def convert_gray(self, img_file):
pix = cv2.imread(os.path.dirname(__file__) + img_file, 0)
pix_none = pix
pix_none = edge_detect.edge_detect(pix_none)
cv2.imshow('pix-none', np.array(pix_none))
# equalize histogram
pix_equal = cv2.equalizeHist(pix)
pix_equal = edge_detect.edge_detect(pix_equal)
cv2.imshow('pix-equal', np.array(pix_equal))
# Contrast Limited Adaptive Histogram Equalization
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))
pix_clahe = clahe.apply(pix)
cv2.imshow('pix-clahe-gray', np.array(pix_clahe))
pix_threshold = cv2.adaptiveThreshold(np.array(pix_clahe), 255,
cv2.ADAPTIVE_THRESH_MEAN_C,
cv2.THRESH_BINARY, 11, 4)
cv2.imshow('pix-threshold', pix_threshold)
pix_clahe = edge_detect.edge_detect(pix_clahe)
cv2.imshow('pix-clahe', np.array(pix_clahe))
cv2.waitKey()
def process(self, cascade, nested_cascade, img_file):
cascade = cv2.CascadeClassifier(self.current_dir + cascade)
nested = cv2.CascadeClassifier(self.current_dir + nested_cascade)
gray = cv2.imread(self.current_dir + img_file, 0)
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# gray = cv2.equalizeHist(gray)
face_rects = self.detect(gray, cascade)
vis = gray.copy()
self.draw_rects(vis, face_rects, (0, 255, 0))
eye_count = 0
if not nested.empty():
for x1, y1, x2, y2 in face_rects:
face_roi = gray[y1:y2, x1:x2]
face_vis = vis[y1:y2, x1:x2]
eyes_rects = self.detect(face_roi.copy(), nested)
self.draw_rects(face_vis, eyes_rects, (255, 0, 0))
for x1, y1, x2, y2 in eyes_rects:
eyes_roi = face_roi[y1:y2, x1:x2]
edge_eyes_roi = edge_detect.edge_detect(eyes_roi)
helper.save_image(
edge_eyes_roi, self.current_dir +
'/../resources/eyes_roi%s.png' % (eye_count))
# edge_eyes_roi = self.noise_removal.median(edge_eyes_roi)
edge_eyes_roi = self.noise_removal.opening(edge_eyes_roi)
helper.save_image(
edge_eyes_roi, self.current_dir +
'/../resources/eyes_roi_noise_removal%s.png' %
(eye_count))
eye_count += 1
edge_eyes_roi = np.array(edge_eyes_roi, dtype=np.uint8)
eyes_vis = face_vis[y1:y2, x1:x2]
circles = cv2.HoughCircles(edge_eyes_roi,
cv2.cv.CV_HOUGH_GRADIENT,
1,
20,
param1=50,
param2=30,
minRadius=0,
maxRadius=0)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv2.circle(eyes_vis, (i[0], i[1]), i[2],
(0, 255, 0), 2)
cv2.imshow('face detect', vis)
def convert_gray(self, img_file):
pix = cv2.imread(os.path.dirname(__file__) + img_file, 0)
pix_none = pix
pix_none = edge_detect.edge_detect(pix_none)
cv2.imshow('pix-none', np.array(pix_none))
# equalize histogram
pix_equal = cv2.equalizeHist(pix)
pix_equal = edge_detect.edge_detect(pix_equal)
cv2.imshow('pix-equal', np.array(pix_equal))
# Contrast Limited Adaptive Histogram Equalization
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(4, 4))
pix_clahe = clahe.apply(pix)
cv2.imshow('pix-clahe-gray', np.array(pix_clahe))
pix_threshold = cv2.adaptiveThreshold(np.array(pix_clahe) , 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 11, 4)
cv2.imshow('pix-threshold', pix_threshold)
pix_clahe = edge_detect.edge_detect(pix_clahe)
cv2.imshow('pix-clahe', np.array(pix_clahe))
cv2.waitKey()
def process(self, cascade, nested_cascade, img_file):
cascade = cv2.CascadeClassifier(self.current_dir + cascade)
nested = cv2.CascadeClassifier(self.current_dir + nested_cascade)
gray = cv2.imread(self.current_dir + img_file, 0)
# gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# gray = cv2.equalizeHist(gray)
face_rects = self.detect(gray, cascade)
vis = gray.copy()
self.draw_rects(vis, face_rects, (0, 255, 0))
eye_count = 0
if not nested.empty():
for x1, y1, x2, y2 in face_rects:
face_roi = gray[y1:y2, x1:x2]
face_vis = vis[y1:y2, x1:x2]
eyes_rects = self.detect(face_roi.copy(), nested)
self.draw_rects(face_vis, eyes_rects, (255, 0, 0))
for x1, y1, x2, y2 in eyes_rects:
eyes_roi = face_roi[y1:y2, x1:x2]
edge_eyes_roi = edge_detect.edge_detect(eyes_roi)
helper.save_image(edge_eyes_roi, self.current_dir + "/../resources/eyes_roi%s.png" % (eye_count))
# edge_eyes_roi = self.noise_removal.median(edge_eyes_roi)
edge_eyes_roi = self.noise_removal.opening(edge_eyes_roi)
helper.save_image(
edge_eyes_roi, self.current_dir + "/../resources/eyes_roi_noise_removal%s.png" % (eye_count)
)
eye_count += 1
edge_eyes_roi = np.array(edge_eyes_roi, dtype=np.uint8)
eyes_vis = face_vis[y1:y2, x1:x2]
circles = cv2.HoughCircles(
edge_eyes_roi, cv2.cv.CV_HOUGH_GRADIENT, 1, 20, param1=50, param2=30, minRadius=0, maxRadius=0
)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
cv2.circle(eyes_vis, (i[0], i[1]), i[2], (0, 255, 0), 2)
cv2.imshow("face detect", vis)