face-parser
1. Face Segmentation
1.1. BiSeNet
from visage.bisenet import BiSeNetFaceParser
from visage.visualize import apply_colormap
img = load_img() # torch.Tensor [3, H, W] in range [-1, 1]
face_parser = BiSeNetFaceParser()
segmentation_mask = face_parser.parse(img)
# Plotting
segmentation_mask_colored = apply_colormap(segmentation_mask) # Colorizes each class with a distinct color for better viewing
plt.imshow(segmentation_mask_colored)
2. Face Bounding Boxes
2.1. FaceBoxesV2
from visage.bounding_boxes.face_boxes_v2 import FaceBoxesV2
img = load_img() # np.ndarray [H, W, 3] in range [0, 255]
detector = FaceBoxesV2()
detected_bboxes = detector.detect(img)
# Plotting
cv2.rectangle(img, detected_bboxes[0].get_point1(), detected_bboxes[0].get_point2(), (255, 0, 0), 10)
plt.imshow(img)
3. Facial Landmarks
3.1. PIPNet
from visage.landmark_detection.pipnet import PIPNet
img = load_img() # np.ndarray [H, W, 3] in range [0, 255]
detected_bboxes = ... # <- from step 2.
pip_net = PIPNet()
landmarks = pip_net.forward(img, detected_bboxes[0])
# Plotting
for x, y in landmarks:
cv2.circle(img, (int(x), int(y)), 5, (255, 0, 0), -1)
plt.imshow(img)
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