type=bool)
parser.add_argument("--cam",
help="Set to True if you are using webcam",
dest="cam",
default=False,
type=bool)
args = parser.parse_args()
# Instantiating the required classes.
detector = FDetector()
Gdetector = GDetector()
im_utils = Image()
detector.set_detector(args.model)
# Read the video
detection = detector.vdetect_face(args.vid_path, cam=args.cam)
run = True
while run:
# Get frames and bounding boxes of faces
for img, boxes, conf in detection:
for box in boxes:
# Get the face by cropping
c_img = im_utils.crop(img, box)
# Apply Gender Detection
gender = Gdetector.detect_gender(c_img, enable_gpu=args.enable_gpu)
# format the label
label = "{}: {:.2f}%".format(gender[0], gender[1] * 100)
# Put padding for rendering the label
Y = box[1] - 10 if box[1] - 10 > 10 else box[1] + 10
cv2.putText(img, label, (box[0], Y), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 255, 0), 2)
from visionlib.face.detection import FDetector
import cv2
import argparse
# Configre the parser.
parser = argparse.ArgumentParser()
parser.add_argument("vid_path", help="Path to image")
parser.add_argument("--enable-gpu",
help="Set to true to enable gpu support",
dest="enable_gpu",
default=False,
type=bool)
args = parser.parse_args()
# Instantiating the required classes.
detector = FDetector()
detector.set_detector("dnn")
# Read the video and apply face detection.
detection = detector.vdetect_face(args.vid_path,
show=True,
enable_gpu=args.enable_gpu)
for img, box, conf in detection:
print(box, conf)
parser.add_argument("vid_path", help="Path to image")
parser.add_argument("--enable-gpu",
help="Set to true to enable gpu support",
dest="enable_gpu",
default=False,
type=bool)
args = parser.parse_args()
# Instantiating the required classes.
detector = FDetector()
Gdetector = GDetector()
im_utils = Image()
detector.set_detector("dnn")
# Read the video
detection = detector.vdetect_face(args.vid_path)
run = True
while run:
# Get frames and bounding boxes of faces
for img, boxes, conf in detection:
for box in boxes:
# Get the face by cropping
c_img = im_utils.crop(img, box)
# Apply Gender Detection
gender = Gdetector.detect_gender(c_img, enable_gpu=args.enable_gpu)
# format the label
label = "{}: {:.2f}%".format(gender[0], gender[1] * 100)
# Put padding for rendering the label
Y = box[1] - 10 if box[1] - 10 > 10 else box[1] + 10
cv2.putText(img, label, (box[0], Y), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 255, 0), 2)