def hog_detector(image, gray):
# Finding height and width of frame
(img_h, img_w) = image.shape[:2]
cv2.putText(image, "HOG method", (img_w - 200, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
(205, 92, 92), 2)
# Get faces into webcam's image
rects = detector(gray, 0)
# For each detected face, find the landmark.
for (i, rect) in enumerate(rects):
# Finding points for rectangle to draw on face
x1, y1, x2, y2, w, h = rect.left(), rect.top(), rect.right() + \
1, rect.bottom() + 1, rect.width(), rect.height()
# cv2.rectangle(image, (x1, y1), (x1 + w, y1 + h), (205, 92, 92), 2)
draw_border(image, (x1, y1), (x2, y2), (205, 92, 92), 2, 10, 20)
# show the face number
cv2.putText(image, "Found #{}".format(i + 1), (x1 - 20, y1 - 20),
cv2.FONT_HERSHEY_TRIPLEX, 0.6, (205, 92, 92), 2)
# Make the prediction and transfom it to numpy array
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
# Draw on our image, all the finded cordinate points (x,y)
for (x, y) in shape:
cv2.circle(image, (x, y), 2, (0, 255, 0), -1)
def dl_detector(image, gray):
# Facial landmarks with DL
# Finding height and width of frame
(h, w) = image.shape[:2]
cv2.putText(image, "DL method", (w - 200, 20), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (205, 92, 92), 2)
total_faces = 0
# https://docs.opencv.org/trunk/d6/d0f/group__dnn.html#ga29f34df9376379a603acd8df581ac8d7
inputBlob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1,
(300, 300), (104, 177, 123))
detector.setInput(inputBlob)
detections = detector.forward()
for i in range(0, detections.shape[2]):
# Probability of prediction
prediction_score = detections[0, 0, i, 2]
if prediction_score < T:
continue
total_faces += 1
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(x1, y1, x2, y2) = box.astype("int")
# For better landmark detection
# y1, x2 = int(y1 * 1.15), int(x2 * 1.05)
# Make the prediction and transfom it to numpy array
shape = predictor(gray,
dlib.rectangle(left=x1, top=y1, right=x2, bottom=y2))
shape = face_utils.shape_to_np(shape)
# cv2.rectangle(image, (x1, y1), (x2, y2), (205, 92, 92), 2)
draw_border(image, (x1, y1), (x2, y2), (205, 92, 92), 2, 10, 20)
# show the face number with confidence score
prediction_score_str = "{:.2f}%".format(prediction_score * 100)
label = "Found #{} ({})".format(total_faces, prediction_score_str)
cv2.putText(image, label, (x1 - 20, y1 - 20), cv2.FONT_HERSHEY_TRIPLEX,
0.6, (205, 92, 92), 2)
# Draw on our image, all the finded cordinate points (x,y)
for (x, y) in shape:
cv2.circle(image, (x, y), 2, (0, 0, 255), -1)