def extractimage(imageParam):
data = []
# ap = argparse.ArgumentParser()
# ap.add_argument("-t", "--training", required=True,
# help="path to the training images")
# args = vars(ap.parse_args())
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
image_scale = 1
# for imagePath in paths.list_images(args["training"]):
img = cv2.imdecode(numpy.fromstring(imageParam.read(), numpy.uint8),
cv2.IMREAD_UNCHANGED)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
desc = LocalBinaryPatterns(24, 8)
if faces == ():
return False
else:
hist = desc.describe(roi_gray)
data.append(hist)
return data
# print(hist)
# for x in hist:
# with open('data.txt', 'a') as f:
# f.write(str(x)+',')
# with open('dataset.csv','wb') as file:
# for line in data:
# file.write(line)
stepSize=stepSize,
windowSize=(winW, winH)):
# if the window does not meet our desired window size, ignore it
if window.shape[0] != winH or window.shape[1] != winW:
continue
# THIS IS WHERE YOU WOULD PROCESS YOUR WINDOW, SUCH AS APPLYING A
# MACHINE LEARNING CLASSIFIER TO CLASSIFY THE CONTENTS OF THE
# WINDOW
# WORKS WITH THIS...........BUT NOT THE cloudFeature call *****************
gray = cv2.cvtColor(window, cv2.COLOR_BGR2GRAY)
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
claheGray = clahe.apply(gray)
hist = desc.describe(claheGray)
# set up histogram
#hist = cloudFeature.get_feature_histogram(image)
prediction = model.predict([hist])[0]
# draw the window
clone = image.copy()
cv2.rectangle(clone, (x, y), (x + winW, y + winH), (0, 255, 0), 2)
# display the image and the prediction
cv2.putText(clone, prediction, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0,
(0, 0, 255), 3)
cv2.imshow("Window", clone)
required=True,
help="path to the tesitng images")
args = vars(ap.parse_args())
# initialize the local binary patterns descriptor along with
# the data and label lists
desc = LocalBinaryPatterns(24, 8)
data = []
labels = []
# loop over the training images
for imagePath in paths.list_images(args["training"]):
# load the image, convert it to grayscale, and describe it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
# extract the label from the image path, then update the
# label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
# train a Linear SVM on the data
model = LinearSVC(C=100.0, random_state=42)
model.fit(data, labels)
# loop over the testing images
for imagePath in paths.list_images(args["testing"]):
# load the image, convert it to grayscale, describe it,
# and classify it
image = cv2.imread(imagePath)
import cv2
data = []
ap = argparse.ArgumentParser()
ap.add_argument("-t",
"--training",
required=True,
help="path to the training images")
args = vars(ap.parse_args())
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml')
image_scale = 1
for imagePath in paths.list_images(args["training"]):
img = cv2.imread(imagePath)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, 1.3, 5)
for (x, y, w, h) in faces:
cv2.rectangle(gray, (x, y), (x + w, y + h), (255, 0, 0), 2)
roi_gray = gray[y:y + h, x:x + w]
roi_color = img[y:y + h, x:x + w]
rezise_img = cv2.resize(roi_gray, (300, 300))
desc = LocalBinaryPatterns(24, 8)
hist = desc.describe(rezise_img)
data.append(hist)
# print(faces)
for x in data:
with open('data.txt', 'a') as f:
f.write(str(x) + ',')
def binary_function(type_rec):
l = []
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-t",
"--training",
required=False,
default='images/training/',
help="path to the training images")
ap.add_argument("-e",
"--testing",
required=False,
default='images/testing/',
help="path to the tesitng images")
args = vars(ap.parse_args())
# initialize the local binary patterns descriptor along with
# the data and label lists
if type_rec == 'symbol':
desc = LocalBinaryPatterns(24, 8)
else:
desc = LocalBinaryPatterns(24, 3)
data = []
labels = []
# loop over the training images
for imagePath in paths.list_images(args["training"]):
# for imagePath in os.listdir('images/training/'):
# load the image, convert it to grayscale, and describe it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
# extract the label from the image path, then update the
# label and data lists
labels.append(imagePath.split(os.path.sep)[-2])
data.append(hist)
model = DecisionTreeClassifier(random_state=0)
model.fit(data, labels)
# loop over the testing images
for imagePath in paths.list_images(args["testing"]):
# for imagePath in os.listdir('images/testing/'):
# load the image, convert it to grayscale, describe it,
# and classify it
image = cv2.imread(imagePath)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(gray)
prediction = model.predict(hist.reshape(1, -1))
# display the image and the prediction
# cv2.putText(image, prediction[0], (10, 30), cv2.FONT_HERSHEY_SIMPLEX,
# 1.0, (0, 0, 255), 3)
# cv2.imshow("Image", image)
# cv2.waitKey(0)
l.append([prediction[0], imagePath])
# print (l)
if type_rec == 'symbol':
result = symbol(l[0][0])
string = "The symbol is " + str(
l[0][0]
) + " Meaning of symbol: " + result[0] + " Reference: " + result[1]
else:
s_p = []
for i in l:
s_p.append(i[0])
patt = ', '.join(s_p)
string = "The symbols identified are " + patt
return string
help="path to the training images")
args = vars(ap.parse_args())
# initialize the local binary patterns descriptor along with
# the data and label lists
desc = LocalBinaryPatterns(24, 8)
data = []
labels = []
print("[INFO] Labeling Images...")
# loop over the training images
for imagePath in paths.list_images(args["training"]):
# load the image, convert it to grayscale, and describe it
image = cv2.imread(imagePath, 0)
#gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = desc.describe(image)
# extract the label from the image path, then update the
# label and data lists
# label = imagePath.split("\\")[1]
labels.append(imagePath.split("\\")[1])
data.append(hist)
print("[INFO] Images Labeled...")
#data = data.reshape(-1,1)
#labels = labels.reshape(-1,1)
# train a Linear SVM on the data
model = LinearSVC(C=100.0, random_state=42, max_iter=5000)
print("[INFO] SVM loaded...")
print("[INFO] Model fitting...")
model.fit(data, labels)
print("[INFO] Model fitted...")
patch_height = (img_height // patch_size) + 1
patch_length = patch_width * patch_height
img_name = path.split('.')[0] + ".JPG"
img = cv2.imread("data/test_images_site_32/" + img_name)
img = imutils.resize(img, width=1024)
img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
(img_h, img_s, img_v) = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
# Store gray patch descriptions
for (patch_id, (x, y, win)) in enumerate(
sliding_window(img_gray,
stepSize=patch_size,
windowSize=(win_size, win_size))):
hist4 = desc4.describe(win)
hist4 /= hist4.sum()
hist4 = hist4.reshape(1, -1)
proba_4 = model_lbp.predict_proba(hist4)
lbp_probas[img_id, patch_id] = proba_4
# Precompute h and s histograms
for (patch_id, (x, y, win_h, win_s)) in enumerate(
sliding_window_double(img_h,
img_s,
stepSize=patch_size,
windowSize=(win_size, win_size))):
h_flat = win_h.reshape(1, win_h.shape[0] * win_h.shape[1])
s_flat = win_s.reshape(1, win_s.shape[0] * win_s.shape[1])
cv2.waitKey(0)
elif search == 1:
# load the index and initialize the searchers
file_rgb = open("index_rgb.cpickle" + ".pkl", 'rb')
index_rgb = pickle.load(file_rgb)
searcher_rgb = Searcher(index_rgb)
desc_rgb = RGBHistogram([8, 8, 8])
queryFeatures_rgb = desc_rgb.describe(queryImage)
file_lbp = open("index_lbp.cpickle" + ".pkl", 'rb')
index_lbp = pickle.load(file_lbp)
searcher_lbp = Searcher(index_lbp)
desc_lbp = LocalBinaryPatterns(24, 8)
gray_lbp = cv2.cvtColor(queryImage, cv2.COLOR_BGR2GRAY)
queryFeatures_lbp = desc_lbp.describe(gray_lbp)
file_hog = open("index_hog.cpickle" + ".pkl", 'rb')
index_hog = pickle.load(file_hog)
searcher_hog = Searcher(index_hog)
winSize = (64, 64)
blockSize = (16, 16)
blockStride = (8, 8)
cellSize = (8, 8)
nbins = 9
derivAperture = 1
winSigma = 4.
histogramNormType = 0
L2HysThreshold = 2.0000000000000001e-01
gammaCorrection = 0
nlevels = 64
# Ensure patch size
if flag_resize_patch:
window_gray = imutils.resize(window_gray, width=100)
window_hsv = imutils.resize(window_hsv, width=100)
# Describe gray patch
(kps, descs) = dad.describe(window_gray)
if kps is None or descs is None:
continue
hist = bovw.describe(descs)
hist /= hist.sum()
hist = hist.toarray()
# Get lbp descriptions
hist4 = desc4.describe(window_gray)
hist8 = desc8.describe(window_gray)
hist4 /= hist4.sum()
hist8 /= hist8.sum()
hist4 = hist4.reshape(1, -1)
hist8 = hist8.reshape(1, -1)
proba_4 = model_lbp4.predict_proba(hist4).flatten()
proba_8 = model_lbp8.predict_proba(hist8).flatten()
# Apply idf factor to histogram
if idf is not None:
idf = idf.reshape(1, -1)
hist *= idf
trainingPath = "/home/krzysztof/Dokumenty/SNR_grupa1/images/training"
keyboard = glob.glob(trainingPath + "/keyboard" + "/*.*")
carpet = glob.glob(trainingPath + "/carpet" + "/*.*")
area_rug = glob.glob(trainingPath + "/area_rug" + "/*.*")
wrapping_paper = glob.glob(trainingPath + "/wrapping_paper" + "/*.*")
desc = LocalBinaryPatterns(24, 8)
data = []
labels = []
paths = carpet + keyboard + area_rug + wrapping_paper
print("Ekstrakcja cech\n")
for imagePath in paths:
print(imagePath + "\n")
image = cv2.imread(imagePath)
print(str(image.shape) + "\n")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # Skala szarości
hist = desc.describe(gray) # LBP na obrazie - zwraca histogram
# extract the label from the image path, then update the
# label and data lists
labels.append(imagePath.split("/")[-2])
data.append(hist)
# Koniec aktu 1: Mamy cechy obrazu. Następnie można użyć ich do rozpoznawania obrazów
# Akt 2
print("Uczenie SVC\n")
model = LinearSVC(C=100.0, random_state=42)
model.fit(data, labels)
print("Testowanie\n")
# Testing
keyboardTestPath = "/home/krzysztof/Dokumenty/SNR_grupa1/images/testing/keyboard.png"
carpetTestPath = "/home/krzysztof/Dokumenty/SNR_grupa1/images/testing/carpet.png"
required=True,
help="path to the training images")
ap.add_argument("-e", "--testing", required=True, help="blah")
args = vars(ap.parse_args())
bob = LocalBinaryPatterns(24, 8)
data = []
labels = []
for imagePath in paths.list_images(args['training']):
# image = io.imread(imagePath)
# # stretch = exposure.rescale_intensity(image)
# grey = skimage.color.rgb2gray(image)
image = cv2.imread(imagePath)
grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = bob.describe(grey)
labels.append(imagePath.split(os.path.sep)[-2])
data.append(hist)
StandardScaler.transform(data)
model = LinearSVC(C=1000.0, random_state=42)
model.fit(data, labels)
for imagePath in paths.list_images(args['testing']):
# image = io.imread(imagePath)
# stretch = exposure.rescale_intensity(image)
# grey = skimage.color.rgb2gray(stretch)
image = cv2.imread(imagePath)
grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist = bob.describe(grey)
prediction = model.predict(hist.reshape(1, -1))
widgets = [
"Analyzing Images: ",
progressbar.Percentage(), " ",
progressbar.Bar(), " ",
progressbar.ETA()
]
pbar = progressbar.ProgressBar(maxval=len(training_set),
widgets=widgets).start()
for i, image_path in enumerate(training_set):
# load the image, convert it to grayscale, and describe it
image = cv2.imread(image_path)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
hist4 = desc4.describe(gray)
hist4 /= hist4.sum()
data4.append(hist4)
hist8 = desc8.describe(gray)
hist8 /= hist8.sum()
data8.append(hist8)
label = image_path.split(os.path.sep)[-2]
labels.append(label)
pbar.update(i)
pbar.finish()
# train a Linear SVM on the data