def __init__(self):
self.current_dir = os.path.dirname(__file__)
self.noise_removal = NoiseRemoval()
self.morphology = Morphology()
def __init__(self):
self.current_dir = os.path.dirname(__file__)
self.noise_removal = NoiseRemoval()
self.morphology = Morphology()
class IrisDetect():
def __init__(self):
self.current_dir = os.path.dirname(__file__)
self.noise_removal = NoiseRemoval()
self.morphology = Morphology()
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 detect(self, img, cascade):
rects = cascade.detectMultiScale(img,
scaleFactor=1.3,
minNeighbors=4,
minSize=(30, 30),
flags=cv2.CASCADE_SCALE_IMAGE)
if len(rects) == 0:
return []
rects[:, 2:] += rects[:, :2]
return rects
def draw_rects(self, img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
class IrisDetect:
def __init__(self):
self.current_dir = os.path.dirname(__file__)
self.noise_removal = NoiseRemoval()
self.morphology = Morphology()
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 detect(self, img, cascade):
rects = cascade.detectMultiScale(
img, scaleFactor=1.3, minNeighbors=4, minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE
)
if len(rects) == 0:
return []
rects[:, 2:] += rects[:, :2]
return rects
def draw_rects(self, img, rects, color):
for x1, y1, x2, y2 in rects:
cv2.rectangle(img, (x1, y1), (x2, y2), color, 2)
