def draw_one(self, frame, box, prob, landmark, count):
"""
Draw landmarks and boxes for only one face detected
"""
im_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# cv2 imwrite no need for RGB conversion
cv2.imwrite(
configs['frames_folder'] + "original" + str(count) + ".png", frame)
cropped_img = extract_face(im_rgb,
box,
image_size=224,
save_path=configs['frames_folder'] +
str(count) + ".png")
# Draw rectangle on frame
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 0, 255),
thickness=2)
transform = get_test_augmentations()
# (C, H, W) -> (H, W, C)
transformed_img = transform(
image=np.array(cropped_img).transpose((1, 2, 0)))['image']
# add batch dim
transformed_img = transformed_img.unsqueeze(0)
# transformed_img = transformed_img.to(device)
cue = self.model.infer(transformed_img)
# save cues
save_image(cue,
configs['frames_folder'] + "cues/" + str(count) + ".png")
score = cue.mean().cpu().item()
# Show probability
cv2.putText(frame, "FDet: " + "{:.3f}".format(prob),
(box[0], int(box[3])), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 2, cv2.LINE_AA)
cv2.putText(frame, "Spoof Score: " + "{:.6f}".format(score),
(box[0], int(box[3] + 30.0)), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 2, cv2.LINE_AA)
# Draw landmarks
# cv2.circle(frame, tuple(ld[0]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[1]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[2]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[3]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[4]), 5, (0, 0, 255), -1)
return frame
def _draw(self, frame, boxes, probs, landmarks, count):
"""
Draw landmarks and boxes for each face detected
"""
im_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# cv2 imwrite no need for RGB conversion
cv2.imwrite(
configs['frames_folder'] + "original" + str(count) + ".png", frame)
for box, prob, ld in zip(boxes, probs, landmarks):
cropped_img = extract_face(im_rgb,
box,
image_size=224,
save_path=configs['frames_folder'] +
str(count) + ".png")
# Draw rectangle on frame
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]),
(0, 0, 255),
thickness=2)
transform = get_test_augmentations()
# (C, H, W) -> (H, W, C)
transformed_img = transform(
image=np.array(cropped_img).transpose((1, 2, 0)))['image']
# add batch dim
transformed_img = transformed_img.unsqueeze(0)
output = self.model.classify(transformed_img)
prediction = torch.argmax(output, dim=1).cpu().numpy()
# Show probability
cv2.putText(frame, "FDet: " + str(prob),
(box[2], int(box[3] - 30.0)), cv2.FONT_HERSHEY_SIMPLEX,
1, (0, 0, 255), 2, cv2.LINE_AA)
cv2.putText(frame, str(labels_map.get(prediction[0])),
(box[2], box[3]), cv2.FONT_HERSHEY_SIMPLEX, 1,
(0, 0, 255), 2, cv2.LINE_AA)
# Draw landmarks
# cv2.circle(frame, tuple(ld[0]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[1]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[2]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[3]), 5, (0, 0, 255), -1)
# cv2.circle(frame, tuple(ld[4]), 5, (0, 0, 255), -1)
return frame
