[to #42322933]move postprocess helper into utilities

modelscope/models/cv/video_single_object_tracking/utils/utils.py

@@ -238,24 +238,3 @@ def check_box(box: list, image_height, image_width) -> bool:
     if box[3] < 0 or box[3] >= image_height:
         return False
     return True
-
-
-def show_tracking_result(video_in_path, bboxes, video_save_path):
-    cap = cv2.VideoCapture(video_in_path)
-    for i in range(len(bboxes)):
-        box = bboxes[i]
-        success, frame = cap.read()
-        if success is False:
-            raise Exception(video_in_path,
-                            ' can not be correctly decoded by OpenCV.')
-        if i == 0:
-            size = (frame.shape[1], frame.shape[0])
-            fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
-            video_writer = cv2.VideoWriter(video_save_path, fourcc,
-                                           cap.get(cv2.CAP_PROP_FPS), size,
-                                           True)
-        cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0),
-                      5)
-        video_writer.write(frame)
-    video_writer.release
-    cap.release()

modelscope/utils/cv/heatmap.py

@@ -1,18 +0,0 @@
-import cv2
-import numpy as np
-
-
-def numpy_to_cv2img(vis_img):
-    """to convert a np.array Hotmap with shape(h, w) to cv2 img
-
-    Args:
-        vis_img (np.array): input data
-
-    Returns:
-        cv2 img
-    """
-    vis_img = (vis_img - vis_img.min()) / (
-        vis_img.max() - vis_img.min() + 1e-5)
-    vis_img = (vis_img * 255).astype(np.uint8)
-    vis_img = cv2.applyColorMap(vis_img, cv2.COLORMAP_JET)
-    return vis_img

modelscope/utils/cv/image_utils.py

@@ -0,0 +1,136 @@
+import cv2
+import numpy as np
+
+from modelscope.outputs import OutputKeys
+from modelscope.preprocessors.image import load_image
+
+
+def numpy_to_cv2img(img_array):
+    """to convert a np.array with shape(h, w) to cv2 img
+
+    Args:
+        img_array (np.array): input data
+
+    Returns:
+        cv2 img
+    """
+    img_array = (img_array - img_array.min()) / (
+        img_array.max() - img_array.min() + 1e-5)
+    img_array = (img_array * 255).astype(np.uint8)
+    img_array = cv2.applyColorMap(img_array, cv2.COLORMAP_JET)
+    return img_array
+
+
+def draw_joints(image, np_kps, score, threshold=0.2):
+    lst_parent_ids_17 = [0, 0, 0, 1, 2, 0, 0, 5, 6, 7, 8, 5, 6, 11, 12, 13, 14]
+    lst_left_ids_17 = [1, 3, 5, 7, 9, 11, 13, 15]
+    lst_right_ids_17 = [2, 4, 6, 8, 10, 12, 14, 16]
+
+    lst_parent_ids_15 = [0, 0, 1, 2, 3, 1, 5, 6, 14, 8, 9, 14, 11, 12, 1]
+    lst_left_ids_15 = [2, 3, 4, 8, 9, 10]
+    lst_right_ids_15 = [5, 6, 7, 11, 12, 13]
+
+    if np_kps.shape[0] == 17:
+        lst_parent_ids = lst_parent_ids_17
+        lst_left_ids = lst_left_ids_17
+        lst_right_ids = lst_right_ids_17
+
+    elif np_kps.shape[0] == 15:
+        lst_parent_ids = lst_parent_ids_15
+        lst_left_ids = lst_left_ids_15
+        lst_right_ids = lst_right_ids_15
+
+    for i in range(len(lst_parent_ids)):
+        pid = lst_parent_ids[i]
+        if i == pid:
+            continue
+
+        if (score[i] < threshold or score[1] < threshold):
+            continue
+
+        if i in lst_left_ids and pid in lst_left_ids:
+            color = (0, 255, 0)
+        elif i in lst_right_ids and pid in lst_right_ids:
+            color = (255, 0, 0)
+        else:
+            color = (0, 255, 255)
+
+        cv2.line(image, (int(np_kps[i, 0]), int(np_kps[i, 1])),
+                 (int(np_kps[pid][0]), int(np_kps[pid, 1])), color, 3)
+
+    for i in range(np_kps.shape[0]):
+        if score[i] < threshold:
+            continue
+        cv2.circle(image, (int(np_kps[i, 0]), int(np_kps[i, 1])), 5,
+                   (0, 0, 255), -1)
+
+
+def draw_box(image, box):
+    cv2.rectangle(image, (int(box[0][0]), int(box[0][1])),
+                  (int(box[1][0]), int(box[1][1])), (0, 0, 255), 2)
+
+
+def draw_keypoints(output, original_image):
+    poses = np.array(output[OutputKeys.POSES])
+    scores = np.array(output[OutputKeys.SCORES])
+    boxes = np.array(output[OutputKeys.BOXES])
+    assert len(poses) == len(scores) and len(poses) == len(boxes)
+    image = cv2.imread(original_image, -1)
+    for i in range(len(poses)):
+        draw_box(image, np.array(boxes[i]))
+        draw_joints(image, np.array(poses[i]), np.array(scores[i]))
+    return image
+
+
+def draw_face_detection_result(img_path, detection_result):
+    bboxes = np.array(detection_result[OutputKeys.BOXES])
+    kpss = np.array(detection_result[OutputKeys.KEYPOINTS])
+    scores = np.array(detection_result[OutputKeys.SCORES])
+    img = cv2.imread(img_path)
+    assert img is not None, f"Can't read img: {img_path}"
+    for i in range(len(scores)):
+        bbox = bboxes[i].astype(np.int32)
+        kps = kpss[i].reshape(-1, 2).astype(np.int32)
+        score = scores[i]
+        x1, y1, x2, y2 = bbox
+        cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
+        for kp in kps:
+            cv2.circle(img, tuple(kp), 1, (0, 0, 255), 1)
+        cv2.putText(
+            img,
+            f'{score:.2f}', (x1, y2),
+            1,
+            1.0, (0, 255, 0),
+            thickness=1,
+            lineType=8)
+    print(f'Found {len(scores)} faces')
+    return img
+
+
+def created_boxed_image(image_in, box):
+    image = load_image(image_in)
+    img = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+    cv2.rectangle(img, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
+                  (0, 255, 0), 3)
+    return image
+
+
+def show_video_tracking_result(video_in_path, bboxes, video_save_path):
+    cap = cv2.VideoCapture(video_in_path)
+    for i in range(len(bboxes)):
+        box = bboxes[i]
+        success, frame = cap.read()
+        if success is False:
+            raise Exception(video_in_path,
+                            ' can not be correctly decoded by OpenCV.')
+        if i == 0:
+            size = (frame.shape[1], frame.shape[0])
+            fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
+            video_writer = cv2.VideoWriter(video_save_path, fourcc,
+                                           cap.get(cv2.CAP_PROP_FPS), size,
+                                           True)
+        cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0),
+                      5)
+        video_writer.write(frame)
+    video_writer.release
+    cap.release()

modelscope/utils/nlp/nlp_utils.py

@@ -0,0 +1,43 @@
+from typing import List
+
+from modelscope.outputs import OutputKeys
+from modelscope.pipelines.nlp import (ConversationalTextToSqlPipeline,
+                                      DialogStateTrackingPipeline)
+
+
+def text2sql_tracking_and_print_results(
+        test_case, pipelines: List[ConversationalTextToSqlPipeline]):
+    for p in pipelines:
+        last_sql, history = '', []
+        for item in test_case['utterance']:
+            case = {
+                'utterance': item,
+                'history': history,
+                'last_sql': last_sql,
+                'database_id': test_case['database_id'],
+                'local_db_path': test_case['local_db_path']
+            }
+            results = p(case)
+            print({'question': item})
+            print(results)
+            last_sql = results['text']
+            history.append(item)
+
+
+def tracking_and_print_dialog_states(
+        test_case, pipelines: List[DialogStateTrackingPipeline]):
+    import json
+    pipelines_len = len(pipelines)
+    history_states = [{}]
+    utter = {}
+    for step, item in enumerate(test_case):
+        utter.update(item)
+        result = pipelines[step % pipelines_len]({
+            'utter':
+            utter,
+            'history_states':
+            history_states
+        })
+        print(json.dumps(result))
+
+        history_states.extend([result[OutputKeys.OUTPUT], {}])

tests/pipelines/test_action_recognition.py

@@ -15,23 +15,6 @@ class ActionRecognitionTest(unittest.TestCase):
     def setUp(self) -> None:
         self.model_id = 'damo/cv_TAdaConv_action-recognition'
 
-    @unittest.skip('deprecated, download model from model hub instead')
-    def test_run_with_direct_file_download(self):
-        model_path = 'https://aquila2-online-models.oss-cn-shanghai.aliyuncs.com/maas_test/pytorch_model.pt'
-        config_path = 'https://aquila2-online-models.oss-cn-shanghai.aliyuncs.com/maas_test/configuration.json'
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            model_file = osp.join(tmp_dir, ModelFile.TORCH_MODEL_FILE)
-            with open(model_file, 'wb') as ofile1:
-                ofile1.write(File.read(model_path))
-            config_file = osp.join(tmp_dir, ModelFile.CONFIGURATION)
-            with open(config_file, 'wb') as ofile2:
-                ofile2.write(File.read(config_path))
-            recognition_pipeline = pipeline(
-                Tasks.action_recognition, model=tmp_dir)
-            result = recognition_pipeline(
-                'data/test/videos/action_recognition_test_video.mp4')
-            print(f'recognition output: {result}.')
-
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_modelhub(self):
         recognition_pipeline = pipeline(

tests/pipelines/test_body_2d_keypoints.py

@@ -9,59 +9,9 @@ from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.pipelines.base import Pipeline
 from modelscope.utils.constant import Tasks
+from modelscope.utils.cv.image_utils import draw_keypoints
 from modelscope.utils.test_utils import test_level
 
-lst_parent_ids_17 = [0, 0, 0, 1, 2, 0, 0, 5, 6, 7, 8, 5, 6, 11, 12, 13, 14]
-lst_left_ids_17 = [1, 3, 5, 7, 9, 11, 13, 15]
-lst_right_ids_17 = [2, 4, 6, 8, 10, 12, 14, 16]
-lst_spine_ids_17 = [0]
-
-lst_parent_ids_15 = [0, 0, 1, 2, 3, 1, 5, 6, 14, 8, 9, 14, 11, 12, 1]
-lst_left_ids_15 = [2, 3, 4, 8, 9, 10]
-lst_right_ids_15 = [5, 6, 7, 11, 12, 13]
-lst_spine_ids_15 = [0, 1, 14]
-
-
-def draw_joints(image, np_kps, score, threshold=0.2):
-    if np_kps.shape[0] == 17:
-        lst_parent_ids = lst_parent_ids_17
-        lst_left_ids = lst_left_ids_17
-        lst_right_ids = lst_right_ids_17
-
-    elif np_kps.shape[0] == 15:
-        lst_parent_ids = lst_parent_ids_15
-        lst_left_ids = lst_left_ids_15
-        lst_right_ids = lst_right_ids_15
-
-    for i in range(len(lst_parent_ids)):
-        pid = lst_parent_ids[i]
-        if i == pid:
-            continue
-
-        if (score[i] < threshold or score[1] < threshold):
-            continue
-
-        if i in lst_left_ids and pid in lst_left_ids:
-            color = (0, 255, 0)
-        elif i in lst_right_ids and pid in lst_right_ids:
-            color = (255, 0, 0)
-        else:
-            color = (0, 255, 255)
-
-        cv2.line(image, (int(np_kps[i, 0]), int(np_kps[i, 1])),
-                 (int(np_kps[pid][0]), int(np_kps[pid, 1])), color, 3)
-
-    for i in range(np_kps.shape[0]):
-        if score[i] < threshold:
-            continue
-        cv2.circle(image, (int(np_kps[i, 0]), int(np_kps[i, 1])), 5,
-                   (0, 0, 255), -1)
-
-
-def draw_box(image, box):
-    cv2.rectangle(image, (int(box[0][0]), int(box[0][1])),
-                  (int(box[1][0]), int(box[1][1])), (0, 0, 255), 2)
-
 
 class Body2DKeypointsTest(unittest.TestCase):
 
@@ -71,14 +21,7 @@ class Body2DKeypointsTest(unittest.TestCase):
 
     def pipeline_inference(self, pipeline: Pipeline, pipeline_input):
         output = pipeline(pipeline_input)
-        poses = np.array(output[OutputKeys.POSES])
-        scores = np.array(output[OutputKeys.SCORES])
-        boxes = np.array(output[OutputKeys.BOXES])
-        assert len(poses) == len(scores) and len(poses) == len(boxes)
-        image = cv2.imread(self.test_image, -1)
-        for i in range(len(poses)):
-            draw_box(image, np.array(boxes[i]))
-            draw_joints(image, np.array(poses[i]), np.array(scores[i]))
+        image = draw_keypoints(output, self.test_image)
         cv2.imwrite('pose_keypoint.jpg', image)
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')

tests/pipelines/test_conversational_text_to_sql.py

@@ -9,6 +9,7 @@ from modelscope.pipelines import pipeline
 from modelscope.pipelines.nlp import ConversationalTextToSqlPipeline
 from modelscope.preprocessors import ConversationalTextToSqlPreprocessor
 from modelscope.utils.constant import Tasks
+from modelscope.utils.nlp.nlp_utils import text2sql_tracking_and_print_results
 from modelscope.utils.test_utils import test_level
 
 
@@ -25,24 +26,6 @@ class ConversationalTextToSql(unittest.TestCase):
         ]
     }
 
-    def tracking_and_print_results(
-            self, pipelines: List[ConversationalTextToSqlPipeline]):
-        for my_pipeline in pipelines:
-            last_sql, history = '', []
-            for item in self.test_case['utterance']:
-                case = {
-                    'utterance': item,
-                    'history': history,
-                    'last_sql': last_sql,
-                    'database_id': self.test_case['database_id'],
-                    'local_db_path': self.test_case['local_db_path']
-                }
-                results = my_pipeline(case)
-                print({'question': item})
-                print(results)
-                last_sql = results['text']
-                history.append(item)
-
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
         cache_path = snapshot_download(self.model_id)
@@ -61,7 +44,7 @@ class ConversationalTextToSql(unittest.TestCase):
                 model=model,
                 preprocessor=preprocessor)
         ]
-        self.tracking_and_print_results(pipelines)
+        text2sql_tracking_and_print_results(self.test_case, pipelines)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
@@ -77,7 +60,7 @@ class ConversationalTextToSql(unittest.TestCase):
                 model=model,
                 preprocessor=preprocessor)
         ]
-        self.tracking_and_print_results(pipelines)
+        text2sql_tracking_and_print_results(self.test_case, pipelines)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_name(self):
@@ -85,12 +68,12 @@ class ConversationalTextToSql(unittest.TestCase):
             pipeline(
                 task=Tasks.conversational_text_to_sql, model=self.model_id)
         ]
-        self.tracking_and_print_results(pipelines)
+        text2sql_tracking_and_print_results(self.test_case, pipelines)
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_with_default_model(self):
         pipelines = [pipeline(task=Tasks.conversational_text_to_sql)]
-        self.tracking_and_print_results(pipelines)
+        text2sql_tracking_and_print_results(self.test_case, pipelines)
 
 
 if __name__ == '__main__':

tests/pipelines/test_crowd_counting.py

@@ -2,13 +2,12 @@
 import unittest
 
 import cv2
-import numpy as np
 from PIL import Image
 
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
-from modelscope.utils.cv.heatmap import numpy_to_cv2img
+from modelscope.utils.cv.image_utils import numpy_to_cv2img
 from modelscope.utils.logger import get_logger
 from modelscope.utils.test_utils import test_level
 

tests/pipelines/test_dialog_state_tracking.py

@@ -1,15 +1,14 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import unittest
-from typing import List
 
 from modelscope.hub.snapshot_download import snapshot_download
 from modelscope.models import Model
 from modelscope.models.nlp import SpaceForDialogStateTracking
-from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.pipelines.nlp import DialogStateTrackingPipeline
 from modelscope.preprocessors import DialogStateTrackingPreprocessor
 from modelscope.utils.constant import Tasks
+from modelscope.utils.nlp.nlp_utils import tracking_and_print_dialog_states
 from modelscope.utils.test_utils import test_level
 
 
@@ -79,24 +78,6 @@ class DialogStateTrackingTest(unittest.TestCase):
         'User-8': 'Thank you, goodbye',
     }]
 
-    def tracking_and_print_dialog_states(
-            self, pipelines: List[DialogStateTrackingPipeline]):
-        import json
-        pipelines_len = len(pipelines)
-        history_states = [{}]
-        utter = {}
-        for step, item in enumerate(self.test_case):
-            utter.update(item)
-            result = pipelines[step % pipelines_len]({
-                'utter':
-                utter,
-                'history_states':
-                history_states
-            })
-            print(json.dumps(result))
-
-            history_states.extend([result[OutputKeys.OUTPUT], {}])
-
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
         cache_path = snapshot_download(self.model_id, revision='update')
@@ -111,7 +92,7 @@ class DialogStateTrackingTest(unittest.TestCase):
                 model=model,
                 preprocessor=preprocessor)
         ]
-        self.tracking_and_print_dialog_states(pipelines)
+        tracking_and_print_dialog_states(pipelines)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_from_modelhub(self):
@@ -128,7 +109,7 @@ class DialogStateTrackingTest(unittest.TestCase):
                 preprocessor=preprocessor)
         ]
 
-        self.tracking_and_print_dialog_states(pipelines)
+        tracking_and_print_dialog_states(pipelines)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_with_model_name(self):
@@ -138,7 +119,7 @@ class DialogStateTrackingTest(unittest.TestCase):
                 model=self.model_id,
                 model_revision='update')
         ]
-        self.tracking_and_print_dialog_states(pipelines)
+        tracking_and_print_dialog_states(pipelines)
 
 
 if __name__ == '__main__':

tests/pipelines/test_face_detection.py

@@ -9,6 +9,7 @@ from modelscope.msdatasets import MsDataset
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
+from modelscope.utils.cv.image_utils import draw_face_detection_result
 from modelscope.utils.test_utils import test_level
 
 
@@ -17,46 +18,21 @@ class FaceDetectionTest(unittest.TestCase):
     def setUp(self) -> None:
         self.model_id = 'damo/cv_resnet_facedetection_scrfd10gkps'
 
-    def show_result(self, img_path, bboxes, kpss, scores):
-        bboxes = np.array(bboxes)
-        kpss = np.array(kpss)
-        scores = np.array(scores)
-        img = cv2.imread(img_path)
-        assert img is not None, f"Can't read img: {img_path}"
-        for i in range(len(scores)):
-            bbox = bboxes[i].astype(np.int32)
-            kps = kpss[i].reshape(-1, 2).astype(np.int32)
-            score = scores[i]
-            x1, y1, x2, y2 = bbox
-            cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 0), 2)
-            for kp in kps:
-                cv2.circle(img, tuple(kp), 1, (0, 0, 255), 1)
-            cv2.putText(
-                img,
-                f'{score:.2f}', (x1, y2),
-                1,
-                1.0, (0, 255, 0),
-                thickness=1,
-                lineType=8)
+    def show_result(self, img_path, detection_result):
+        img = draw_face_detection_result(img_path, detection_result)
         cv2.imwrite('result.png', img)
-        print(
-            f'Found {len(scores)} faces, output written to {osp.abspath("result.png")}'
-        )
+        print(f'output written to {osp.abspath("result.png")}')
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_dataset(self):
         input_location = ['data/test/images/face_detection.png']
-        # alternatively:
-        # input_location = '/dir/to/images'
 
         dataset = MsDataset.load(input_location, target='image')
         face_detection = pipeline(Tasks.face_detection, model=self.model_id)
         # note that for dataset output, the inference-output is a Generator that can be iterated.
         result = face_detection(dataset)
         result = next(result)
-        self.show_result(input_location[0], result[OutputKeys.BOXES],
-                         result[OutputKeys.KEYPOINTS],
-                         result[OutputKeys.SCORES])
+        self.show_result(input_location[0], result)
 
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_modelhub(self):
@@ -64,18 +40,14 @@ class FaceDetectionTest(unittest.TestCase):
         img_path = 'data/test/images/face_detection.png'
 
         result = face_detection(img_path)
-        self.show_result(img_path, result[OutputKeys.BOXES],
-                         result[OutputKeys.KEYPOINTS],
-                         result[OutputKeys.SCORES])
+        self.show_result(img_path, result)
 
     @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_modelhub_default_model(self):
         face_detection = pipeline(Tasks.face_detection)
         img_path = 'data/test/images/face_detection.png'
         result = face_detection(img_path)
-        self.show_result(img_path, result[OutputKeys.BOXES],
-                         result[OutputKeys.KEYPOINTS],
-                         result[OutputKeys.SCORES])
+        self.show_result(img_path, result)
 
 
 if __name__ == '__main__':

tests/pipelines/test_face_image_generation.py

@@ -1,5 +1,4 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
-import os
 import os.path as osp
 import unittest
 

tests/pipelines/test_face_recognition.py

@@ -21,7 +21,6 @@ class FaceRecognitionTest(unittest.TestCase):
 
         face_recognition = pipeline(
             Tasks.face_recognition, model=self.model_id)
-        # note that for dataset output, the inference-output is a Generator that can be iterated.
         emb1 = face_recognition(img1)[OutputKeys.IMG_EMBEDDING]
         emb2 = face_recognition(img2)[OutputKeys.IMG_EMBEDDING]
         sim = np.dot(emb1[0], emb2[0])

tests/pipelines/test_image2image_generation.py

@@ -3,6 +3,7 @@ import unittest
 
 from torchvision.utils import save_image
 
+from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level
@@ -27,13 +28,13 @@ class Image2ImageGenerationTest(unittest.TestCase):
         result2 = img2img_gen_pipeline(('data/test/images/img2img_input.jpg',
                                         'data/test/images/img2img_style.jpg'))
         save_image(
-            result1['output_img'].clamp(-1, 1),
+            result1[OutputKeys.OUTPUT_IMG].clamp(-1, 1),
             'result1.jpg',
             range=(-1, 1),
             normalize=True,
             nrow=4)
         save_image(
-            result2['output_img'].clamp(-1, 1),
+            result2[OutputKeys.OUTPUT_IMG].clamp(-1, 1),
             'result2.jpg',
             range=(-1, 1),
             normalize=True,

tests/pipelines/test_image_matting.py

@@ -18,19 +18,6 @@ class ImageMattingTest(unittest.TestCase):
     def setUp(self) -> None:
         self.model_id = 'damo/cv_unet_image-matting'
 
-    @unittest.skip('deprecated, download model from model hub instead')
-    def test_run_with_direct_file_download(self):
-        model_path = 'http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs' \
-                     '.com/data/test/maas/image_matting/matting_person.pb'
-        with tempfile.TemporaryDirectory() as tmp_dir:
-            model_file = osp.join(tmp_dir, ModelFile.TF_GRAPH_FILE)
-            with open(model_file, 'wb') as ofile:
-                ofile.write(File.read(model_path))
-            img_matting = pipeline(Tasks.portrait_matting, model=tmp_dir)
-
-            result = img_matting('data/test/images/image_matting.png')
-            cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
-
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_dataset(self):
         input_location = ['data/test/images/image_matting.png']

tests/pipelines/test_image_style_transfer.py

@@ -15,7 +15,7 @@ class ImageStyleTransferTest(unittest.TestCase):
     def setUp(self) -> None:
         self.model_id = 'damo/cv_aams_style-transfer_damo'
 
-    @unittest.skip('deprecated, download model from model hub instead')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
         snapshot_path = snapshot_download(self.model_id)
         print('snapshot_path: {}'.format(snapshot_path))

tests/pipelines/test_key_word_spotting_farfield.py

@@ -1,7 +1,6 @@
 import os.path
 import unittest
 
-from modelscope.fileio import File
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
 from modelscope.utils.test_utils import test_level

tests/pipelines/test_ofa_tasks.py

@@ -4,14 +4,13 @@ import unittest
 from os import path as osp
 
 import cv2
-import numpy as np
 from PIL import Image
 
 from modelscope.models import Model
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
-from modelscope.preprocessors.image import load_image
 from modelscope.utils.constant import Tasks
+from modelscope.utils.cv.image_utils import created_boxed_image
 from modelscope.utils.test_utils import test_level
 
 
@@ -22,11 +21,9 @@ class OfaTasksTest(unittest.TestCase):
         os.makedirs(self.output_dir, exist_ok=True)
 
     def save_img(self, image_in, box, image_out):
-        image = load_image(image_in)
-        img = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
-        cv2.rectangle(img, (int(box[0]), int(box[1])),
-                      (int(box[2]), int(box[3])), (0, 255, 0), 3)
-        cv2.imwrite(osp.join(self.output_dir, image_out), img)
+        cv2.imwrite(
+            osp.join(self.output_dir, image_out),
+            created_boxed_image(image_in, box))
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_with_image_captioning_with_model(self):

tests/pipelines/test_person_image_cartoon.py

@@ -24,19 +24,6 @@ class ImageCartoonTest(unittest.TestCase):
             cv2.imwrite('result.png', result[OutputKeys.OUTPUT_IMG])
             print(f'Output written to {osp.abspath("result.png")}')
 
-    @unittest.skip('deprecated, download model from model hub instead')
-    def test_run_by_direct_model_download(self):
-        model_dir = './assets'
-        if not os.path.exists(model_dir):
-            os.system(
-                'wget https://invi-label.oss-cn-shanghai.aliyuncs.com/label/model/cartoon/assets.zip'
-            )
-            os.system('unzip assets.zip')
-
-        img_cartoon = pipeline(
-            Tasks.image_portrait_stylization, model=model_dir)
-        self.pipeline_inference(img_cartoon, self.test_image)
-
     @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
     def test_run_modelhub(self):
         img_cartoon = pipeline(

tests/pipelines/test_skin_retouching.py

@@ -23,10 +23,9 @@ class SkinRetouchingTest(unittest.TestCase):
         cv2.imwrite('result_skinretouching.png', result[OutputKeys.OUTPUT_IMG])
         print(f'Output written to {osp.abspath("result_skinretouching.png")}')
 
-    @unittest.skip('deprecated, download model from model hub instead')
+    @unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
     def test_run_by_direct_model_download(self):
         model_dir = snapshot_download(self.model_id)
-
         skin_retouching = pipeline(Tasks.skin_retouching, model=model_dir)
         self.pipeline_inference(skin_retouching, self.test_image)
 

tests/pipelines/test_video_single_object_tracking.py

@@ -1,11 +1,10 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
 import unittest
 
-from modelscope.models.cv.video_single_object_tracking.utils.utils import \
-    show_tracking_result
 from modelscope.outputs import OutputKeys
 from modelscope.pipelines import pipeline
 from modelscope.utils.constant import Tasks
+from modelscope.utils.cv.image_utils import show_video_tracking_result
 from modelscope.utils.test_utils import test_level
 
 
@@ -22,8 +21,8 @@ class SingleObjectTracking(unittest.TestCase):
         init_bbox = [414, 343, 514, 449]  # [x1, y1, x2, y2]
         result = video_single_object_tracking((video_path, init_bbox))
         print('result is : ', result[OutputKeys.BOXES])
-        show_tracking_result(video_path, result[OutputKeys.BOXES],
-                             './tracking_result.avi')
+        show_video_tracking_result(video_path, result[OutputKeys.BOXES],
+                                   './tracking_result.avi')
 
     @unittest.skipUnless(test_level() >= 1, 'skip test in current test level')
     def test_run_modelhub_default_model(self):