# Load DLIB'S CNN Face Detector model
print("[INFO] Loading CNN face detection model...")
detector = dlib.cnn_face_detection_model_v1(args["model"])
# Load the input image from disk, resize it and then convert it from BGR to RGB channel ordering
# (which is what the dlib expects)
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform face detection using dlib's face detector
start = time.time()
print("[INFO] Performing face detection with dlib face detector...")
results = detector(rgb, args["upsample"])
end = time.time()
print("[INFO] Face detection took {:.4f} seconds".format(end - start))
# Convert the resulting dlib rectangle objects to bounding boxes, then ensure that the bounding boxes are all within
# the bounds of the input image
boxes = [convert_and_trim_bb(image, r.rect) for r in results]
# Loop over the bounding boxes
for (x, y, w, h) in boxes:
# Draw the bounding box on the image
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)
# Loop over the frames from the video stream
while True:
# Grab the frame from the threaded video stream, resize it to have a maximum width of 600 pixels and then convert it
# from BGR to RGB channel ordering (which is what the dlib expects)
frame = vs.read()
frame = imutils.resize(frame, width=600)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Perform face detection using dlib's face detector
start = time.time()
print("[INFO] Performing face detection with dlib face detector...")
results = detector(rgb, args["upsample"])
end = time.time()
print("[INFO] Face detection took {:.4f} seconds".format(end - start))
# Convert the resulting dlib rectangle objects to bounding boxes, then ensure that the bounding boxes are all within
# the bounds of the input image
boxes = [convert_and_trim_bb(frame, r.rect) for r in results]
# Loop over the bounding boxes
for (x, y, w, h) in boxes:
# Draw the bounding box on the image
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Show the output image
cv2.imshow("Output", frame)
key = cv2.waitKey(1) & 0xFF
# If the `q` key was pressed, break from the loop
if key == ord("q"):
break
# Do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()
# Load DLIB's HOG + Liner SVM Face Detector model
print("[INFO] Loading HOG + Linear SVM face detector...")
detector = dlib.get_frontal_face_detector()
# Load the input image from disk, resize it and then convert it from BGR to RGB channel ordering
# (which is what dlib expects)
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Perform face detection using dlib's frontal face detector
start = time.time()
print("[INFO] Performing face detection with dlib...")
rects = detector(rgb, args["upsample"])
end = time.time()
print("[INFO] Face detection took {:.4f} seconds".format(end - start))
# Convert the resulting dlib rectangle objects to bounding boxes, then ensure the bounding boxes are all within the
# bounds of the input image
boxes = [convert_and_trim_bb(image, r) for r in rects]
# Loop over the bounding boxes
for (x, y, w, h) in boxes:
# Draw the bounding box on the image
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Show the output image
cv2.imshow("Output", image)
cv2.waitKey(0)
while True:
# Grab the frame from the threaded video stream, resize it to have a maximum width of 600 pixels and then convert it
# from BGR to RGB channel ordering (which is what the dlib expects)
frame = vs.read()
frame = imutils.resize(frame, width=600)
rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Perform face detection using dlib's face detector
start = time.time()
print(
"[INFO] Performing face detection with dlib frontal face detector...")
rects = detector(rgb, args["upsample"])
end = time.time()
print("[INFO] Face detection took {:.4f} seconds".format(end - start))
# Convert the resulting dlib rectangle objects to bounding boxes, then ensure that the bounding boxes are all within
# the bounds of the input image
boxes = [convert_and_trim_bb(frame, r) for r in rects]
# Loop over the bounding boxes
for (x, y, w, h) in boxes:
# Draw the bounding box on the image
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Show the output image
cv2.imshow("Output", frame)
key = cv2.waitKey(1) & 0xFF
# If the `q` key was pressed, break from the loop
if key == ord("q"):
break
# Do a bit of cleanup
cv2.destroyAllWindows()
vs.stop()