def face_img_func(key, entry, viewer):
# Image conversion
img = entry['img'][0] # Use only a first data in the batch
assert(img.ndim == 3 and (img.shape[0] == 1 or img.shape[0] == 3))
img = np.transpose(img, (1, 2, 0))
img = img.copy() # for safety
img += 0.5 # [-0.5:0.5] -> [0:1]
# Draw
try:
detection_raw = entry['detection'][0]
detection = (detection_raw > 0.5)
if 0.0 <= detection_raw <= 1.0:
drawing.draw_detection(img, detection)
landmark = entry['landmark'][0]
visibility = entry['visibility'][0]
landmark_color = (0, 1, 0) if detection == 1 else (0, 0, 1)
drawing.draw_landmark(img, landmark, visibility, landmark_color, 0.5)
pose = entry['pose'][0]
drawing.draw_pose(img, pose)
gender = entry['gender'][0]
if 0.0 <= gender <= 1.0:
gender = (gender > 0.5)
drawing.draw_gender(img, gender)
except KeyError:
pass
img = (img * 255).astype(np.uint8)
caption = '{:02d}'.format(viewer.img_cnts[key])
return {'img': img, 'cap': caption}
def runOnImage(img):
img = img.astype(np.float32) / 255.0
landmarks, visibilities, poses, genders, rects = hyperface(img)
# Draw results
for i in six.moves.xrange(len(landmarks)):
landmark = landmarks[i]
visibility = visibilities[i]
pose = poses[i]
gender = genders[i]
rect = rects[i]
landmark_color = (0, 1, 0) # detected color
drawing.draw_landmark(img, landmark, visibility, landmark_color, 0.5, denormalize_scale=False)
drawing.draw_pose(img, pose, idx=i)
gender = (gender > 0.5)
drawing.draw_gender_rect(img, gender, rect)
img *= 255
return img
img = imgs[0]
detection = detections[0]
landmark = landmarks[0]
visibility = visibilitys[0]
pose = poses[0]
gender = genders[0]
img = np.transpose(img, (1, 2, 0))
img = img.copy()
img += 0.5 # [-0.5:0.5] -> [0:1]
detection = (detection > 0.5)
gender = (gender > 0.5)
# Draw results
drawing.draw_detection(img, detection)
landmark_color = (0, 1, 0) if detection == 1 else (0, 0, 1)
drawing.draw_landmark(img, landmark, visibility, landmark_color, 0.5)
drawing.draw_pose(img, pose)
drawing.draw_gender(img, gender)
# Show image
logger.info('Show the result image')
# cv2.imshow('result', img)
# cv2.waitKey(0)
img = img * 255
cv2.imwrite('sample_images/lena_face1.png', img)
print('finished at', time.time())
print('cost time', time.time() - init_time)
landmarks, visibilities, poses, genders, rects = hyperface(img)
# Draw results
for i in six.moves.xrange(len(landmarks)):
landmark = landmarks[i]
visibility = visibilities[i]
pose = poses[i]
gender = genders[i]
rect = rects[i]
landmark_color = (0, 1, 0) # detected color
drawing.draw_landmark(img,
landmark,
visibility,
landmark_color,
0.5,
denormalize_scale=False)
drawing.draw_pose(img, pose, idx=i)
gender = (gender > 0.5)
# drawing.draw_gender(img, gender, idx=i)
drawing.draw_gender_rect(img, gender, rect)
# Send to imgviewer
img *= 255 # [0:1] -> [0:255]
max_cnt = 10
viewer_que.put(('imgs', '{}'.format(cnt_view), {'img': img}))
cnt_view = (cnt_view + 1) % max_cnt
cnt_img = (cnt_img + 1) % len(test)
time.sleep(1.0)