def do_hog(hog_type='xeryus', char_size=(72, 72), window_size=(80, 80),
block_size=(2, 2), cell_size=(8, 8), nbins=9):
# reset hog tree
features = []
labels = []
testFeatures = []
testLabels = []
FEATURE_DIR.rmtree()
FEATURE_DIR.mkdir()
logging.debug("Creating test and training set")
for label in IMG_DIR.listdir(filter=DIRS_NO_LINKS):
for f in label.listdir(pattern='*.ppm'):
img = cv2.imread(f)
if hog_type == 'xeryus':
hist = hog.hog_xeryus(img, char_size, window_size, block_size,
cell_size, nbins)
else:
hist = hog_alternative(img)
features.append(hist[:,0])
labels.append(str(label.name))
logging.info("HOG feature size: %d", len(hist))
np.save(FEATURE_DIR + 'hog', features)
np.save(FEATURE_DIR + 'labels', labels)
def do_hog(char_size=(32, 32), window_size=(80, 80), block_size=(2, 2), cell_size=(8, 8), nbins=9, train_split=0.7):
# reset hog tree
features = []
labels = []
test_features = []
test_labels = []
HOG_DIR.rmtree()
HOG_DIR.mkdir()
train_dir = HOG_DIR + "train/"
test_dir = HOG_DIR + "test/"
train_dir.mkdir()
test_dir.mkdir()
logging.debug("Creating test and training set")
for label in IMG_DIR.listdir(filter=DIRS_NO_LINKS):
for f in label.listdir(pattern="*.ppm"):
img = cv2.imread(f)
hist = hog.hog_xeryus(img, char_size, window_size, block_size, cell_size, nbins)
# Split into training and test set
if np.random.random_sample() <= train_split:
features.append(hist[:, 0])
labels.append(str(label.name))
else:
test_features.append(hist[:, 0])
test_labels.append(str(label.name))
logging.info("HOG feature size: %d", len(hist))
np.save(HOG_DIR + "train/hog.npy", features)
np.save(HOG_DIR + "train/labels.npy", labels)
np.save(HOG_DIR + "test/hog.npy", test_features)
np.save(HOG_DIR + "test/labels.npy", test_labels)
def do_hog(char_size=(32, 32),
window_size=(80, 80),
block_size=(2, 2),
cell_size=(8, 8),
nbins=9,
train_split=0.7):
# reset hog tree
features = []
labels = []
test_features = []
test_labels = []
HOG_DIR.rmtree()
HOG_DIR.mkdir()
train_dir = HOG_DIR + 'train/'
test_dir = HOG_DIR + 'test/'
train_dir.mkdir()
test_dir.mkdir()
logging.debug("Creating test and training set")
for label in IMG_DIR.listdir(filter=DIRS_NO_LINKS):
for f in label.listdir(pattern='*.ppm'):
img = cv2.imread(f)
hist = hog.hog_xeryus(img, char_size, window_size, block_size,
cell_size, nbins)
# Split into training and test set
if np.random.random_sample() <= train_split:
features.append(hist[:, 0])
labels.append(str(label.name))
else:
test_features.append(hist[:, 0])
test_labels.append(str(label.name))
logging.info("HOG feature size: %d", len(hist))
np.save(HOG_DIR + 'train/hog.npy', features)
np.save(HOG_DIR + 'train/labels.npy', labels)
np.save(HOG_DIR + 'test/hog.npy', test_features)
np.save(HOG_DIR + 'test/labels.npy', test_labels)
def recursion(self, word_img, cuts, currentCut, lexicon, wordString,
probability, stateProbabilities, transProbabilities, classes,
hypotheses, savedPredictions, minCutWindow, maxCutWindow):
if currentCut == len(cuts) - 1:
if wordString in hypotheses:
hypotheses[wordString] = max(hypotheses[wordString],
probability)
else:
hypotheses[wordString] = probability
return
# Find the next cut
start = currentCut
for end in range(start + 1, len(cuts)):
if not minCutWindow <= (cuts[end] - cuts[start]) < maxCutWindow:
continue
window = word_img[:, cuts[start]:cuts[end]]
window = cut_letters.removeWhitelines(window)
if not window is None:
f = hog.hog_xeryus(window).reshape(1, -1)
# First check if the selected combination has been predicted before
if str(start) in savedPredictions and str(
end) in savedPredictions[str(start)]:
predictions = savedPredictions[str(start)][str(end)]
else: # Else calculate it and add to database
predictions = self.getPredictions(self.knn, f, classes)
savedPredictions[str(start)][str(end)] = predictions
for prediction, prob in predictions.iteritems():
# Add the predicted character to the word
wordString = wordString + prediction
# Add a factor to overcome advantages for large windows
factor = end - start
# Add the prediction probability to the total probability
probability += factor * np.log10(prob)
# Add the state (position of character) probability to the total probability
if len(wordString) <= len(stateProbabilities):
if prediction in stateProbabilities[len(wordString) -
1]:
probability += factor * stateProbabilities[
len(wordString) - 1][prediction]
else:
continue
# probability -= 5
else:
continue
#probability -= 5
# Add the transition probability to the total probability
if len(wordString) > 1:
if wordString[-2] in transProbabilities:
if wordString[-1] in transProbabilities[
wordString[-2]]:
probability += factor * transProbabilities[
wordString[-2]][wordString[-1]]
else:
continue
# probability -= 5
else:
continue
# probability -= 5
# Set the correct cut index
currentCut = end
# Into recursion, and beyond!
self.recursion(word_img, cuts, currentCut, lexicon,
wordString, probability, stateProbabilities,
transProbabilities, classes, hypotheses,
savedPredictions, minCutWindow,
maxCutWindow)
word_img = img[rect['top']:rect['bottom'], rect['left']:rect['right']]
word_img = cut_letters.removeWhitelines(word_img)
if word_img is None:
logger.warning('Word not good for classifying: {}'.format(text))
continue
# Get the cuts
cuts = find_cuts(word_img)
if cuts is None:
logger.warning('No cuts found in word')
continue
cv2.imwrite(out_dir + 'original.png', word_img)
cuts_img = cut_letters.showCuts(word_img.copy(), cuts)
cv2.imwrite(out_dir + 'cuts.png', cuts_img)
i = 0
for start in range(len(cuts)):
for end in range(start+1, len(cuts)):
if not (15 < cuts[end] - cuts[start] < 100):
continue # Skip too small/too large sections
segment = word_img[:,cuts[start]:cuts[end]]
f = hog_xeryus(segment)
letter = svm.predict(f[:,0])
print('{}: {}:{} {}'.format(i, cuts[start], cuts[end], letter[0]))
cv2.imwrite(out_dir + str(i) + '-' + letter[0] + '.png', segment)
i += 1
sys.exit(0)
def recursion(self, word_img, cuts, currentCut, lexicon, wordString, probability, stateProbabilities, transProbabilities, classes, hypotheses, savedPredictions, minCutWindow, maxCutWindow):
if currentCut == len(cuts)-1:
if wordString in hypotheses:
hypotheses[wordString] = max(hypotheses[wordString], probability)
else:
hypotheses[wordString] = probability
return
# Find the next cut
start = currentCut
for end in range(start+1, len(cuts)):
if not minCutWindow <= (cuts[end] - cuts[start]) < maxCutWindow:
continue
window = word_img[:,cuts[start]:cuts[end]]
window = cut_letters.removeWhitelines(window)
if not window is None:
f = hog.hog_xeryus(window).reshape(1, -1)
# First check if the selected combination has been predicted before
if str(start) in savedPredictions and str(end) in savedPredictions[str(start)]:
predictions = savedPredictions[str(start)][str(end)]
else: # Else calculate it and add to database
predictions = self.getPredictions(self.knn, f, classes)
savedPredictions[str(start)][str(end)] = predictions
for prediction, prob in predictions.iteritems():
# Add the predicted character to the word
wordString = wordString + prediction
# Add a factor to overcome advantages for large windows
factor = end-start
# Add the prediction probability to the total probability
probability += factor * np.log10(prob)
# Add the state (position of character) probability to the total probability
if len(wordString) <= len(stateProbabilities):
if prediction in stateProbabilities[len(wordString)-1]:
probability += factor * stateProbabilities[len(wordString)-1][prediction]
else:
continue
# probability -= 5
else:
continue
#probability -= 5
# Add the transition probability to the total probability
if len(wordString) > 1:
if wordString[-2] in transProbabilities:
if wordString[-1] in transProbabilities[wordString[-2]]:
probability += factor * transProbabilities[wordString[-2]][wordString[-1]]
else:
continue
# probability -= 5
else:
continue
# probability -= 5
# Set the correct cut index
currentCut = end
# Into recursion, and beyond!
self.recursion(
word_img, cuts, currentCut, lexicon, wordString,
probability, stateProbabilities, transProbabilities,
classes, hypotheses, savedPredictions, minCutWindow,
maxCutWindow
)
