def __call__(self, image, mask=None, **kwargs):
if self.is_resize:
image = pre_processor.resize(image,
size=(self.resize_height,
self.resize_width))
if mask is not None:
mask = pre_processor.resize(mask,
size=(self.resize_height,
self.resize_width))
origin_height = image.shape[0]
origin_width = image.shape[1]
top = randint(0, origin_height - self.height)
left = randint(0, origin_width - self.width)
# crop
image = image[top:top + self.height, left:left + self.width, :]
if mask is not None:
if np.ndim(mask) == 2:
mask = mask[top:top + self.height, left:left + self.width]
elif np.ndim(mask) == 3:
mask = mask[top:top + self.height, left:left + self.width, :]
return dict({'image': image, 'mask': mask}, **kwargs)
def show_semantic_segmentation(img, result, fps, window_height, window_width,
config):
orig_img = resize(img, size=[window_height, window_width])
seg_img = label_to_color_image(result, colormap)
seg_img = cv2.resize(seg_img, dsize=(window_width, window_height))
window_img = cv2.addWeighted(orig_img, 1, seg_img, 0.8, 0)
window_img = add_fps(window_img, fps)
window_name = "Semantic Segmentation Demo"
cv2.imshow(window_name, window_img)
def show_keypoint_detection(img, result, fps, window_height, window_width,
config):
window_img = resize(img, size=[window_height, window_width])
input_width = config.IMAGE_SIZE[1]
input_height = config.IMAGE_SIZE[0]
window_img = visualize_keypoint_detection(window_img, result[0],
(input_height, input_width))
window_img = add_fps(window_img, fps)
window_name = "Keypoint Detection Demo"
cv2.imshow(window_name, window_img)
def show_semantic_segmentation(img, result, fps, window_height, window_width,
config):
orig_img = resize(img, size=[window_height, window_width])
colormap = np.array(get_color_map(len(config.CLASSES)), dtype=np.uint8)
seg_img = label_to_color_image(result, colormap)
seg_img = cv2.resize(seg_img, dsize=(window_width, window_height))
window_img = cv2.addWeighted(orig_img, 1, seg_img, 0.8, 0)
window_img = add_fps(window_img, fps)
window_name = "Semantic Segmentation Demo"
cv2.imshow(window_name, window_img)
def show_object_detection(img, result, fps, window_height, window_width,
config):
window_img = resize(img, size=[window_height, window_width])
input_width = config.IMAGE_SIZE[1]
input_height = config.IMAGE_SIZE[0]
window_img = add_rectangle(config.CLASSES, window_img, result,
(input_height, input_width))
img = add_fps(window_img, fps)
window_name = "Object Detection Demo"
cv2.imshow(window_name, window_img)
def show_classification(img, result, fps, window_height, window_width, config):
window_img = resize(img, size=[window_height, window_width])
result_class = np.argmax(result, axis=1)
add_class_label(window_img,
text=str(result[0, result_class][0]),
font_scale=0.52,
dl_corner=(230, 230))
add_class_label(window_img,
text=config.CLASSES[result_class[0]],
font_scale=0.52,
dl_corner=(230, 210))
window_img = add_fps(window_img, fps)
window_name = "Classification Demo"
cv2.imshow(window_name, window_img)