help="Model to use for detecting face",
dest="face_model",
default="hog",
)
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 class
fdetector = FDetector()
kdetector = KDetector()
# Read the image
img = cv2.imread(args.img_path)
# Set the detectors
kdetector.set_detector(args.key_model)
fdetector.set_detector(args.face_model)
# Detect face and the points
fimg, boxes, conf = fdetector.detect_face(img, enable_gpu=True)
points = kdetector.detect_keypoints(img, rects=boxes)
# Draw the points
pimg = kdetector.draw_points(fimg, points, show=True)
from visionlib.face.detection import FDetector
import cv2
import argparse
# Configre the parser.
parser = argparse.ArgumentParser()
parser.add_argument("img_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()
# Read the image
img = cv2.imread(args.img_path)
detector.set_detector("hog")
# Apply face detection and show image
d_img, boxes, conf = detector.detect_face(img,
show=True,
enable_gpu=args.enable_gpu)
for box, conf in zip(boxes, conf):
print(box, conf)
parser = argparse.ArgumentParser()
parser.add_argument("img_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.
Fdetector = FDetector()
Gdetector = GDetector()
im_utils = Image()
# Read the image
img = cv2.imread(args.img_path)
Fdetector.set_detector("hog")
# Detect the Faces
d_img, boxes, conf = Fdetector.detect_face(img)
for box in boxes:
# Get the face by cropping
c_img = im_utils.crop(d_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(d_img, label, (box[0], Y), cv2.FONT_HERSHEY_SIMPLEX, 0.7,
(0, 255, 0), 2)
cv2.imshow("pic2", img)
cv2.waitKey(0)