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1. 完成Maas-cv CR标准 自查
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9957634
238 lines
7.8 KiB
Python
238 lines
7.8 KiB
Python
import cv2
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import numpy as np
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from modelscope.outputs import OutputKeys
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from modelscope.preprocessors.image import load_image
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def numpy_to_cv2img(img_array):
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"""to convert a np.array with shape(h, w) to cv2 img
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Args:
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img_array (np.array): input data
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Returns:
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cv2 img
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"""
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img_array = (img_array - img_array.min()) / (
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img_array.max() - img_array.min() + 1e-5)
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img_array = (img_array * 255).astype(np.uint8)
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img_array = cv2.applyColorMap(img_array, cv2.COLORMAP_JET)
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return img_array
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def draw_joints(image, np_kps, score, threshold=0.2):
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lst_parent_ids_17 = [0, 0, 0, 1, 2, 0, 0, 5, 6, 7, 8, 5, 6, 11, 12, 13, 14]
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lst_left_ids_17 = [1, 3, 5, 7, 9, 11, 13, 15]
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lst_right_ids_17 = [2, 4, 6, 8, 10, 12, 14, 16]
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lst_parent_ids_15 = [0, 0, 1, 2, 3, 1, 5, 6, 14, 8, 9, 14, 11, 12, 1]
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lst_left_ids_15 = [2, 3, 4, 8, 9, 10]
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lst_right_ids_15 = [5, 6, 7, 11, 12, 13]
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if np_kps.shape[0] == 17:
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lst_parent_ids = lst_parent_ids_17
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lst_left_ids = lst_left_ids_17
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lst_right_ids = lst_right_ids_17
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elif np_kps.shape[0] == 15:
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lst_parent_ids = lst_parent_ids_15
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lst_left_ids = lst_left_ids_15
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lst_right_ids = lst_right_ids_15
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for i in range(len(lst_parent_ids)):
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pid = lst_parent_ids[i]
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if i == pid:
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continue
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if (score[i] < threshold or score[1] < threshold):
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continue
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if i in lst_left_ids and pid in lst_left_ids:
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color = (0, 255, 0)
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elif i in lst_right_ids and pid in lst_right_ids:
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color = (255, 0, 0)
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else:
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color = (0, 255, 255)
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cv2.line(image, (int(np_kps[i, 0]), int(np_kps[i, 1])),
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(int(np_kps[pid][0]), int(np_kps[pid, 1])), color, 3)
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for i in range(np_kps.shape[0]):
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if score[i] < threshold:
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continue
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cv2.circle(image, (int(np_kps[i, 0]), int(np_kps[i, 1])), 5,
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(0, 0, 255), -1)
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def draw_box(image, box):
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cv2.rectangle(image, (int(box[0][0]), int(box[0][1])),
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(int(box[1][0]), int(box[1][1])), (0, 0, 255), 2)
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def realtime_object_detection_bbox_vis(image, bboxes):
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for bbox in bboxes:
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cv2.rectangle(image, (bbox[0], bbox[1]), (bbox[2], bbox[3]),
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(255, 0, 0), 2)
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return image
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def draw_keypoints(output, original_image):
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poses = np.array(output[OutputKeys.POSES])
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scores = np.array(output[OutputKeys.SCORES])
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boxes = np.array(output[OutputKeys.BOXES])
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assert len(poses) == len(scores) and len(poses) == len(boxes)
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image = cv2.imread(original_image, -1)
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for i in range(len(poses)):
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draw_box(image, np.array(boxes[i]))
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draw_joints(image, np.array(poses[i]), np.array(scores[i]))
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return image
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def draw_face_detection_no_lm_result(img_path, detection_result):
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bboxes = np.array(detection_result[OutputKeys.BOXES])
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scores = np.array(detection_result[OutputKeys.SCORES])
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img = cv2.imread(img_path)
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assert img is not None, f"Can't read img: {img_path}"
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for i in range(len(scores)):
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bbox = bboxes[i].astype(np.int32)
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x1, y1, x2, y2 = bbox
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score = scores[i]
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cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
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cv2.putText(
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img,
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f'{score:.2f}', (x1, y2),
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1,
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1.0, (0, 255, 0),
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thickness=1,
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lineType=8)
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print(f'Found {len(scores)} faces')
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return img
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def draw_facial_expression_result(img_path, facial_expression_result):
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label_idx = facial_expression_result[OutputKeys.LABELS]
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map_list = [
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'Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral'
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]
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label = map_list[label_idx]
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img = cv2.imread(img_path)
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assert img is not None, f"Can't read img: {img_path}"
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cv2.putText(
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img,
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'facial expression: {}'.format(label), (10, 10),
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1,
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1.0, (0, 255, 0),
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thickness=1,
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lineType=8)
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print('facial expression: {}'.format(label))
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return img
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def draw_face_detection_result(img_path, detection_result):
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bboxes = np.array(detection_result[OutputKeys.BOXES])
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kpss = np.array(detection_result[OutputKeys.KEYPOINTS])
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scores = np.array(detection_result[OutputKeys.SCORES])
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img = cv2.imread(img_path)
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assert img is not None, f"Can't read img: {img_path}"
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for i in range(len(scores)):
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bbox = bboxes[i].astype(np.int32)
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kps = kpss[i].reshape(-1, 2).astype(np.int32)
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score = scores[i]
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x1, y1, x2, y2 = bbox
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cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
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for kp in kps:
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cv2.circle(img, tuple(kp), 1, (0, 0, 255), 1)
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cv2.putText(
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img,
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f'{score:.2f}', (x1, y2),
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1,
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1.0, (0, 255, 0),
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thickness=1,
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lineType=8)
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print(f'Found {len(scores)} faces')
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return img
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def created_boxed_image(image_in, box):
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image = load_image(image_in)
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img = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
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cv2.rectangle(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
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(0, 255, 0), 3)
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return img
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def show_video_tracking_result(video_in_path, bboxes, video_save_path):
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cap = cv2.VideoCapture(video_in_path)
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for i in range(len(bboxes)):
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box = bboxes[i]
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success, frame = cap.read()
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if success is False:
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raise Exception(video_in_path,
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' can not be correctly decoded by OpenCV.')
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if i == 0:
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size = (frame.shape[1], frame.shape[0])
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fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
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video_writer = cv2.VideoWriter(video_save_path, fourcc,
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cap.get(cv2.CAP_PROP_FPS), size,
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True)
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cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0),
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5)
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video_writer.write(frame)
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video_writer.release
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cap.release()
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def panoptic_seg_masks_to_image(masks):
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draw_img = np.zeros([masks[0].shape[0], masks[0].shape[1], 3])
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from mmdet.core.visualization.palette import get_palette
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mask_palette = get_palette('coco', 133)
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from mmdet.core.visualization.image import _get_bias_color
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taken_colors = set([0, 0, 0])
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for i, mask in enumerate(masks):
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color_mask = mask_palette[i]
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while tuple(color_mask) in taken_colors:
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color_mask = _get_bias_color(color_mask)
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taken_colors.add(tuple(color_mask))
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mask = mask.astype(bool)
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draw_img[mask] = color_mask
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return draw_img
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def semantic_seg_masks_to_image(masks):
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from mmdet.core.visualization.palette import get_palette
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mask_palette = get_palette('coco', 133)
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draw_img = np.zeros([masks[0].shape[0], masks[0].shape[1], 3])
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for i, mask in enumerate(masks):
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color_mask = mask_palette[i]
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mask = mask.astype(bool)
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draw_img[mask] = color_mask
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return draw_img
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def show_video_summarization_result(video_in_path, result, video_save_path):
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frame_indexes = result[OutputKeys.OUTPUT]
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cap = cv2.VideoCapture(video_in_path)
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for i in range(len(frame_indexes)):
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idx = frame_indexes[i]
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success, frame = cap.read()
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if success is False:
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raise Exception(video_in_path,
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' can not be correctly decoded by OpenCV.')
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if i == 0:
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size = (frame.shape[1], frame.shape[0])
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fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
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video_writer = cv2.VideoWriter(video_save_path, fourcc,
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cap.get(cv2.CAP_PROP_FPS), size,
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True)
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if idx == 1:
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video_writer.write(frame)
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video_writer.release()
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cap.release()
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