: {}'.format(time.time() - st, i, kernel, r, tr_acc, te_acc))
def main():
# training parameter
k = 10 # fold
result_path = "results/PB1_A_spam.acc"
model_name = "spam_" + str(k) + "fold"
threshes_path = "data/spambase.threshes"
data_path = "data/spam/data.pickle"
# kernel = 'poly'
kernel = "linear"
# kernel = 'rbf'
verbose = False
tol = 0.01
c = 0.1
# laod and preprocess training data
training_data = loader.load_pickle_file(data_path)
# TODO convert labels from {0, 1} to {-1, 1}
util.replace_zero_label_with_neg_one(training_data)
# normalize
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, training_data[0])
print("Preparing k fold data.")
k_folds = Preprocess.prepare_k_folds(training_data, k)
for i in range(1):
st = time.time()
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
# start training
print("{:3f} Start training. Kernel: {}".format(time.time() - st, kernel))
clf = svm.SVC(C=c, kernel=kernel, tol=tol, verbose=verbose)
# clf = svm.NuSVC(kernel=kernel, tol=tol, verbose=verbose)
clf.fit(tr_data[0], tr_data[1])
tr_pred = clf.predict(tr_data[0])
te_pred = clf.predict(te_data[0])
tr_acc = (tr_data[1] == tr_pred).sum() / tr_data[0].shape[0]
te_acc = (te_data[1] == te_pred).sum() / te_data[0].shape[0]
print("{:3f} Final results. Train acc: {}, Test acc: {}".format(time.time() - st, tr_acc, te_acc))
def analyze_data(time_interval=TimeInterval, refined_type=FullyPreprocessedPath):
print 'time_interval: ' + str(time_interval) + ' min'
print 'refined_type: ' + refined_type
print '--------------------------------------------'
# Refine the data and save
refined_data_path = Preprocess.preprocess_data(time_interval, refined_type)
# Build similarity model and save
Similarity.Build.similarity_model(time_interval, refined_type)
# Set data for visualization
Visualization.set_data4visualization(time_interval, refined_type)
def analyze_data(time_interval=TimeInterval, refined_type=FullyPreprocessedPath):
print 'time_interval: ' + str(time_interval) + ' min'
print 'refined_type: ' + refined_type
print '--------------------------------------------'
# Draw graphs and save the figures
graph_directory = Graph.Save.raw_data2graph()
# Refine the data and save
refined_data_path = Preprocess.refining_data(time_interval, refined_type)
# Build similarity model and save
Similarity.Build.similarity_model(time_interval, refined_type)
# Set data for visualization
Visualization.set_data4visualization(time_interval, refined_type)
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0:
return listOfPossiblePlates
for possiblePlate in listOfPossiblePlates:
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(possiblePlate.imgPlate)
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0), fx = 1.6, fy = 1.6) # povecavanje velicine slike
thresholdValue, possiblePlate.imgThresh = cv2.threshold(possiblePlate.imgThresh, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(possiblePlate.imgGrayscale, possiblePlate.imgThresh)
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(listOfPossibleCharsInPlate)
if (len(listOfListsOfMatchingCharsInPlate) == 0):
possiblePlate.strChars = ""
continue
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
listOfListsOfMatchingCharsInPlate[i].sort(key = lambda matchingChar: matchingChar.intCenterX)
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(listOfListsOfMatchingCharsInPlate[i])
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[intIndexOfLongestListOfChars]
possiblePlate.strChars = recognizeCharsInPlate(possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
return listOfPossiblePlates
def main():
#define new objects
preprocess = Preprocess()
process = Process()
points = Points()
postprocess = Postprocess()
#declare and initialize variables
search_string = ''
option = 0
count2 = 0 #delete this
reordered_search_string = ''
permutation_set = set()
temp_permutation_set = set()
permutation_list = [] #2D list
blank_permutation_list = []
filtered_content = []
sorted_results = []
final_results = []
sorted_final_results = []
#menu options
print "\nSearch options:\n"
print "1. Search for words"
print "2. Search for words starting with"
print "3. Search for words ending with"
print "4. Search for words containing"
print "5. Search with blank tiles (use the underscore character to represent blanks)\n"
#option = int(raw_input("Choose option:"))
option = 1
#search_string = raw_input('Please input tiles for search: ').lower()
search_string = "andrew"
#basic input check
if (preprocess.checkInput(search_string)):
reordered_search_string = preprocess.reorderString(search_string) #alphabetize tiles
else:
sys.exit()
t1 = time.time() #diagnostics
#Input(search_string, option) #turned into function for testing purposes
if (option == 0): #no option chosen
print "ERROR: No option chosen, exiting."
sys.exit()
elif(option == 1):
print "Searching for words...\n"
permutation_list = process.stringPermutations(reordered_search_string)
filtered_content = process.collectDictionarySegments(reordered_search_string)
sorted_results = process.findWords(permutation_list, filtered_content)
final_results = points.associatePointScore(sorted_results)
elif(option == 2):
print "Searching for words starting with: ", search_string, "\n"
filtered_content = process.collectDictionarySegments(search_string[0]) #get first letter int he word being searched
sorted_results = process.findWordsContaining(search_string, filtered_content, option)
final_results = points.associatePointScore(sorted_results)
elif(option == 3):
print "Searching for words ending in: ", search_string, "\n"
alphabet = 'abcdefghijklmnopqrstuvwxyz'
filtered_content = process.collectDictionarySegments(alphabet)
sorted_results = process.findWordsContaining(search_string, filtered_content, option)
final_results = points.associatePointScore(sorted_results)
elif(option == 4):
print "Searching for words containing: ", search_string, "\n"
alphabet = 'abcdefghijklmnopqrstuvwxyz'
filtered_content = process.collectDictionarySegments(alphabet)
sorted_results = process.findWordsContaining(search_string, filtered_content, option)
final_results = points.associatePointScore(sorted_results)
elif(option == 5):
print "Searching with blank tiles...\n"
alphabet = 'abcdefghijklmnopqrstuvwxyz'
blank_permutation_list = process.blankTileProcessing(reordered_search_string)
filtered_content = process.collectDictionarySegments(alphabet)
#TO DO: Creates a 2D list, gotta convert to 1D list - DONE
#TO DO: find way to use union keyword to take out duplicates, it will take care of one nested for loop in findWords function - DONE
#TO DO: Do another union - DONE
# time vs duplication trade off. Takes longer to take out the duplicates with the union
for blank_permutation_string in blank_permutation_list:
#permutation_list.extend(process.stringPermutations(blank_permutation_string))
temp_permutation_set = set(process.stringPermutations(blank_permutation_string))
permutation_set = permutation_set.union(temp_permutation_set)
permutation_list = list(permutation_set)
sorted_results = process.findWords(permutation_list, filtered_content)
final_results = points.associatePointScore(sorted_results)
else:
print "ERROR: Please choose an option between 1-5"
sys.exit()
t2 = time.time() - t1 #diagnostics
sorted_option = 0
print "Results found and processed. Sort results by...\n"
print "1. Points - lowest to highest"
print "2. Points - highest to lowest"
print "3. Length - longest to shortest"
print "4. Length - shortest to longest"
sorted_option = int(raw_input("choose option: "))
print "Option", sorted_option, "chosen"
if (sorted_option == 1):
print "Sorting results by points, highest to lowest\n"
sorted_final_results = postprocess.resultsByPoints(final_results)
elif (sorted_option == 2):
print "Sorting results by points, lowest to highest\n"
sorted_final_results = postprocess.resultsByPointsReverse(final_results)
elif (sorted_option == 3):
print "Sorting results by length, longest to shortest\n"
sorted_final_results = postprocess.resultsByLength(final_results)
elif (sorted_option == 4):
print "Sorting results by length, shortest to longest\n"
sorted_final_results = postprocess.resultsByLengthReverse(final_results)
else:
print "Option 1-4 not chosen, outputting results by default order"
sorted_final_results = final_results
Output(sorted_final_results, t2)
sys.path.append('..')
import numpy as np
import rbm_rm
import rbm_cm
import matplotlib.pyplot as plt
import utils
import Preprocess
mnist_dir = os.path.join(os.environ['DATA_HOME'], 'mnist')
mnist_train_path = os.path.join(mnist_dir, 'MNISTTrainData.npy')
data_rm = np.load(mnist_train_path)
[normed, meanv, stdv] = Preprocess.mean_zero_unit_variance(data_rm)
#Look, I didn't actually use the normalized data because it broke everything
train_rm = data_rm[30000:, :]
valid_rm = data_rm[:30000, :]
data_cm = data_rm.transpose()
train_cm = data_cm[:,30000:]
valid_cm = data_cm[:,:30000]
nHidden = 100
ViewDimensions = (10, 10) # Should multiply to nHidden
TP = rbm_rm.RBMTrainParams()
TP.maxepoch = 15
rm_learner = rbm_rm.GV_RBM(nHidden, train_rm.shape[1])
# training parameter
result_path = 'results/housingLiRGD_1.mse'
model_name = 'housing'
lamda = 0.0001 # 0.000015
is_batch = False
# normalization = Preprocess.zero_mean_unit_var
normalization = Preprocess.shift_and_scale
term_fun = util.mse_less_than
term_thresh = 25
cols_not_norm = [0,7]
# laod and preprocess training data
training_data = loader.load_dataset('data/housing_train.txt')
testing_data = loader.load_dataset('data/housing_test.txt')
Preprocess.normalize_features_all(normalization, training_data[0], testing_data[0], not_norm=cols_not_norm)
# start training
model = gd.LinearRegressionGD()
model.build(training_data[0], training_data[1], lamda, term_fun, term_thresh, is_batch)
try:
pass
except KeyboardInterrupt:
print 'Interrupted'
finally:
training_mse = model.test(training_data[0], training_data[1], util.mse)
testing_mse = model.test(testing_data[0], testing_data[1], util.mse)
print 'Error for training data is:'
print training_mse
print 'Error for testing data is:'
print testing_mse
def main():
# training parameter
target = 'crx'
# target = 'vote'
k = 10 # fold
round_limit = 150
if target == 'crx':
result_path = 'results/crxBoosting_final_1.acc'
model_name = 'crx_' + str(k) + 'fold'
threshes_path = 'data/crx.threshes'
data_path = 'data/crx_parsed.data'
else:
result_path = 'results/voteBoosting_final.acc'
model_name = 'vote_' + str(k) + 'fold'
threshes_path = 'data/vote.threshes'
data_path = 'data/vote_parsed.data'
# laod and preprocess training data
training_data = loader.load_pickle_file(data_path)
# load thresholds
threshes = loader.load_pickle_file(threshes_path)
# start training
training_errs = []
testing_errs = []
round_err_1st_boost = None
tr_errs_1st_boost = None
te_errs_1st_boost = None
te_auc_1st_boost = None
roc = []
auc = 0.0
k_folds = Preprocess.prepare_k_folds(training_data, k)
for i in range(k):
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
tr_n, f_d = np.shape(tr_data[0])
te_n, = np.shape(te_data[1])
# TODO prepare distribution
d = util.init_distribution(len(tr_data[0]))
# TODO compute thresholds cheat sheet
thresh_cs = util.pre_compute_threshes_uci(tr_data[0], tr_data[1], threshes)
boost = b.Boosting(d)
testing_predict = np.zeros((1, te_n)).tolist()[0]
training_predict = np.zeros((1, tr_n)).tolist()[0]
round_tr_err = []
round_te_err = []
round_model_err = []
round_te_auc = []
converged = False
tol = 1e-5
te_auc = 2.
round = 0
while round < round_limit: # and not converged:
round += 1
boost.add_model(ds.DecisionStump, tr_data[0], tr_data[1], threshes, thresh_cs)
boost.update_predict(tr_data[0], training_predict)
boost.update_predict(te_data[0], testing_predict)
c_model_err = boost.model[-1].w_err
round_model_err.append(c_model_err)
c_f_ind = boost.model[-1].f_ind
c_thresh = boost.model[-1].thresh
c_tr_err = util.get_err_from_predict(training_predict, tr_data[1])
c_te_err = util.get_err_from_predict(testing_predict, te_data[1])
# TODO calculate the AUC for testing results
# c_te_auc = util.get_auc_from_predict(testing_predict, te_data[1])
# round_tr_err.append(c_tr_err)
# round_te_err.append(c_te_err)
# round_te_auc.append(c_te_auc)
print('Round: {} Feature: {} Threshold: {} Round_err: {:.12f} Train_err: {:.12f} Test_err {:.12f}'.format(round, c_f_ind, c_thresh, c_model_err, c_tr_err, c_te_err))
# converged = abs(c_te_auc - te_auc) / te_auc <= tol
# te_auc = c_te_auc
training_errs.append(c_tr_err)
testing_errs.append(c_te_err)
# if k == 0:
# round_err_1st_boost = round_model_err
# tr_errs_1st_boost = round_tr_err
# te_errs_1st_boost = round_te_err
# te_auc_1st_boost = round_te_auc
# break # for testing
mean_training_err = np.mean(training_errs)
mean_testing_err = np.mean(testing_errs)
print(str(k) + '-fold validation done. Training errs are:')
print(training_errs)
print('Mean training err is:')
print(mean_training_err)
print('Testing errs are:')
print(testing_errs)
print('Mean testing err is:')
print(mean_testing_err)
result = {}
result['Fold'] = str(k)
result['Trainingerrs'] = str(training_errs)
result['MeanTrainingAcc'] = str(mean_training_err)
result['Testingerrs'] = str(testing_errs)
result['MeanTestingAcc'] = str(mean_testing_err)
result['1stBoostTrainingError'] = str(tr_errs_1st_boost)
result['1stBoostTestingError'] = str(te_errs_1st_boost)
result['1stBoostModelError'] = str(round_err_1st_boost)
result['1stBoostTestingAUC'] = str(te_auc_1st_boost)
# result['ROC'] = str(roc)
result['AUC'] = str(auc)
# log the training result to file
util.write_result_to_file(result_path, model_name, result)
def GenerateMake(inputDir, outputDir, toolsRoot, linkerStage='elf'):
def MakeFileList(s, outDir, extension):
return list(
map(lambda x: "{0}\\{1}.{2}".format(outDir, x.stem, extension), s))
def FillCompilerTemplate(dest, source):
print("compile {0} => {1}".format(source, dest))
langSpec = ""
lang = ""
sourcePath = pathlib.Path(source)
if sourcePath.suffix == '.c':
langSpec = "-std=gnu99"
lang = "-x c"
m = {
'lang': lang,
'langSpec': langSpec,
'objectFile': str(dest),
'sourceFile': str(source)
}
t = string.Template(compileSrcTemplate)
return t.safe_substitute(m)
p = pathlib.Path(inputDir)
print(inputDir)
print("********************************")
sources = [x for x in p.iterdir() if x.suffix == ('.c')]
print(sources)
Preprocess.InitPreprocessor([], [])
for s in sources:
Preprocess.Rewrite(str(s), outputDir)
Preprocess.DumpTraceRecords(outputDir)
asmSources = [x for x in p.iterdir() if x.suffix == ('.s')]
asmSourceList = list(map(str, asmSources))
print(asmSources)
ppSourceList = MakeFileList(sources, outputDir, 'c') + asmSourceList
print(ppSourceList)
objectList = MakeFileList(sources + asmSources, outputDir, 'o')
print(objectList)
flashTarget = ''
if linkerStage == 'elf': #otherwise we should not generate a flash target!
flashTarget = string.Template(flashTargetTemplate).safe_substitute(
programmer=targetConfigs['programmer'],
port=targetConfigs['port'],
device=targetConfigs['device'])
device = targetConfigs['device']
AvrFamily = '__AVR_DEV_LIB_NAME__={0}'.format(device)
if device == 'm328p':
AvrFamily = '__AVR_ATmega328P__=1'
replaceMap = {
'linkerStage':
eval("{0}LinkerTemplate".format(linkerStage)),
'cSources':
" ".join(map(str, sources)),
'AvrFamily':
AvrFamily,
'AvrFamilyPath':
targetConfigs['target'],
'asmSources':
"",
'ToolsRoot':
toolsRoot,
'ppSources':
" ".join(ppSourceList),
'objects':
" ".join(objectList),
'cDependencies':
" ".join(MakeFileList(sources, outputDir, 'd')),
'compileSources':
"\n".join(list(map(FillCompilerTemplate, objectList, ppSourceList))),
'target':
targetConfigs['target'],
'flashTarget':
flashTarget,
'extension':
linkerStage
}
ppSourceList = None
objectList = None
temp = string.Template(makeFileTemplate)
makeFile = temp.safe_substitute(replaceMap)
with open(pathlib.Path(outputDir) / "Makefile", "w") as f:
f.write(makeFile)
def detectCharsInPlates(listOfPossiblePlates):
PlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0:
return listOfPossiblePlates
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates:
possiblePlate.imgGray, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate)
if Main.showSteps == True:
cv2.imshow("Possible image plate", possiblePlate.imgPlate)
cv2.imshow("Possible gray scale image plate",
possiblePlate.imgGray)
cv2.imshow("Possible plate image threshold",
possiblePlate.imgThresh)
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True:
cv2.imshow(
"Possible image plate threshold after eliminating gray areas",
possiblePlate.imgThresh)
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGray, possiblePlate.imgThresh)
if Main.showSteps == True:
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, numChannels), np.uint8)
# clear the contours list
del contours[:]
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("Image Contours", imgContours)
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if Main.showSteps == True:
imgContours = np.zeros((height, width, numChannels), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
RandomBlue = random.randint(0, 255)
RandomGreen = random.randint(0, 255)
RandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1,
(RandomBlue, RandomGreen, RandomRed))
cv2.imshow("Image Contours 2", imgContours)
# if no groups of matching chars were found in the plate
if (len(listOfListsOfMatchingCharsInPlate) == 0):
if Main.showSteps == True:
print(
"chars found in plate number " + str(PlateCounter) +
" = (none), click on any image and press a key to continue . . ."
)
PlateCounter = PlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
possiblePlate.strChars = ""
continue
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
# sort chars from left to right
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.CenterX)
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) # and remove inner overlapping chars
if Main.showSteps == True:
imgContours = np.zeros((height, width, numChannels), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
RandomBlue = random.randint(0, 255)
RandomGreen = random.randint(0, 255)
RandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1,
(RandomBlue, RandomGreen, RandomRed))
cv2.imshow("Image Contours 3", imgContours)
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
LenOfLongestListOfChars = 0
IndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > LenOfLongestListOfChars:
LenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
IndexOfLongestListOfChars = i
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
IndexOfLongestListOfChars]
if Main.showSteps == True:
imgContours = np.zeros((height, width, numChannels), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("Image Conours 4", imgContours)
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True:
print("chars found in plate number " + str(PlateCounter) + " = " +
possiblePlate.strChars +
", click on any image and press a key to continue . . .")
PlateCounter = lateCounter + 1
cv2.waitKey(0)
if Main.showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
return listOfPossiblePlates
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(possiblePlate.imgPlate) # preprocess to get grayscale and threshold images
if Main.showSteps == True:
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
# increase size of plate image for easier viewing and char detection
# Daha kolay görüntüleme ve karakter algılama için plaka görüntüsünün boyutunu artırın
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0), fx = 1.6, fy = 1.6)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(possiblePlate.imgThresh, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True:
cv2.imshow("5d", possiblePlate.imgThresh)
# find all possible chars in the plate,
#Plakada ki tüm olası karakterleri bul
# Bu işlem tüm contpursları bulur.
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True:
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # contours listesi temizleme
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
# given a list of all possible chars, find groups of matching chars within the plate
# Olası tüm karakterleri listede verir, plakada ki grupları bulurç
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(listOfPossibleCharsInPlate)
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
#farklı grupları renklendirdik
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("7", imgContours)
if (len(listOfListsOfMatchingCharsInPlate) == 0): # if no groups of matching chars were found in the plate
if Main.showSteps == True:
print("chars found in plate number " + str(
intPlateCounter) + " = (none), click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
possiblePlate.strChars = ""
continue # go back to top of for loop
for i in range(0, len(listOfListsOfMatchingCharsInPlate)): # within each list of matching chars
listOfListsOfMatchingCharsInPlate[i].sort(key = lambda matchingChar: matchingChar.intCenterX) # sort chars from left to right
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(listOfListsOfMatchingCharsInPlate[i]) # and remove inner overlapping chars
# end for
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("8", imgContours)
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
# Plaka olması muhtemel olan parçalar listeye atıldıktan sonra, listeden en uzun parça plaka olarak seçilir.
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
#En uzun eşleşen gerçek karakter listesi olarak ele alınır.
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[intIndexOfLongestListOfChars]
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
possiblePlate.strChars = recognizeCharsInPlate(possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True:
print("chars found in plate number " + str(
intPlateCounter) + " = " + possiblePlate.strChars + ", click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end of big for loop that takes up most of the function
if Main.showSteps == True:
print("\nchar detection complete, click on any image and press a key to continue . . .\n")
cv2.waitKey(0)
return listOfPossiblePlates
def main():
# argument for input video/image/calibration
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="Path to video file")
ap.add_argument("-i", "--image", help="Path to the image")
ap.add_argument("-c", "--calibration", help="image or video or camera")
args = vars(ap.parse_args())
img_original_scene = None
loop = None
camera = None
# if -c assigned, calibrate the angle of camera or video
if args.get("calibration", True):
img_original_scene = cv2.imread(args["calibration"])
if img_original_scene is None:
print("Please check again the path of image or argument !")
img_original_scene = imutils.resize(img_original_scene, width=720)
cal.calibration(img_original_scene)
return
else: # run video / image / cam
if args.get("video", True):
camera = cv2.VideoCapture(args["video"])
if camera is None:
print("Please check again the path of video or argument !")
loop = True
elif args.get("image", True):
img_original_scene = cv2.imread(args["image"])
if img_original_scene is None:
print("Please check again the path of image or argument !")
loop = False
else:
camera = cv2.VideoCapture(0)
loop = True
# Load and check KNN Model
assert DetectChars.loadKNNDataAndTrainKNN(), "KNN can't be loaded !"
save_number = 0
prev_license = ""
licenses_verify = []
# Looping for Video
while loop:
# grab the current frame
(grabbed, frame) = camera.read()
if args.get("video") and not grabbed:
break
# resize the frame and preprocess
img_original_scene = imutils.resize(frame, width=620)
_, img_thresh = pp.preprocess(img_original_scene)
# Show the preprocess result
cv2.imshow("threshold", img_thresh)
# Get the license in frame
img_original_scene = imutils.transform(img_original_scene)
img_original_scene, new_license = searching(img_original_scene, loop)
# only save 5 same license each time (verification)
if new_license == "":
print("no characters were detected\n")
else:
if len(licenses_verify) == N_VERIFY and len(
set(licenses_verify)) == 1:
if prev_license == new_license:
print(f"still = {prev_license}\n")
else:
# show and save verified plate
print(
f"A new license plate read from image = {new_license} \n"
)
cv2.imshow(new_license, img_original_scene)
file_name = f"hasil/{new_license}.png"
cv2.imwrite(file_name, img_original_scene)
prev_license = new_license
licenses_verify = []
else:
if len(licenses_verify) == N_VERIFY:
# drop first if reach the N_VERIFY
licenses_verify = licenses_verify[1:]
licenses_verify.append(new_license)
# add text and rectangle, just for information and bordering
cv2.putText(
img_original_scene,
"Press 's' to save frame to be 'save.png', for calibrating",
(10, 30),
cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255),
1,
bottomLeftOrigin=False)
cv2.rectangle(img_original_scene,
((img_original_scene.shape[1] // 2 - 230),
(img_original_scene.shape[0] // 2 - 80)),
((img_original_scene.shape[1] // 2 + 230),
(img_original_scene.shape[0] // 2 + 80)), SCALAR_GREEN,
3)
cv2.imshow("imgOriginalScene", img_original_scene)
key = cv2.waitKey(5) & 0xFF
# if 's' key pressed save the image
if key == ord('s'):
save_number = str(save_number)
savefileimg = "calib_knn/img_" + save_number + ".png"
savefileThr = "calib_knn/Thr_" + save_number + ".png"
# cv2.saveimage("save.png", imgOriginalScene)
cv2.imwrite(savefileimg, frame)
cv2.imwrite(savefileThr, img_thresh)
print("image save !")
save_number = int(save_number)
save_number = save_number + 1
if key == 27: # if the 'q' key is pressed, stop the loop
camera.release() # cleanup the camera and close any open windows
break
# For image only
if not loop:
img_original_scene = imutils.resize(img_original_scene, width=720)
cv2.imshow("original", img_original_scene)
imgGrayscale, img_thresh = pp.preprocess(img_original_scene)
cv2.imshow("threshold", img_thresh)
img_original_scene = imutils.transform(img_original_scene)
img_original_scene, new_license = searching(img_original_scene, loop)
print(f"license plate read from image = {new_license} \n")
cv2.waitKey(0)
cv2.waitKey(0)
cv2.destroyAllWindows()
return
k = 10 # fold
result_path = 'results/spamNBBern_1.acc'
model_name = 'spam_' + str(k) + 'fold_' + 'SS'
# laod and preprocess training data
training_data = loader.load_dataset('data/spambase.data')
# start training
training_accs = []
training_cms = []
testing_accs = []
testing_cms = []
roc = []
auc = 0.0
k_folds = Preprocess.prepare_k_folds(training_data, k)
means = loader.load_spam_mean('data/spam_mean')
for i in range(k):
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
model = m.NBBernoulli(means)
model.build(tr_data[0], tr_data[1])
training_test_res = model.test(tr_data[0], tr_data[1], util.compute_acc_confusion_matrix)
training_accs.append(training_test_res[0])
training_cms.append(training_test_res[1])
testing_test_res = model.test(te_data[0], te_data[1], util.compute_acc_confusion_matrix)
testing_accs.append(testing_test_res[0])
f.write(str(j) + " ")
f.write("\n")
f.write('\n')
for row in r:
for j in row:
f.write(str(j) + " ")
f.write("\n")
f.write("\n")
f.write(str(maxi))
while lrate < 1.0:
print(lrate)
z.write("FOR LRATE: " + str(lrate) + "\n")
z.write("IRIS:")
temp = P.main("Datasets/IRIS.csv", lrate)
a[0].append(temp[0])
p[0].append(temp[1])
r[0].append(temp[2])
z.write(str(temp[0]) + " " + str(temp[1]) + " " + str(temp[2]) + "\n")
z.write("SPECT:")
temp = P.main("Datasets/SPECT.csv", lrate)
a[1].append(temp[0])
p[1].append(temp[1])
r[1].append(temp[2])
z.write(str(temp[0]) + " " + str(temp[1]) + " " + str(temp[2]) + "\n")
z.write("SPECTF:")
temp = P.main("Datasets/SPECTF.csv", lrate)
a[2].append(temp[0])
p[2].append(temp[1])
r[2].append(temp[2])
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate
) # preprocess to get grayscale and threshold images
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6) #fx = 1.6, fy = 1.6
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if (len(listOfListsOfMatchingCharsInPlate) == 0
): # if no groups of matching chars were found in the plate
possiblePlate.strChars = ""
continue # go back to top of for loop
# end if
for i in range(0, len(listOfListsOfMatchingCharsInPlate)
): # within each list of matching chars
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX
) # sort chars from left to right
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) # and remove inner overlapping chars
# end for
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
return listOfPossiblePlates
def main():
# argument for input video/image/calibration
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to video file")
ap.add_argument("-i", "--image",
help = "Path to the image")
ap.add_argument("-c", "--calibration",
help = "image or video or camera")
args = vars(ap.parse_args())
if args.get("calibration", True):
imgOriginalScene = cv2.imread(args["calibration"])
if imgOriginalScene is None:
print("Please check again the path of image or argument !")
imgOriginalScene = imutils.resize(imgOriginalScene, width = 640)
cal.calibration(imgOriginalScene)
return
if args.get("video", True):
camera = cv2.VideoCapture(args["video"])
if camera is None:
print(" Please check again the path of video or argument !")
loop = True
elif args.get("image", True):
#imgOriginalScene = cv2.imread(args["image"])
imgOriginalScene = cv2.imread("media/gspeintercon/GSPE1/GSPE/OCR/plat4.JPG")
if imgOriginalScene is None:
print(" Please check again the path of image or argument !")
loop = False
else:
#camera = cv2.VideoCapture("rtsp://192.168.1.10:554/user=admin&password=&channel=1&stream=0.sdp?")
#camera = cv2.VideoCapture("rtsp://*****:*****@192.168.0.23:554/PSIA/streaming/channels/301")
camera = cv2.VideoCapture(0)
#camera = cv2.VideoCapture(0)
loop = True
# add knn library for detect chars
blnKNNTrainingSuccessful = DetectChars.loadKNNDataAndTrainKNN() # attempt KNN training
if blnKNNTrainingSuccessful == False: # if KNN training was not successful
print("\nerror: KNN traning was not successful\n") # show error message
return
count = 0
# not very important, just iterating for license array haha
license = []
VER = np.zeros(VERIF)
for x in VER:
license.append("")
numlicense = ""
knn = 0
# Looping for Video
while (loop):
# grab the current frame
(grabbed, frame) = camera.read()
#frame = camera.read()
if args.get("video") and not grabbed:
break
# resize the frame and convert it to grayscale
imgOriginalScene = imutils.resize(frame, width = 640)
imgGrayscale, imgThresh = pp.preprocess(imgOriginalScene)
cv2.imshow("threshold", imgThresh)
#imgOriginalScene = imutils.transform (imgOriginalScene)
imgOriginalScene, licenses = searching(imgOriginalScene,loop)
# only save 5 same license each time
license[count+1] = licenses
nums = license[VERIF-1]
if (license[count] == license[count+1]):
license[count]=license[count+1]
count = count + 1
elif (license[count] != license[count+1]):
coba = license[count+1]
count = 0
license[count] = coba
if count == (VERIF-1):
'''
plateAlloc = " "
numstring = ""
numbers = sum(c.isdigit() for c in nums)
words = sum(c.isalpha() for c in nums)
for c in nums:
numstring.append()
'''
global plat
plat = " "
plat = list(plat)
numstring = ""
numstring = list(numstring)
alphastring = ""
alphastring = list(alphastring)
numbers = sum(c.isdigit() for c in nums)
words = sum(c.isalpha() for c in nums)
for i in nums:
#nums = np.array(nums)
#nums = list(nums)
if i.isalpha():
#nums[i] = np.array(nums[i])
alphastring.append(i)
elif i.isdigit():
#nums[i] = np.array(nums[i])
numstring.append(i)
print(nums)
print(numstring)
print(alphastring)
#add numbers
a = 2
for b in numstring:
plat[a] = b
a+=1
#add front letter(s)
c = 0
sumfront = sum(c.isalpha() for c in nums[0:2])
if (sumfront == 1):
for d in nums[0:1]:
plat[c] = d
c+=1
elif (sumfront == 2):
for d in nums[0:2]:
plat[c] = d
c+=1
#add back letter(s)
e = -3
sumback = sum(e.isalpha() for e in nums[-3:])
if (sumback == 1):
for f in nums[-1:]:
plat[e] = f
e+=1
elif (sumback == 2):
for f in nums[-2:]:
plat[e] = f
e+=1
elif (sumback == 3):
for f in nums[-3:]:
plat[e] = f
e+=1
plat = ''.join(plat)
if (license[VERIF-1] == ""):
print("no characters were detected\n")
else:
#if number license same, not be saved
if (numlicense == license[VERIF-1]):
print("still = " + numlicense + "\n")
elif (len(nums) <= 9 and nums[0] >= 'A' and nums[0] <= 'Z' and numbers <= 4 and words <= 5):
numlicense = license[VERIF-1]
#print("A new license plate read from image = " + license[VERIF-1] + "\n")
print("A new license plate read from image = " + plat + "\n")
#cv2.imshow(license[VERIF-1], imgOriginalScene)
cv2.imshow(plat, imgOriginalScene)
insertdata= updatetime(numlicense)
if check(numlicense):
ts = time.time()
timestamp = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')
timestamp2 = datetime.datetime.fromtimestamp(ts).strftime('%H:%M:%S')
#Ganti Path sesuai dengan laptop masing2 heheh
namefile = "/var/www/html/MonitoringDashboard/hasil_parksystem/"+ license[VERIF-1] + timestamp + timestamp2 + ".png"
cv2.imwrite(namefile, imgOriginalScene)
#Hapus bagian ini untuk tidak menggunakan sensor dan mengirim mqtt
broker="192.168.8.120"
port=1883
client1= paho.Client("control1") #create client object
client1.connect(broker,port) #establish connection
ret= client1.publish("xiaomi/to/write",'{"cmd": "write", "model": "plug", "sid": "158d0002365abb", "data": {"status": "on"}}')
count = 0
#determine plate regions
global plateRegion
plateRegion = ""
plateDic = {'B':"Jakarta", 'D':"Bandung", 'L':"Surabaya", 'A':"Banten", 'E':"Cirebon", 'G':"Pekalongan", 'H':"Semarang"}
for i, j in plateDic.items():
if (plat[0] == i):
plateRegion = j
#and nums[0] >= 'A' and nums[0] <= 'Z' and nums[-1] >= 'A' and nums[-1] <= 'Z'
'''
global plateRegion
if (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'B'):
plateRegion = "Jakarta"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'D'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'E'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'D'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'D'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'D'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'D'):
plateRegion = "Bandung"
elif (len(nums) >= 5 and len(nums) <= 9 and nums[0] == 'L'):
plateRegion = "Surabaya"
else:
plateRegion = "Who knows?"
'''
#print(license)
# re-show scene image
#imgOriginalScene = cv2.blur(imgOriginalScene,(12,12))
cv2.putText(imgOriginalScene,"Press 's' to save frame to be 'save.png', for calibrating",(10,30),cv2.FONT_HERSHEY_SIMPLEX, 0.5,(255,255,255),1,bottomLeftOrigin = False)
#drawRedRectangleAroundPlate(imgOriginalScene, imgOriginalScene)
#cv2.rectangle(imgOriginalScene,((imgOriginalScene.shape[1]/2-230),(imgOriginalScene.shape[0]/2-80)),((imgOriginalScene.shape[1]/2+230),(imgOriginalScene.shape[0]/2+80)),SCALAR_GREEN,3)
#cv2.rectangle(imgOriginalScene,((int(imgOriginalScene.shape[1]/2-400)),(int(imgOriginalScene.shape[0]/2-200))),((int(imgOriginalScene.shape[1]/2+400)),(int(imgOriginalScene.shape[0]/2+200))),SCALAR_GREEN,3)
cv2.rectangle(imgOriginalScene,((int(imgOriginalScene.shape[1]/2-230)),(int(imgOriginalScene.shape[0]/2-80))),((int(imgOriginalScene.shape[1]/2+230)),(int(imgOriginalScene.shape[0]/2+80))),SCALAR_GREEN,3)
cv2.imshow("imgOriginalScene", imgOriginalScene)
#cv2.imshow("ori", frame)
key = cv2.waitKey(5) & 0xFF
if key == ord('s'):
knn = str(knn)
savefileimg = "calib_knn/img_"+ knn +".png"
savefileThr = "calib_knn/Thr_"+ knn +".png"
#cv2.saveimage("save.png", imgOriginalScene)
cv2.imwrite(savefileimg, frame)
cv2.imwrite(savefileThr, imgThresh)
print("image save !")
knn = int(knn)
knn = knn + 1
if key == 27: # if the 'q' key is pressed, stop the loop
break
camera.release() # cleanup the camera and close any open windows
# For image only
if (loop == False):
imgOriginalScene = imutils.resize(imgOriginalScene, width = 850)
cv2.imshow("original",imgOriginalScene)
imgGrayscale, imgThresh = pp.preprocess(imgOriginalScene)
cv2.imshow("threshold",imgThresh)
#imgOriginalScene = imutils.transform (imgOriginalScene)
imgOriginalScene,license = searching(imgOriginalScene,loop)
#imgOriginalScene = imutils.detransform(imgOriginalScene)
cv2.waitKey(0)
cv2.waitKey(0)
cv2.destroyAllWindows()
return
wt = pre.time_weight(Dc, N=n_code, L_n=P.shape[1])
for i in range(Dc.shape[1]):
X_i = Dc[:, i]
X_l, X_selected, X_h = pre.dct_segment_generate(X_i,
N=n_code,
L_n=P.shape[1])
Y = eb.watermark_embed(X_selected, P, wmbits, N=n_code, weight=wt[i])
Y_i = pre.dct_reconstruct(X_l, Y, X_h)
signal_wmd[:, i] = Y_i
embeded = tf.istt_my(signal_wmd, length=y.shape[0])
return embeded, ns
path = 'F:/audio_wm/audio/'
all_file = pre.filter_file(path, 'wav')
#all_file = ['./audio/batman-5min.wav']
n_dt = 8192
L_n = 32
n_code = 32 # 比特数除以4， 这里比特数是128
p0 = eb.seed_generate(L_n)
P = eb.pn_code_generate(16, p0)
np.save('F:/audio_wm/data/p0.npy', p0)
for filepath in all_file:
print("Embedding in " + filepath)
aFullFilename = os.path.split(filepath)[-1]
filename = aFullFilename.split('.')[0]
audio, sr = librosa.load(filepath, sr=44100, mono=False)
def detect_PlateChar(possible_plates):
intPlateCounter = 0
imgContours = None
contours = []
#if list is empty, then we exit the function
if len(possible_plates) == 0:
return possible_plates
#else:
for possiblePlate in possible_plates:
#for each plate we preprocess it
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate)
# increase size of plate image for easier processing
#remember that possiblePlate is a class, with imgThresh as a member,
#refer to PossiblePlate.py
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
#threshold again to eliminate gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
#find all possible chars
#this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
#find groups of matching chars for each plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
#if no matching chars was found, we skip the rest and jump to the beginning of the loop
if (len(listOfListsOfMatchingCharsInPlate) == 0):
possiblePlate.strChars = ""
continue
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
# Sort from left to right, and remove any overlapping charss
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX)
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i])
#within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
#loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
return possible_plates
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(possiblePlate.imgPlate) # preprocess to get grayscale and threshold images
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0), fx = 1.6, fy = 1.6)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(possiblePlate.imgThresh, 0.0, 255.0, cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5d", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True: # show steps ###################################################
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # clear the contours list
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
# end if # show steps #####################################################################
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(listOfPossibleCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("7", imgContours)
# end if # show steps #####################################################################
if (len(listOfListsOfMatchingCharsInPlate) == 0): # if no groups of matching chars were found in the plate
if Main.showSteps == True: # show steps ###############################################
print "chars found in plate number " + str(intPlateCounter) + " = (none), click on any image and press a key to continue . . ."
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
# end if # show steps #################################################################
possiblePlate.strChars = ""
continue # go back to top of for loop
# end if
for i in range(0, len(listOfListsOfMatchingCharsInPlate)): # within each list of matching chars
listOfListsOfMatchingCharsInPlate[i].sort(key = lambda matchingChar: matchingChar.intCenterX) # sort chars from left to right
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(listOfListsOfMatchingCharsInPlate[i]) # and remove inner overlapping chars
# end for
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("8", imgContours)
# end if # show steps #####################################################################
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[intIndexOfLongestListOfChars]
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
# end if # show steps #####################################################################
possiblePlate.strChars = recognizeCharsInPlate(possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
print "chars found in plate number " + str(intPlateCounter) + " = " + possiblePlate.strChars + ", click on any image and press a key to continue . . ."
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end if # show steps #####################################################################
# end of big for loop that takes up most of the function
if Main.showSteps == True:
print "\nchar detection complete, click on any image and press a key to continue . . .\n"
cv2.waitKey(0)
# end if
return listOfPossiblePlates
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = [] # this will be the return value
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
cv2.destroyAllWindows()
if Main.showSteps == True: # show steps #######################################################
cv2.imshow("0", imgOriginalScene)
# end if # show steps #########################################################################
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene) # preprocess to get grayscale and threshold images
if Main.showSteps == True: # show steps #######################################################
cv2.imshow("1a", imgGrayscaleScene)
cv2.imshow("1b", imgThreshScene)
# end if # show steps #########################################################################
# find all possible chars in the scene,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
if Main.showSteps == True: # show steps #######################################################
print "step 2 - len(listOfPossibleCharsInScene) = " + str(len(listOfPossibleCharsInScene)) # 131 with MCLRNF1 image
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("2b", imgContours)
# end if # show steps #########################################################################
# given a list of all possible chars, find groups of matching chars
# in the next steps each group of matching chars will attempt to be recognized as a plate
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(listOfPossibleCharsInScene)
if Main.showSteps == True: # show steps #######################################################
print "step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(len(listOfListsOfMatchingCharsInScene)) # 13 with MCLRNF1 image
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("3", imgContours)
# end if # show steps #########################################################################
for listOfMatchingChars in listOfListsOfMatchingCharsInScene: # for each group of matching chars
possiblePlate = extractPlate(imgOriginalScene, listOfMatchingChars) # attempt to extract plate
if possiblePlate.imgPlate is not None: # if plate was found
listOfPossiblePlates.append(possiblePlate) # add to list of possible plates
# end if
# end for
print "\n" + str(len(listOfPossiblePlates)) + " possible plates found" # 13 with MCLRNF1 image
if Main.showSteps == True: # show steps #######################################################
print "\n"
cv2.imshow("4a", imgContours)
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), Main.SCALAR_RED, 2)
cv2.imshow("4a", imgContours)
print "possible plate " + str(i) + ", click on any image and press a key to continue . . ."
cv2.imshow("4b", listOfPossiblePlates[i].imgPlate)
cv2.waitKey(0)
# end for
print "\nplate detection complete, click on any image and press a key to begin char recognition . . .\n"
cv2.waitKey(0)
# end if # show steps #########################################################################
return listOfPossiblePlates
import os
def extract(y, n_dt, P, wmbits, n_code):
"""
"""
Dc = tf.stt_my(y, n_dt=n_dt)
for i in range(Dc.shape[1]):
X_i = Dc[:, i]
X_l, X_s, X_h = pre.dct_segment_generate(X_i, N=n_code, L_n=P.shape[1])
wmt = et.extract(X_s, P)
wmbits.append(wmt)
return wmbits
path = 'F:/audio_wm/result/'
all_file = pre.filter_file(path, 'wav')
p0 = np.load('F:/audio_wm/data/p0.npy')
P = et.pn_reconstruct(16, p0)
for filepath in all_file:
aFilename = os.path.split(filepath)[-1]
filename = aFilename.split('.')[0]
audio, sr = librosa.load(filepath, sr=44100, mono=False)
n_dt = 8192
n_code = 32
#p0 = np.load('./data/' + filename +'.npy')
#P = et.pn_reconstruct(16, p0)
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0:
return listOfPossiblePlates
for possiblePlate in listOfPossiblePlates:
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate)
if Main.showSteps == True:
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True:
cv2.imshow("5d", possiblePlate.imgThresh)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True:
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("7", imgContours)
if (len(listOfListsOfMatchingCharsInPlate) == 0):
if Main.showSteps == True:
print(
"chars found in plate number " + str(intPlateCounter) +
" = (none), click on any image and press a key to continue . . ."
)
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
possiblePlate.strChars = ""
continue
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX)
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i])
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("8", imgContours)
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True:
print("chars found in plate number " + str(intPlateCounter) +
" = " + possiblePlate.strChars +
", click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
if Main.showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
return listOfPossiblePlates
def main():
# training parameter
k = 10 # fold
layer_thresh = 2
T = 50
result_path = 'results/spamDT_final.acc'
model_name = 'spam_' + str(k) + 'fold'
threshes_path = 'data/spambase.threshes'
# laod and preprocess training data
training_data = loader.load_dataset('data/spambase.data')
# load thresholds
threshes = loader.load_pickle_file(threshes_path)
# start training
training_errs = []
testing_errs = []
roc = []
auc = 0.0
k_folds = Preprocess.prepare_k_folds(training_data, k)
for i in range(1):
st = time.time()
tr_data, te_data = Preprocess.get_i_fold(k_folds, i)
tr_n, f_d = np.shape(tr_data[0])
te_n, = np.shape(te_data[1])
t = dt.DecisionTree()
t.build(tr_data[0], tr_data[1], threshes, layer_thresh)
# test the bagging model and compute testing acc
training_errs.append(t.test(tr_data[0], tr_data[1], util.acc))
testing_errs.append(t.test(te_data[0], te_data[1], util.acc))
print('Round {} finishes, time used: {}'.format(i, time.time() - st))
mean_training_err = np.mean(training_errs)
mean_testing_err = np.mean(testing_errs)
print(str(k) + '-fold validation done. Training errs are:')
print(training_errs)
print('Mean training err is:')
print(mean_training_err)
print('Testing errs are:')
print(testing_errs)
print('Mean testing err is:')
print(mean_testing_err)
result = {}
result['Fold'] = k
result['Trainingerrs'] = training_errs
result['MeanTrainingAcc'] = mean_training_err
result['Testingerrs'] = testing_errs
result['MeanTestingAcc'] = mean_testing_err
result['ROC'] = roc
result['AUC'] = auc
# log the training result to file
util.write_result_to_file(result_path, model_name, result, True)
def preprocess():
path = request.form['datasetfile']
print(path)
pr.process(path)
return render_template('main.html', result=0)
def main():
# training parameter
round_limit = 50
result_path = 'results/spamActive_random_final_1.acc'
model_name = 'spam_active'
threshes_path = 'data/spambase.threshes'
# laod and preprocess training data
training_data = loader.load_dataset('data/spambase.data')
# TODO convert labels from {0, 1} to {-1, 1}
util.replace_zero_label_with_neg_one(training_data)
# load thresholds
threshes = loader.load_pickle_file(threshes_path)
# start training
training_errs = []
testing_errs = []
# round_err_1st_boost = None
# tr_errs_1st_boost = None
# te_errs_1st_boost = None
# te_auc_1st_boost = None
roc = []
auc = 0.0
k_folds = Preprocess.prepare_k_folds(training_data, 5)
tr_data_pool, te_data = Preprocess.get_i_fold(k_folds, 1)
data_set = DataSet.DataSet(tr_data_pool)
data_rates = (5, 10, 15, 20, 30, 50)
for c in data_rates:
tr_data = data_set.random_pick(c, False)
tr_n, f_d = np.shape(tr_data[0])
te_n, = np.shape(te_data[1])
# TODO prepare distribution
d = util.init_distribution(len(tr_data[0]))
# TODO compute thresholds cheat sheet
thresh_cs = util.pre_compute_threshes(tr_data[0], tr_data[1], threshes)
boost = b.Boosting(d)
testing_predict = np.zeros((1, te_n)).tolist()[0]
training_predict = np.zeros((1, tr_n)).tolist()[0]
round_tr_err = []
round_te_err = []
round_model_err = []
round_te_auc = []
converged = False
tol = 1e-5
te_auc = 2.
round = 0
while round < round_limit: # and not converged:
round += 1
boost.add_model(ds.DecisionStump, tr_data[0], tr_data[1], threshes, thresh_cs)
boost.update_predict(tr_data[0], training_predict)
boost.update_predict(te_data[0], testing_predict)
c_model_err = boost.model[-1].w_err
round_model_err.append(c_model_err)
c_f_ind = boost.model[-1].f_ind
c_thresh = boost.model[-1].thresh
c_tr_err = util.get_err_from_predict(training_predict, tr_data[1])
c_te_err = util.get_err_from_predict(testing_predict, te_data[1])
# TODO calculate the AUC for testing results
# c_te_auc = util.get_auc_from_predict(testing_predict, te_data[1])
round_tr_err.append(c_tr_err)
round_te_err.append(c_te_err)
# round_te_auc.append(c_te_auc)
print('Data {}% Round: {} Feature: {} Threshold: {:.3f} Round_err: {:.12f} Train_err: {:.12f} Test_err {:.12f} AUC {}'.format(c, round, c_f_ind, c_thresh, c_model_err, c_tr_err, c_te_err, 0))
# converged = abs(c_te_auc - te_auc) / te_auc <= tol
# te_auc = c_te_auc
training_errs.append(round_tr_err[-1])
testing_errs.append(round_te_err[-1])
# break # for testing
mean_training_err = np.mean(training_errs)
mean_testing_err = np.mean(testing_errs)
print('Training errs are:')
print(training_errs)
print('Mean training err is:')
print(mean_training_err)
print('Testing errs are:')
print(testing_errs)
print('Mean testing err is:')
print(mean_testing_err)
result = {}
result['Trainingerrs'] = training_errs
result['MeanTrainingAcc'] = mean_training_err
result['Testingerrs'] = testing_errs
result['MeanTestingAcc'] = mean_testing_err
# result['ROC'] = str(roc)
result['AUC'] = auc
# log the training result to file
util.write_result_to_file(result_path, model_name, result, True)
def train(Para,num_epochs,path,validation_A_dir,output_dir,model_name):
n_frames = 128
dataset_A, dataset_B = pre.sample_data(dataset_A = Para["coded_sps_A_norm"],
dataset_B = Para["coded_sps_B_norm"],
frames_per_sample = n_frames,
s_type='parallel',
perm=[1,0])
##validation
n_samples = dataset_A.shape[0]
##build model
# model = Sequential()
#Inputs = Input(shape=(80,None))
Inputs = Input(shape=(None,80))
a= Conv1D(128,kernel_size=5, strides=2,activation='relu')(Inputs)
b= Conv1D(256,kernel_size=5, strides=2,activation='relu')(a)
c= Conv1D(512,kernel_size=5, strides=2,activation='relu')(b)
x = Bidirectional(LSTM(512, activation='relu', recurrent_activation='hard_sigmoid', return_sequences=True, return_state=False, stateful=False))(c)
y = Bidirectional(LSTM(512, activation='relu', recurrent_activation='hard_sigmoid', return_sequences=True, return_state=False, stateful=False))(x)
d= Conv1D(1024,kernel_size=5, strides=2,activation='relu')(y)
e= Conv1D(512,kernel_size=5, strides=2,activation='relu')(d)
f= Conv1D(80,kernel_size=5, strides=2,activation='linear')(e)
# Outputs = Dense(80, activation = 'linear')(d)
# model = Model(inputs=Inputs, outputs=Outputs)
model = Model(inputs=Inputs, outputs=f)
print(model.summary())
# model.add(Bidirectional(LSTM(128,input_dim=(None, return_sequences=True)))
# model.add(Bidirectional(LSTM(64, return_sequences=True)))
sgd=SGD(lr=0.01, momentum=0.0, decay=0.0, nesterov=False);
#sgd = SGD(lr=0.05, decay=1e-6, momentum=0.9, nesterov=True)
rmsprop=RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0);
adagrad=Adagrad(lr=0.01, epsilon=1e-08, decay=0.0);
adadelta=Adadelta(lr=1.0, rho=0.95, epsilon=1e-08, decay=0.0);
adam=Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0);
adamax=Adamax(lr=0.002, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0);
nadam=Nadam(lr=0.0005, beta_1=0.9, beta_2=0.999, epsilon=1e-08, schedule_decay=0.004);
OptimIz=nadam;
model.compile(
loss='mean_squared_error', #mean_squared_error
optimizer=OptimIz,
metrics=['accuracy'])
checkpointer = ModelCheckpoint(
filepath=path+model_name+".hdf5",
monitor="loss",
mode="min",
verbose=1,
save_best_only=True)
StartTime= time.time()
print('indata.shape:',dataset_A.shape)
print('outdata.shape:',dataset_B.shape)
data_A=dataset_A.reshape(dataset_A.shape[0],dataset_A.shape[2],dataset_A.shape[1])
data_B=dataset_B.reshape(dataset_B.shape[0],dataset_B.shape[2],dataset_B.shape[1])
Hist=model.fit(data_A, data_B, batch_size=1, epochs=num_epochs,verbose=1,callbacks=[checkpointer],validation_split=0.1,shuffle=False)
#do waveform reconstruction
sampling_rate = 16000
num_mcep = 80 #24
frame_period = 5.0
if validation_A_dir is not None:
validation_A_output_dir = os.path.join(output_dir, 'converted_A')
if not os.path.exists(validation_A_output_dir):
os.makedirs(validation_A_output_dir)
test_A_dir=str(validation_A_dir)+'\*\*.wav'
Eva_list_A=glob.glob(test_A_dir)
for filepath in tqdm(Eva_list_A,desc='Generating'):
filedir=os.path.basename(os.path.dirname(filepath))
outpath=os.path.join(validation_A_output_dir,filedir)
if not os.path.exists(outpath):
os.makedirs(outpath)
wav, _ = librosa.load(filepath, sr = sampling_rate, mono = True)
wav = pre.wav_padding(wav = wav, sr = sampling_rate, frame_period = frame_period, multiple = 4)
f0, timeaxis, sp, ap = pre.world_decompose(wav = wav, fs = sampling_rate, frame_period = frame_period)
f0_converted = pre.pitch_conversion(f0 = f0, mean_log_src = Para["log_f0s_mean_A"], std_log_src = Para["log_f0s_std_A"], mean_log_target = Para["log_f0s_mean_B"], std_log_target = Para["log_f0s_std_B"])
coded_sp = pre.world_encode_spectral_envelop(sp = sp, fs = sampling_rate, dim = num_mcep)
coded_sp_transposed = coded_sp.T
coded_sp_norm = (coded_sp_transposed - Para["coded_sps_A_mean"]) / Para["coded_sps_A_std"]
data_Tes=np.array([coded_sp_norm])
data_Tes_new=data_Tes.reshape(data_Tes.shape[0],data_Tes.shape[2],data_Tes.shape[1])
data_Ans= model.predict(data_Tes_new, batch_size=1, verbose=1, steps=None)
#data_Ans = model.test(inputs = data_Tes, direction = 'A2B')[0]
coded_sp_converted_norm = data_Ans
coded_sp_converted_norm_new=coded_sp_converted_norm.reshape(coded_sp_converted_norm.shape[2],coded_sp_converted_norm.shape[1])
coded_sp_converted = coded_sp_converted_norm_new * Para["coded_sps_B_std"] + Para["coded_sps_B_mean"]
coded_sp_converted = coded_sp_converted.T
coded_sp_converted = np.ascontiguousarray(coded_sp_converted)
decoded_sp_converted = pre.world_decode_spectral_envelop(coded_sp = coded_sp_converted, fs = sampling_rate)
wav_transformed = pre.world_speech_synthesis(f0 = f0_converted, decoded_sp = decoded_sp_converted, ap = ap, fs = sampling_rate, frame_period = frame_period)
librosa.output.write_wav(os.path.join(outpath,os.path.basename(filepath)),wav_transformed, sampling_rate)
print(model.summary())
with open(path+model_name+".json", "w") as f:
f.write(model.to_json())
EndTime = time.time()
print('time is {} sec'.format(EndTime-StartTime))
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate
) # preprocess to get grayscale and threshold images
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5d", possiblePlate.imgThresh)
# end if # show steps #####################################################################
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True: # show steps ###################################################
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # clear the contours list
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
# end if # show steps #####################################################################
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("7", imgContours)
# end if # show steps #####################################################################
if (len(listOfListsOfMatchingCharsInPlate) == 0
): # if no groups of matching chars were found in the plate
if Main.showSteps == True: # show steps ###############################################
print(
"chars found in plate number " + str(intPlateCounter) +
" = (none), click on any image and press a key to continue . . ."
)
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
# end if # show steps #################################################################
possiblePlate.strChars = ""
continue # go back to top of for loop
# end if
for i in range(0, len(listOfListsOfMatchingCharsInPlate)
): # within each list of matching chars
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX
) # sort chars from left to right
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) # and remove inner overlapping chars
# end for
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("8", imgContours)
# end if # show steps #####################################################################
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
if Main.showSteps == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
# end if # show steps #####################################################################
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True: # show steps ###################################################
print("chars found in plate number " + str(intPlateCounter) +
" = " + possiblePlate.strChars +
", click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end if # show steps #####################################################################
# end of big for loop that takes up most of the function
if Main.showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
# end if
return listOfPossiblePlates
def before_train(train_A_dir, train_B_dir, model_dir, output_dir, tensorboard_log_dir):
sampling_rate = 16000
num_mcep = 80 #24
frame_period = 5.0
print('Preprocessing Data...')
start_time = time.time()
# list_A= pre.load_data_list(train_A_dir)
# list_B= pre.load_data_list(train_B_dir)
# print(list_A[1])
train_A_dir=str(train_A_dir)+'\*\*.wav' #當層 \*.wav 內有一層\*\*.wav
train_B_dir=str(train_B_dir)+'\*\*.wav'
list_A=glob.glob(train_A_dir)
list_B=glob.glob(train_B_dir)
tta=natsorted(list_A)
ttb=natsorted(list_B)
wavs_A = pre.load_wavs(wav_list = tta, sr = sampling_rate)
wavs_B = pre.load_wavs(wav_list = ttb, sr = sampling_rate)
f0s_A, timeaxes_A, sps_A, aps_A, coded_sps_A = pre.world_encode_data(wavs = wavs_A, fs = sampling_rate, frame_period = frame_period, coded_dim = num_mcep)
log_f0s_mean_A, log_f0s_std_A = pre.logf0_statistics(f0s_A)
print('Log Pitch A')
print('Mean: %f, Std: %f' %(log_f0s_mean_A, log_f0s_std_A))
f0s_B, timeaxes_B, sps_B, aps_B, coded_sps_B = pre.world_encode_data(wavs = wavs_B, fs = sampling_rate, frame_period = frame_period, coded_dim = num_mcep)
log_f0s_mean_B, log_f0s_std_B = pre.logf0_statistics(f0s_B)
print('Log Pitch B')
print('Mean: %f, Std: %f' %(log_f0s_mean_B, log_f0s_std_B))
coded_sps_A_transposed = pre.transpose_in_list(lst = coded_sps_A)
coded_sps_B_transposed = pre.transpose_in_list(lst = coded_sps_B)
coded_sps_A_norm, coded_sps_A_mean, coded_sps_A_std = pre.coded_sps_normalization_fit_transoform(coded_sps = coded_sps_A_transposed)
print("Input data fixed.")
coded_sps_B_norm, coded_sps_B_mean, coded_sps_B_std = pre.coded_sps_normalization_fit_transoform(coded_sps = coded_sps_B_transposed)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
np.savez(os.path.join(model_dir, 'logf0s_normalization.npz'), mean_A = log_f0s_mean_A, std_A = log_f0s_std_A, mean_B = log_f0s_mean_B, std_B = log_f0s_std_B)
np.savez(os.path.join(model_dir, 'mcep_normalization.npz'), mean_A = coded_sps_A_mean, std_A = coded_sps_A_std, mean_B = coded_sps_B_mean, std_B = coded_sps_B_std)
end_time = time.time()
time_elapsed = end_time - start_time
Para_name=['sampling_rate', 'num_mcep', 'frame_period',
'coded_sps_A_norm', 'coded_sps_B_norm', 'coded_sps_A', 'coded_sps_B',
'coded_sps_A_mean', 'coded_sps_A_std', 'coded_sps_B_mean', 'coded_sps_B_std',
'log_f0s_mean_A', 'log_f0s_std_A', 'log_f0s_mean_B', 'log_f0s_std_B']
# Para_num=len(Para_name)
Local_Var=locals()
Para_data=[Local_Var[para_index] for para_index in Para_name]
Para=dict(zip(Para_name, Para_data))
print('Preprocessing Done.')
print('Time Elapsed for Data Preprocessing: %02d:%02d:%02d' % (time_elapsed // 3600, (time_elapsed % 3600 // 60), (time_elapsed % 60 // 1)))
return Para
# training parameter
k = 50 # fold
result_path = 'results/spamRidge_4.acc'
model_name = 'spam_' + str(k) + 'fold_' + 'shift_scale'
normalization = Preprocess.zero_mean_unit_var
# normalization = Preprocess.shift_and_scale
# cols_not_norm = [i for i in range(54)]
# cols_not_norm = [i for i in range(48, 54)]
cols_not_norm = ()
lamda = 2
# laod and preprocess training data
training_data = loader.load_dataset('data/spambase.data')
Preprocess.normalize_features_all(normalization, training_data[0], not_norm=cols_not_norm)
# start training
training_accs = []
training_cms = []
testing_accs = []
testing_cms = []
roc = []
auc = 0.0
for i in range(k):
(tr_data, te_data) = Preprocess.prepare_k_fold_data(training_data, k, i + 1)
model = rm.Ridge()
model.build(tr_data[0], tr_data[1], lamda)
def detectCharsInPlates(listOfPossiblePlates, filePath):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
return listOfPossiblePlates # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
for index, possiblePlate in enumerate(
listOfPossiblePlates
): # for each possible plate, this is a big for loop that takes up most of the function
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate
) # preprocess to get grayscale and threshold images
adaptivePlate = cv2.adaptiveThreshold(possiblePlate.imgGrayscale, 255,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY, 11, 2)
blurPlate = cv2.GaussianBlur(adaptivePlate, (5, 5), 0)
ret, processedPlate = cv2.threshold(
blurPlate, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
if Main.showSteps == True: # show steps ###################################################
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c.adaptive", adaptivePlate)
cv2.imshow("5d.blur", blurPlate)
cv2.imshow("5e.otsu", processedPlate)
# end if # show steps #####################################################################
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(adaptivePlate)
listOfPossibleCharsInPlate.sort(key=lambda Char: Char.intCenterX)
if Main.showSteps == True: # show steps ###################################################
showContours(possiblePlate, listOfPossibleCharsInPlate)
listOfListsOfChars = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate, minChars=3, maxAngle=10)
if len(listOfListsOfChars) == 0:
continue
# find chars that have same heights
listOfListsOfChars1 = [
getEqualHeightList(x) for x in listOfListsOfChars
]
listOfListsOfChars2 = getEqualHeightList(listOfListsOfChars1, mode=1)
# remove Distance Char
listOfListsOfChars3 = [
removeDistanceChar(x) for x in listOfListsOfChars2
]
# flatten list
listOfCharsInPlate = [
char for listChars in listOfListsOfChars3 for char in listChars
]
# remove inner Chars
listOfCharsInPlate = removeInnerChars(listOfCharsInPlate)
# number of plate elements must be > 6
if len(listOfCharsInPlate) >= 6:
possiblePlate.isPlate = True
if Main.showSteps == True: # show steps #######################################################
showListOfLists(possiblePlate, listOfCharsInPlate)
# end of big for loop that takes up most of the function
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgGrayscale, listOfCharsInPlate, index,
filePath)
print("predict: ", possiblePlate.strChars)
else:
continue
listOfPlates = [plate for plate in listOfPossiblePlates if plate.isPlate]
if Main.showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
# end if
return listOfPlates
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = []
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
if Main.showSteps == True:
cv2.imshow("0", imgOriginalScene)
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene)
if Main.showSteps == True:
cv2.imshow("1a", imgGrayscaleScene)
cv2.imshow("1b", imgThreshScene)
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
if Main.showSteps == True:
print "step 2 - len(listOfPossibleCharsInScene) = " + str(len(listOfPossibleCharsInScene))
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("2b", imgContours)
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(listOfPossibleCharsInScene)
if Main.showSteps == True:
print "step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(len(listOfListsOfMatchingCharsInScene))
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("3", imgContours)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
possiblePlate = extractPlate(imgOriginalScene, listOfMatchingChars)
if possiblePlate.imgPlate is not None:
listOfPossiblePlates.append(possiblePlate)
if Main.showSteps == True:
print "\n"
cv2.imshow("4a", imgContours)
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), Main.SCALAR_RED, 2)
cv2.imshow("4a", imgContours)
print "possible plate " + str(i) + ", click on any image and press a key to continue . . ."
cv2.imshow("4b", listOfPossiblePlates[i].imgPlate)
cv2.waitKey(0)
print "\nplate detection complete, click on any image and press a key to begin char recognition . . .\n"
cv2.waitKey(0)
return listOfPossiblePlates
from Custom_keras_Encoder_Decoder import Encoder_Decoder, load_Encoder_Decoder
import Encoder_Decoder_for_dummies
import Preprocess
from read_xlsx import get_trivial
from prep_data import preprocess_data
MAX_SIZE = 1000
#qanda = QandA_data.QandA_data()
#questions, answers = qanda.get_data()
#print(len(questions), len(answers))
questions, answers = preprocess_data(get_trivial(["_", "\'", "&", "\"", ":", "(", ")"], [], MAX_SIZE)[1], get_trivial(["_", "\'", "&", "\"", ":", "(", ")"], [], MAX_SIZE)[2])
#print (questions[4250],"\n", answers[4250])
#questions, answers = preprocess_data(questions, answers)
preprocess = Preprocess.Preprocess(questions[:10000], answers[:10000])
def str_to_tokens(sentence : str):
words = sentence.lower().split()
tokens_list = list()
for word in words:
if word in preprocess.que_word_dict:
tokens_list.append(preprocess.que_word_dict[word])
return preprocessing.sequence.pad_sequences( [tokens_list] , maxlen=preprocess.max_input_length , padding='post')
ed = Encoder_Decoder_for_dummies.ED_dummies(preprocess)
ed.train(epochs=500, batch_size=1000)
ed.save("saves")
#ed.load("saves")
def __get_outer_data(self, outer_data_dir, data_szie):
return Preprocess.process_all_articles(outer_data_dir, data_szie)
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = [] # this will be the return value
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
cv2.imshow("Original Image", imgOriginalScene)
cv2.waitKey(0)
# if Main.showSteps == True:
# cv2.imshow("0", imgOriginalScene)
# # cv2.waitKey(0)
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene) # preprocess to get grayscale and threshold images
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
# cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
# cv2.imshow("2b", imgContours)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
listOfListsOfMatchingCharsInScene = CharacterDetection.findListOfListsOfMatchingChars(listOfPossibleCharsInScene)
if Main.showSteps == True:
print("step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(len(listOfListsOfMatchingCharsInScene)))
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
imgContours2 = np.zeros((height, width, 3), np.uint8)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
cv2.drawContours(imgContours2, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
# # # end for
# cv2.imshow("3", imgContours)
# cv2.waitKey(0)
# # cv2.destroyAllWindows()
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
possiblePlate = extractPlate(imgOriginalScene, listOfMatchingChars)
if possiblePlate.imgPlate is not None:
listOfPossiblePlates.append(possiblePlate)
cv2.imshow('The plates',possiblePlate.imgPlate)
cv2.waitKey(0)
# cv2.destroyAllWindows()
if Main.showSteps == True:
print("\n" + str(len(listOfPossiblePlates)) + " possible plates found")
cv2.destroyAllWindows()
return listOfPossiblePlates
def retieveEventsAtDate(date, url):
# root website
html = urllib.urlopen(url).read()
soup = BeautifulSoup(html, "html.parser")
briefs = soup.select("div.item.event_item.vevent")
# retieve all the events detail pages url.
print 'collecting', len(briefs), 'events from', url
toVisit = []
for brief in briefs:
toVisit.append(brief.find('a').get('href'))
result = []
# get information from each events detail pages.
for item in toVisit:
tmp = {}
url = item
html = urllib.urlopen(url).read()
soup = BeautifulSoup(html, "html.parser")
# the event information is reside here.
event = soup.select("div.box_header.vevent")[0]
# extract title from h1 tag's text
title = event.h1.span.get_text().strip()
# extract time from h2 tag's abbr field
time = ""
for abbr in event.h2.findAll('abbr'):
time += abbr.get_text().strip() + " "
print time
time = timePreprocess(date, time)
print time
if time is None:
print("can't resolve time, discard this event")
continue
display_location = event.h3.a
real_location = event.h3.small
if display_location is not None and real_location is not None and len(display_location) > 0 and len(real_location) > 0:
display_location = display_location.get_text().strip()
real_location = real_location.get_text().strip()
else:
# discard bad formated events
print("can't resolve location, discard this event")
continue
if "Cornell University" in real_location:
real_location = real_location.replace("Cornell University", "")
print "real_location becomes: " + real_location
if "Ithaca, NY 14853, USA" not in real_location:
real_location = display_location + ", Ithaca, NY 14853, USA"
latlng = Preprocess.decodeAddressToCoordinates(real_location)
print "latlng: %s" % latlng
lat = latlng['lat']
lng = latlng['lng']
# street = event.h3.small
# if street is not None:
# street = street.get_text().strip()
# else:
# street = "Cornell University"
description = event.select("div.description")
if len(description) > 0:
description = description[0].get_text()
else:
# discard bad formated events
print("can't resolve description, discard this event")
continue
image = soup.select("div.box_image")
if len(image) > 0:
image = image[0].a.img['src']
else:
# discard bad formated events
print("can't resolve image, discard this event")
continue
tmp['title'] = title
tmp['time'] = time
tmp['location'] = display_location
tmp['description'] = description
tmp['image'] = image
tmp['lat'] = lat
tmp['lng'] = lng
tmp['secondaryTag'] = ['Cornell Sponsored']
for free_food_keyword in free_food_keywords:
if free_food_keyword in tmp['description'].lower():
tmp['secondaryTag'].append('Free Food')
break
print 'retrieved:', tmp['title']
result.append(tmp)
# return all events dicitionary in an array.
return result
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates
) == 0: # ukoliko je lista mogucih tablica prazna
return listOfPossiblePlates # return
# end if
for possiblePlate in listOfPossiblePlates: # za svaku mogucu tablicu
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate
) # predprocesi da se dobiju grayscale i threshold slike
if Main.showSteps == True: # prikazi korake
cv2.imshow("5a", possiblePlate.imgPlate)
cv2.imshow("5b", possiblePlate.imgGrayscale)
cv2.imshow("5c", possiblePlate.imgThresh)
# end if
# povecati velicinu slike da bi se bolje vidjelo i lakse detektovali charovi
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6)
# threshold ponovo da bi se otklonile sive povrsine
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
if Main.showSteps == True:
cv2.imshow("5d", possiblePlate.imgThresh)
# pronalazi sve moguce charove na slici
# ova funkcija prvo pronalazi sve konture, i onda samo ukljucuje konture koje bi mogle biti charovi
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True:
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # ocistiti listu kontura
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("6", imgContours)
# end if #
# nakon sto imamo listu svih mogucih charova, pronaci grupu slicnih charova na slici
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("7", imgContours)
# end if
if (len(listOfListsOfMatchingCharsInPlate) == 0
): # ukoliko nisu pronadene grupe slicnih charova
if Main.showSteps == True:
print(
"chars found in plate number " + str(intPlateCounter) +
" = (none), click on any image and press a key to continue . . ."
)
intPlateCounter = intPlateCounter + 1
cv2.destroyWindow("8")
cv2.destroyWindow("9")
cv2.destroyWindow("10")
cv2.waitKey(0)
# end if
possiblePlate.strChars = ""
continue # vracanje na pocetak for petlje
# end if
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX
) # sortirati charova s lijeva na desno
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) # i ukloniti charove koji se preklapaju
# end for
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("8", imgContours)
# end if
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# proci petljom kroz sve vektore poklapajucih charova i uzeti indeks onog koji se najvise poklapa
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
if Main.showSteps == True:
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("9", imgContours)
# end if
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
if Main.showSteps == True:
print("chars found in plate number " + str(intPlateCounter) +
" = " + possiblePlate.strChars +
", click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end if
if Main.showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
# end if
return listOfPossiblePlates
threshes_path = "data/vote.threshes"
data_path = "data/vote_parsed.data"
# laod and preprocess training data
training_data = loader.load_pickle_file(data_path)
print("total data points: {}".format(len(training_data[0])))
# load thresholds
threshes = loader.load_pickle_file(threshes_path)
# start training
training_errs_by_percent = {}
testing_errs_by_percent = {}
auc_by_percent = {}
roc = []
auc = 0.0
k_folds = Preprocess.prepare_k_folds(training_data, k)
percent_list = (5, 10, 15, 20, 30, 50, 80)
for i in range(k):
tr_data_all, te_data = Preprocess.get_i_fold(k_folds, i)
for c in percent_list:
if c not in training_errs_by_percent.keys():
training_errs_by_percent[c] = []
testing_errs_by_percent[c] = []
auc_by_percent[c] = []
tr_data = Preprocess.get_c_percent(c, tr_data_all)
tr_n, f_d = np.shape(tr_data[0])
te_n, = np.shape(te_data[1])
# TODO prepare distribution
def detectPlatesInScene(img):
listOfPossiblePlates = []
height, width, numChannels = img.shape
imgGray = np.zeros((height, width, 1), np.uint8)
imgThresh = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
cv2.destroyAllWindows()
if Main.showSteps == True:
cv2.imshow("0", img)
imgGray, imgThresh = Preprocess.preprocess(img) # preprocess to get grayscale and threshold images
if Main.showSteps == True:
cv2.imshow("Gray SCale Image", imgGray)
cv2.imshow("Image threshold", imgThresh)
# find all possible chars in the scene,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThresh)
if Main.showSteps == True: #
print("step 2 - len(listOfPossibleCharsInScene) = " + str(len(listOfPossibleCharsInScene)))
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("2b", imgContours)
# given a list of all possible chars, find groups of matching chars
# in the next steps each group of matching chars will attempt to be recognized as a plate
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(listOfPossibleCharsInScene)
if Main.showSteps == True:
print("step 3 - listOfListsOfMatchingCharsInScene.Count = " + str( len(listOfListsOfMatchingCharsInScene)))
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
RandomBlue = random.randint(0, 255)
RandomGreen = random.randint(0, 255)
RandomRed = random.randint(0, 255)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1, (RandomBlue, RandomGreen, RandomRed))
cv2.imshow("Image Contours", imgContours)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
possiblePlate = extractPlate(img, listOfMatchingChars)
if possiblePlate.imgPlate is not None:
listOfPossiblePlates.append(possiblePlate)
print("\n" + str(len(listOfPossiblePlates)) + " possible plates found")
if Main.showSteps == True:
print("\n")
cv2.imshow("Image Contours", imgContours)
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), Main.SCALAR_RED, 2)
cv2.imshow("Image Contpurs", imgContours)
print("possible plate " + str(i) + ", click on any image and press a key to continue . . .")
cv2.imshow("List of Possible plates", listOfPossiblePlates[i].imgPlate)
cv2.waitKey(0)
print("\nplate detection complete, click on any image and press a key to begin char recognition . . .\n")
cv2.waitKey(0)
return listOfPossiblePlates
# params
lamda = 0.5
tol = 0.92
normalize_method = prep.zero_mean_unit_var
term_method = util.acc_higher_than_ridge
# laod and preprocess training data
tr_data = loader.load_pickle_file(train_data_path)
te_data = loader.load_pickle_file(test_data_path)
print("{:.2f} Data loaded!".format(time.time() - st))
tr_data[0] = tr_data[0].tolist()
te_data[0] = te_data[0].tolist()
# normalize features
prep.normalize_features_all(normalize_method, tr_data[0], te_data[0])
print("{:.2f} Features normalized!".format(time.time() - st))
saved_model = loader.load_pickle_file(model_path) # load the model
theta = saved_model.theta
is_batch = True
penalty = "l2" # l2 for RIDGE
alpha = 0.05
model = gd.LogisticRegressionGD(theta, penalty, alpha)
# model.build(tr_data[0], tr_data[1], lamda, term_method, tol, is_batch)
model.build(tr_data[0], tr_data[1], lamda, term_method, tol, is_batch, te_data[0], te_data[1])
training_acc = model.test(tr_data[0], tr_data[1], util.acc)
testing_acc = model.test(te_data[0], te_data[1], util.acc)
print("{} Final results. Train acc: {}, Test acc: {}".format(time.time() - st, training_acc, testing_acc))
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = [] # this will be the return value
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
cv2.destroyAllWindows()
if main.showSteps == True: # show steps #######################################################
cv2.imshow("0", imgOriginalScene)
# end if # show steps #########################################################################
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene) # preprocess to get grayscale and threshold images
if main.showSteps == True: # show steps #######################################################
cv2.imshow("1a", imgGrayscaleScene)
cv2.imshow("1b", imgThreshScene)
# end if # show steps #########################################################################
# find all possible chars in the scene,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
if main.showSteps == True: # show steps #######################################################
print "step 2 - len(listOfPossibleCharsInScene) = " + str(len(listOfPossibleCharsInScene)) # 131 with MCLRNF1 image
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, main.SCALAR_WHITE)
cv2.imshow("2b", imgContours)
# end if # show steps #########################################################################
# given a list of all possible chars, find groups of matching chars
# in the next steps each group of matching chars will attempt to be recognized as a plate
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(listOfPossibleCharsInScene)
if main.showSteps == True: # show steps #######################################################
print "step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(len(listOfListsOfMatchingCharsInScene)) # 13 with MCLRNF1 image
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (intRandomBlue, intRandomGreen, intRandomRed))
# end for
cv2.imshow("3", imgContours)
# end if # show steps #########################################################################
for listOfMatchingChars in listOfListsOfMatchingCharsInScene: # for each group of matching chars
possiblePlate = extractPlate(imgOriginalScene, listOfMatchingChars) # attempt to extract plate
if possiblePlate.imgPlate is not None: # if plate was found
listOfPossiblePlates.append(possiblePlate) # add to list of possible plates
# end if
# end for
print "\n" + str(len(listOfPossiblePlates)) + " possible plates found" # 13 with MCLRNF1 image
if main.showSteps == True: # show steps #######################################################
print "\n"
cv2.imshow("4a", imgContours)
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]), tuple(p2fRectPoints[1]), main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]), tuple(p2fRectPoints[2]), main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]), tuple(p2fRectPoints[3]), main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]), tuple(p2fRectPoints[0]), main.SCALAR_RED, 2)
cv2.imshow("4a", imgContours)
print "possible plate " + str(i) + ", click on any image and press a key to continue . . ."
cv2.imshow("4b", listOfPossiblePlates[i].imgPlate)
cv2.waitKey(0)
# end for
print "\nplate detection complete, click on any image and press a key to begin char recognition . . .\n"
cv2.waitKey(0)
# end if # show steps #########################################################################
return listOfPossiblePlates
from perceptron_dual import PerceptronDual
import csv
import Utilities as util
import numpy as np
import Consts as c
import Preprocess
data_file = 'data/twoSpirals.txt'
# load and preprocess data
features = []
labels = []
with open(data_file) as f:
for line in csv.reader(f, delimiter='\t'):
cur_l = int(float(line[-1]))
sign = 1
cur_f = [sign * float(l) for l in line[:-1]]
features.append(cur_f)
labels.append([cur_l])
features = np.array(features)
Preprocess.normalize_features_all(Preprocess.zero_mean_unit_var, features)
# Preprocess.normalize_features_all(Preprocess.shift_and_scale, features)
labels = np.array(labels).transpose()[0]
# create perceptron
# kernel = c.LINEAR
kernel = c.GAUSSIAN
model = PerceptronDual(kernel_fun=kernel)
model.fit(features, labels)
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = []
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
if Main.showSteps == True:
cv2.imshow("0", imgOriginalScene)
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(imgOriginalScene)
if Main.showSteps == True:
cv2.imshow("1a", imgGrayscaleScene)
cv2.imshow("1b", imgThreshScene)
listOfPossibleCharsInScene = findPossibleCharsInScene(imgThreshScene)
if Main.showSteps == True:
print "step 2 - len(listOfPossibleCharsInScene) = " + str(
len(listOfPossibleCharsInScene))
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
cv2.imshow("2b", imgContours)
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(
listOfPossibleCharsInScene)
if Main.showSteps == True:
print "step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(
len(listOfListsOfMatchingCharsInScene))
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
cv2.imshow("3", imgContours)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
possiblePlate = extractPlate(imgOriginalScene, listOfMatchingChars)
if possiblePlate.imgPlate is not None:
listOfPossiblePlates.append(possiblePlate)
if Main.showSteps == True:
print "\n"
cv2.imshow("4a", imgContours)
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(
listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]),
tuple(p2fRectPoints[1]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]),
tuple(p2fRectPoints[2]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]),
tuple(p2fRectPoints[3]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]),
tuple(p2fRectPoints[0]), Main.SCALAR_RED, 2)
cv2.imshow("4a", imgContours)
print "possible plate " + str(
i) + ", click on any image and press a key to continue . . ."
cv2.imshow("4b", listOfPossiblePlates[i].imgPlate)
cv2.waitKey(0)
print "\nplate detection complete, click on any image and press a key to begin char recognition . . .\n"
cv2.waitKey(0)
return listOfPossiblePlates
def detectCharsInPlates(listOfPossiblePlates):
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0:
return listOfPossiblePlates
listOfPossiblePlates_refined = []
for possiblePlate in listOfPossiblePlates:
# grayscale and threshold images by preprocess
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess1(
possiblePlate.imgPlate)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if Main.showSteps == True:
height, width, numChannels = possiblePlate.imgPlate.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# find groups of matching chars within the plate in list
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if (len(listOfListsOfMatchingCharsInPlate) == 0):
possiblePlate.strChars = ""
continue
# within each list of matching chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
# sort chars from left to right
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX)
# and remove inner overlapping chars
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i])
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# suppose that the longest list of matching chars within the plate is the actual list of chars
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
listOfPossiblePlates_refined.append(possiblePlate)
return listOfPossiblePlates_refined
Y_test = []
train_dataset = pd.read_csv("dataset.csv", encoding='utf-8',sep='|')
test_dataset = pd.read_csv("testset.csv", encoding='utf-8',sep='|')
train_dataset = train_dataset.dropna()
test_dataset = test_dataset.dropna()
k=0
X_data_ids = []
X_data_attn = []
X_data_seg = []
X_test_ids = []
X_test_attn = []
X_test_seg = []
import Preprocess as pr
p = pr.Preprocess(tokenizer,maxlen)
for sentence in train_dataset['Sentence']:
X_dataset = p.work(sentence)
X_data_ids.append(X_dataset[0])
X_data_attn.append(X_dataset[1])
X_data_seg.append(X_dataset[2])
for sentence in test_dataset['Sentence']:
X_testset = p.work(sentence)
X_test_ids.append(X_testset[0])
X_test_attn.append(X_testset[1])
X_test_seg.append(X_testset[2])
Y_data = p.labeling(train_dataset, Y_data)
Y_test = p.labeling(test_dataset, Y_test)
__author__ = 'Mounica' from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from sklearn.linear_model import SGDClassifier import pandas as pd import Preprocess from sklearn.externals import joblib import sys PATH = sys.argv[1] INPUTPATH = sys.argv[2] FILE_NAME = sys.argv[3] FILE_PATH = INPUTPATH + "/" + FILE_NAME dataset = Preprocess.process(FILE_PATH) userId = dataset['userId'] status = dataset['status'] gender = dataset['gender'] age = dataset['age'] datasetList = zip(userId, status, gender, age) ds = pd.DataFrame(data=datasetList, columns=['userId', 'status', 'gender', 'age']) ngram_vectorizer = CountVectorizer(ngram_range=(1,3), min_df=1) tfidf_transformer = TfidfTransformer() X_train = ds['status'] y_train_gender = ds['gender'] y_train_age = ds['age'] #Traindata vectorization
def detectPlatesInScene(imgOriginalScene):
listOfPossiblePlates = [] # this will be the return value
height, width, numChannels = imgOriginalScene.shape
imgGrayscaleScene = np.zeros((height, width, 1), np.uint8)
imgThreshScene = np.zeros((height, width, 1), np.uint8)
imgContours = np.zeros((height, width, 3), np.uint8)
if Main.showSteps == True: # show steps #######################################################
#cv2.imshow("0", imgOriginalScene)
Image.fromarray(imgOriginalScene).show()
input('Press any key to continue...')
imgGrayscaleScene, imgThreshScene = Preprocess.preprocess(
imgOriginalScene) # preprocess to get grayscale and threshold images
# find all possible chars in the scene,
# this function first finds all contours, then only includes contours that could be chars (without comparison to other chars yet)
listOfPossibleCharsInScene = findPossibleCharsInScene(
imgThreshScene
) # Here we get a list of all the contours in the image that may be characters.
if Main.showSteps == True: # show steps #######################################################
#print("step 2 - len(listOfPossibleCharsInScene) = " + str(len(listOfPossibleCharsInScene)))
imgContours = np.zeros((height, width, 3), np.uint8)
contours = []
for possibleChar in listOfPossibleCharsInScene:
contours.append(possibleChar.contour)
cv2.drawContours(imgContours, contours, -1, Main.SCALAR_WHITE)
Image.fromarray(imgOriginalScene).show()
input('Press any key to continue...')
# This is for the boxing of all the contours
"""
for possibleChar in listOfPossibleCharsInScene:
cv2.rectangle(imgContours,(possibleChar.intBoundingRectX,possibleChar.intBoundingRectY),(possibleChar.intBoundingRectX+possibleChar.intBoundingRectWidth,possibleChar.intBoundingRectY+possibleChar.intBoundingRectHeight),(0.0, 255.0, 255.0),1)
cv2.imshow('PossiblePlate',imgContours)
cv2.waitKey(0)
"""
# given a list of all possible chars, find groups of matching chars
# in the next steps each group of matching chars will attempt to be recognized as a plate
listOfListsOfMatchingCharsInScene = DetectChars.findListOfListsOfMatchingChars(
listOfPossibleCharsInScene)
if Main.showSteps == True: # show steps #######################################################
print("step 3 - listOfListsOfMatchingCharsInScene.Count = " + str(
len(listOfListsOfMatchingCharsInScene))) # 13 with MCLRNF1 image
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInScene:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
#imgContours2 = np.zeros((height, width, 3), np.uint8)
contours = []
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
#cv2.drawContours(imgContours, contours, -1, (255, 255, 255))
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
#imgContours = Image.fromarray(imgContours,'RGB').show()
# end if # show steps #########################################################################
for listOfMatchingChars in listOfListsOfMatchingCharsInScene: # for each group of matching chars
possiblePlate = extractPlate(
imgOriginalScene, listOfMatchingChars) # attempt to extract plate
if possiblePlate.imgPlate is not None: # if plate was found
listOfPossiblePlates.append(
possiblePlate) # add to list of possible plates
if Main.showSteps == True:
print("\n" + str(len(listOfPossiblePlates)) + " possible plates found")
if Main.showSteps == True: # show steps #######################################################
print("\n")
Image.fromarray(imgContours, 'RGB').show()
input('Press any key to continue...')
for i in range(0, len(listOfPossiblePlates)):
p2fRectPoints = cv2.boxPoints(
listOfPossiblePlates[i].rrLocationOfPlateInScene)
cv2.line(imgContours, tuple(p2fRectPoints[0]),
tuple(p2fRectPoints[1]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[1]),
tuple(p2fRectPoints[2]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[2]),
tuple(p2fRectPoints[3]), Main.SCALAR_RED, 2)
cv2.line(imgContours, tuple(p2fRectPoints[3]),
tuple(p2fRectPoints[0]), Main.SCALAR_RED, 2)
#cv2.imshow("4a", imgContours)
print("possible plate " + str(i) +
", click on any image and press a key to continue . . .")
#Image.fromarray(listOfPossiblePlates[i].imgPlate,'RGB').show()
# end for
print(
"\nplate detection complete, press a key to begin char recognition . . .\n"
)
input()
# end if # show steps #########################################################################
return listOfPossiblePlates
def templateMatching(im,elements, isPhoto):
templates = ['images/templates/resistorT4.PNG', 'images/templates/resistorT2.PNG', 'images/templates/resistorT2_0degree.PNG', 'images/templates/resistorT2_45degree.PNG', 'images/templates/resistorT2_135degree.PNG','images/templates/resistorT4_0degree.PNG'];
unmatched_resistors = [];
for elem in elements:
if elem[4] == 'o':
unmatched_resistors += [elem];
matched_resistors = {}
matched_resistor_key = {}
for threshold in [1, .9, .8, .7, .6, .5]:#, .5, .4, .3]:
for restt in range(2, 15):
for t in templates:
templ = cv2.imread(t,cv2.CV_LOAD_IMAGE_COLOR);
res = 20 - restt;
template = cv2.resize(templ, dsize = (0,0), fx = res/10., fy = res/10., interpolation = cv2.INTER_CUBIC);
[template, g]= Preprocess.getImageToSendToContour(template, False);
w, h = template.shape[::-1]
res = cv2.matchTemplate(im,template,cv2.TM_CCOEFF_NORMED)
loc = np.where( res >= threshold)
pts = []
for pt in zip(*loc[::-1]):
pts += [[pt[0], pt[1], w, h, 'r']];
indicesToRemove_ii = []
indicesToRemove_i = []
for i in range(0, len(unmatched_resistors)):
ii = -1;
minDistance = 1000000;
for ifindmin in range(0,len(pts)):
dist = Postprocess.distance_resistor(unmatched_resistors[i][0:5], pts[ifindmin]);
if dist < minDistance and (ifindmin not in indicesToRemove_ii) and dist < 20 and dist < matched_resistor_key.get(i, 10000)*(threshold*1.1) and dist>7:
ii = ifindmin;
minDistance = dist;
if ii == -1:
continue;
matchresistor = unmatched_resistors[i][:];
matchresistor[0] = pts[ii][0]; #take on location of the element in the circuit
matchresistor[1] = pts[ii][1];
matchresistor[2] = pts[ii][2];
matchresistor[3] = pts[ii][3];
indicesToRemove_ii += [ii];
indicesToRemove_i += [i];
matched_resistors[i] = matchresistor;
matched_resistor_key[i] = dist;
#newunmatched = []
#for i in range(0, len(unmatched_resistors)):
# if i not in indicesToRemove_i:
# newunmatched += [unmatched_resistors[i]]
#unmatched_resistors = newunmatched;
# for r in matched_resistors:
# print r
print matched_resistors
print unmatched_resistors
for i in matched_resistors.keys():
pt = matched_resistors[i];
cv2.rectangle(im, (pt[0], pt[1]), (pt[0] + pt[2], pt[1] + pt[3]), (0,0,0), 2)
matchresistor = unmatched_resistors[i];
matchresistor[0] = pt[0]; #take on location of the element in the circuit
matchresistor[1] = pt[1];
matchresistor[2] = pt[2];
matchresistor[3] = pt[3];
cv2.imshow('resistors', im);
# cv2.imshow('temp', template);
# key = cv2.waitKey(0)
return elements;
#cv2.rectangle(im, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
import Tree
# training parameter
k = 10
term_con = c.LAYER
term_thresh = 7
result_path = 'results/spamDT_10.acc'
model_name = 'spam_' + str(k) + 'fold_' + term_con + '_' + str(term_thresh)
# laod training data
training_data = loader.load_dataset('data/spambase.data')
# load threshold data
# threshs = loader.load_pickle_file('config/spam_threshold')
threshs = Preprocess.generate_thresholds(training_data[0], 'config/spam_thresh_path')
# start training
training_accs = []
training_cms = []
testing_accs = []
testing_cms = []
for i in range(k):
(tr_data, te_data) = Preprocess.prepare_k_fold_data(training_data, k, i + 1)
tree = Tree.DecisionTree()
tree.build(tr_data[0],
tr_data[1], threshs, term_con, term_thresh)
training_test_res = tree.test(tr_data[0], tr_data[1])
def detectCharsInPlates(listOfPossiblePlates, type, cameraName):
infoFromDatabase(cameraName)
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # Olası plakaların listesi boşsa
return listOfPossiblePlates # listeyi döndür
# Eğer bu noktaya geldiyse plakalar listesinde en az bir plaka olduğundan emin olabiliriz
for possiblePlate in listOfPossiblePlates: # Her olası plaka için for döngüsü
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate, type
) # Gri tonlamalı ve threshold görüntüler elde etmek için önişlem
# Daha kolay görüntüleme ve karakter algılama için plaka görüntüsünün boyutunu arttırır
#possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0), fx = 1.5, fy = 1.5)
#Gri alanları gidermek için tekrar threshold
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# Plakadaki tüm olası karakterleri bulma
# Bu işlev ilk önce tüm konturları bulur, ardından sadece karakter alabilecek kontürleri içerir (diğer karakterlerle karşılaştırmadan)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
# Olası tüm karakterlerin bir listesi verildiğinde, plaka içindeki eşleşen karakter gruplarını bulur
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if (len(listOfListsOfMatchingCharsInPlate) == 0
): #Plakada eşleşen karakter grubu bulunamazsa
possiblePlate.strChars = ""
continue #Loop için başa dön
for i in range(0, len(listOfListsOfMatchingCharsInPlate)
): # Eşleşen karakterlerin her listesi ile
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX
) # Karakterleri soldan sağa sırala
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i]
) #üst üste binen karakterleri kaldır
# Her olası plaka içerisinde, potansiyel eşleştirme karakterlerinin en uzun listesinin gerçek karakter listesi olduğunu varsay
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# Eşleşen karakterlerin tüm vektörlerinde dolaş, en çok karaktere sahip olanın dizinini al
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]
) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(
listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# Plaka içerisinde eşleşen en uzun karakter listesinin gerçek karakter listesi olduğunu varsayalım
longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[
intIndexOfLongestListOfChars]
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, longestListOfMatchingCharsInPlate)
return listOfPossiblePlates
def predict_nii_multi(net, img1, img2, gpu, name, is_test=False):
net.eval()
img1 = np.array(img1, dtype='float64')
img1[img1 < -200] = -200
img1[img1 > 250] = 250
img1 = ((img1 + 200) * 255 // 450)
img1 = np.array(img1, dtype='uint8')
if not is_test:
img2 = np.array(img2, dtype='uint8')
for i in range(img1.shape[2]):
img1[:, :, i] = np.flip(img1[:, :, i], 0)
img1[:, :, i] = np.rot90(img1[:, :, i])
img1[:, :, i] = np.rot90(img1[:, :, i])
img1[:, :, i] = np.rot90(img1[:, :, i])
startposition, endposition = pp.getRangImageDepth(img2)
sub_srcimages = pp.make_multi_patch(img1, (128, 128), 5, 3,
startposition, endposition) / 255
layers = img1.shape[2]
save_nii = np.zeros((img1.shape[0], img1.shape[1], layers),
dtype='uint8')
for ind in range(startposition, endposition + 1, 1):
# ind=startposition+1
layer_input = sub_srcimages[:, :, :, (ind - startposition) *
25:(ind - startposition) * 25 + 25]
im = predict_layer_multi(net, layer_input, gpu)
im = np.flip(im, 0)
im = np.rot90(im)
im = np.rot90(im)
im = np.rot90(im)
# cv2.imshow('2', im*127)
# cv2.waitKey(0)
save_nii[:, :, ind] = im
print("Predicting {}-{}".format(name, ind))
pass
pass
else:
for i in range(img1.shape[2]):
img1[:, :, i] = np.flip(img1[:, :, i], 0)
img1[:, :, i] = np.rot90(img1[:, :, i])
img1[:, :, i] = np.rot90(img1[:, :, i])
img1[:, :, i] = np.rot90(img1[:, :, i])
startposition = 1
endposition = img1.shape[2] - 2
sub_srcimages = pp.make_multi_patch(img1, (128, 128), 5, 3,
startposition, endposition) / 255
layers = img1.shape[2]
save_nii = np.zeros((img1.shape[0], img1.shape[1], layers),
dtype='uint8')
for ind in range(startposition, endposition + 1, 1):
# ind=startposition+1
layer_input = sub_srcimages[:, :, :, (ind - startposition) *
25:(ind - startposition) * 25 + 25]
im = predict_layer_multi(net, layer_input, gpu)
im = np.flip(im, 0)
im = np.rot90(im)
im = np.rot90(im)
im = np.rot90(im)
# cv2.imshow('2', im*127)
# cv2.waitKey(0)
save_nii[:, :, ind] = im
print("Predicting {}-{}".format(name, ind))
pass
pass
save_nii = np.array(save_nii, dtype='uint8')
new_img = nib.Nifti1Image(save_nii, affine=np.eye(4))
nib.save(new_img, mPath.DataPath_Volume_Predict + name + '.nii')
from sklearn.ensemble import RandomForestRegressor
import sklearn.metrics as mc
import Preprocess as load
import time as time
import numpy as np
# Load data
train_data, train_label, test_data, test_label = load.read_data()
start = time.clock()
# Create model
rf = RandomForestRegressor(max_depth=10,
random_state=0,
n_estimators=200
)
# Traning model
pre_rf = rf.fit(train_data, train_label).predict(test_data)
# Calculate score
score_rf = rf.score(test_data, test_label)
pre_rf = np.reshape(pre_rf, [-1, 1])
[m,n] = np.shape(test_label)
rsquare =1- (((pre_rf- test_label) ** 2).sum()) / (((test_label - test_label.mean()) ** 2).sum())
prepro = rf.get_params()
se = mc.mean_squared_error(test_label, pre_rf)
print("R-square:" + str(score_rf))
def detectCharsInPlates(listOfPossiblePlates, save_intermediate, output_folder,
showSteps):
'''
This function processes the list of number plates and returns a lsit of processed number plates
with their text.
@input: List of images of number plates
@output:A list of number plates with all the information encapsulated
'''
# Initialize the varaibles
intPlateCounter = 0
imgContours = None
contours = []
if len(listOfPossiblePlates) == 0: # if list of possible plates is empty
l = []
l = l.append(listOfPossiblePlates)
print('No Plates found')
return l # return
# end if
# at this point we can be sure the list of possible plates has at least one plate
listOfPossiblePlates_refined = []
for possiblePlate in listOfPossiblePlates: # for each possible plate, this is a big for loop that takes up most of the function
#possiblePlate.imgPlate = cv2.fastNlMeansDenoisingColored(possiblePlate.imgPlate,None,15,15,7,21)
#possiblePlate.imgPlate = cv2.equalizeHist(possiblePlate.imgPlate)
# preprocess to get grayscale and threshold images
possiblePlate.imgGrayscale, possiblePlate.imgThresh = Preprocess.preprocess(
possiblePlate.imgPlate, save_intermediate, output_folder,
showSteps)
if showSteps == True: # show steps ###################################################
cv2.imshow("imgPlate", possiblePlate.imgPlate)
cv2.imshow("imgGrayscale", possiblePlate.imgGrayscale)
cv2.imshow("imgThresh", possiblePlate.imgThresh)
cv2.waitKey(0)
# increase size of plate image for easier viewing and char detection
possiblePlate.imgThresh = cv2.resize(possiblePlate.imgThresh, (0, 0),
fx=1.6,
fy=1.6,
interpolation=cv2.INTER_LINEAR)
# threshold again to eliminate any gray areas
thresholdValue, possiblePlate.imgThresh = cv2.threshold(
possiblePlate.imgThresh, 0.0, 255.0,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)
# This clears the image more removing all the unknown noise from it.
if showSteps == True: # show steps ###################################################
cv2.imshow("imgThresh_gray_remover", possiblePlate.imgThresh)
cv2.waitKey(0)
if save_intermediate == True: # show steps ###################################################
cv2.imwrite("%s/imgThresh_gray_remover.png" % (output_folder),
possiblePlate.imgThresh)
# end if # show steps #####################################################################
# find all possible chars in the plate,
# this function first finds all contours, then only includes contours that could be chars (without
# comparison to other chars yet)
listOfPossibleCharsInPlate = findPossibleCharsInPlate(
possiblePlate.imgGrayscale, possiblePlate.imgThresh)
if showSteps == True or save_intermediate == True:
height, width = possiblePlate.imgThresh.shape
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:] # clear the contours list
for possibleChar in listOfPossibleCharsInPlate:
contours.append(possibleChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (255, 255, 255))
#print('These are the possible characters in the plate :')
if showSteps == True: # show steps ###################################################
cv2.imshow("Possible_chars_in_plate", imgContours)
cv2.waitKey(0)
if save_intermediate == True: # show steps ###################################################
cv2.imwrite("%s/Possible_chars_in_plate.png" % (output_folder),
imgContours)
# end if # show steps #####################################################################
# given a list of all possible chars, find groups of matching chars within the plate
listOfListsOfMatchingCharsInPlate = findListOfListsOfMatchingChars(
listOfPossibleCharsInPlate)
if (len(listOfListsOfMatchingCharsInPlate) == 0
): # if no groups of matching chars were found in the plate
#print('\nNo matching characters found:')
if showSteps == True: # show steps ###############################################
print(
"chars found in plate number " + str(intPlateCounter) +
" = (none), click on any image and press a key to continue . . ."
)
intPlateCounter = intPlateCounter + 1
cv2.destroyAllWindows()
# end if # show steps #################################################################
possiblePlate.strChars = ""
continue # go back to top of for loop
# end if
if showSteps == True or save_intermediate == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
if showSteps == True:
cv2.imshow("A_Complete_plate", imgContours)
cv2.waitKey(0)
# end if # show steps #####################################################################
if save_intermediate == True: # show steps ###################################################
cv2.imwrite("%s/A_Complete_plate.png" % (output_folder),
imgContours)
# within each list of matching chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
# sort chars from left to right
listOfListsOfMatchingCharsInPlate[i].sort(
key=lambda matchingChar: matchingChar.intCenterX)
# and remove inner overlapping chars
listOfListsOfMatchingCharsInPlate[i] = removeInnerOverlappingChars(
listOfListsOfMatchingCharsInPlate[i])
# end for
if showSteps == True or save_intermediate == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
for listOfMatchingChars in listOfListsOfMatchingCharsInPlate:
intRandomBlue = random.randint(0, 255)
intRandomGreen = random.randint(0, 255)
intRandomRed = random.randint(0, 255)
del contours[:]
for matchingChar in listOfMatchingChars:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1,
(intRandomBlue, intRandomGreen, intRandomRed))
# end for
if showSteps == True:
cv2.imshow("Remove_Overlapping", imgContours)
cv2.waitKey(0)
if save_intermediate == True: # show steps ###################################################
cv2.imwrite("%s/Remove_Overlapping.png" % (output_folder),
imgContours)
# end if # show steps #####################################################################
"""
# within each possible plate, suppose the longest list of potential matching chars is the actual list of chars
intLenOfLongestListOfChars = 0
intIndexOfLongestListOfChars = 0
# loop through all the vectors of matching chars, get the index of the one with the most chars
for i in range(0, len(listOfListsOfMatchingCharsInPlate)):
if len(listOfListsOfMatchingCharsInPlate[i]) > intLenOfLongestListOfChars:
intLenOfLongestListOfChars = len(listOfListsOfMatchingCharsInPlate[i])
intIndexOfLongestListOfChars = i
# end if
# end for
"""
# suppose that the longest list of matching chars within the plate is the actual list of chars
#longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate[intIndexOfLongestListOfChars]
# All the left plates till now are elligible to be potential part of a number plate
#longestListOfMatchingCharsInPlate = listOfListsOfMatchingCharsInPlate
if showSteps == True or save_intermediate == True: # show steps ###################################################
imgContours = np.zeros((height, width, 3), np.uint8)
del contours[:]
for longestListOfMatchingCharsInPlate in listOfListsOfMatchingCharsInPlate:
for matchingChar in longestListOfMatchingCharsInPlate:
contours.append(matchingChar.contour)
# end for
cv2.drawContours(imgContours, contours, -1, (255, 255, 255))
if showSteps == True:
cv2.imshow("The_Longest_list_of_matching_chars", imgContours)
cv2.waitKey(0)
# end if # show steps #####################################################################
if save_intermediate == True: # show steps ###################################################
cv2.imwrite(
"%s/The_Longest_list_of_matching_chars.png" % (output_folder),
imgContours)
possiblePlate.strChars = recognizeCharsInPlate(
possiblePlate.imgThresh, listOfListsOfMatchingCharsInPlate,
save_intermediate, output_folder, showSteps)
if showSteps == True:
cv2.destroyAllWindows()
listOfPossiblePlates_refined.append(possiblePlate)
if showSteps == True: # show steps ###################################################
print("chars found in plate number " + str(intPlateCounter) +
" = " + possiblePlate.strChars +
", click on any image and press a key to continue . . .")
intPlateCounter = intPlateCounter + 1
cv2.waitKey(0)
# end if # show steps #####################################################################
# end of big for loop that takes up most of the function
if showSteps == True:
print(
"\nchar detection complete, click on any image and press a key to continue . . .\n"
)
cv2.waitKey(0)
# end if
return listOfPossiblePlates_refined # we return the list of plates with the probable plate number of each plate.