[to #42322933] 1230: add hand detection

data/test/images/hand_detection.jpg

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44b225eaff012bd016fcfe8a3dbeace93fd418164f40e4b5f5b9f0d76f39097b
+size 308635

modelscope/metainfo.py

@@ -216,6 +216,7 @@ class Pipelines(object):
     image_portrait_enhancement = 'gpen-image-portrait-enhancement'
     image_to_image_generation = 'image-to-image-generation'
     image_object_detection_auto = 'yolox_image-object-detection-auto'
+    hand_detection = 'yolox-pai_hand-detection'
     skin_retouching = 'unet-skin-retouching'
     tinynas_classification = 'tinynas-classification'
     tinynas_detection = 'tinynas-detection'

modelscope/pipelines/cv/easycv_pipelines/detection_pipeline.py

@@ -15,6 +15,8 @@ from .base import EasyCVPipeline
 @PIPELINES.register_module(
     Tasks.image_object_detection,
     module_name=Pipelines.image_object_detection_auto)
+@PIPELINES.register_module(
+    Tasks.image_object_detection, module_name=Pipelines.hand_detection)
 class EasyCVDetectionPipeline(EasyCVPipeline):
     """Pipeline for easycv detection task."""
 

modelscope/utils/cv/image_utils.py

@@ -461,7 +461,7 @@ def show_image_object_detection_auto_result(img_path,
             lineType=8)
         cv2.putText(
             img,
-            label, (int((box[0] + box[2]) * 0.5), int(box[1])),
+            label, (int(box[0]), int(box[3])),
             1,
             1.0, (0, 255, 0),
             thickness=1,

tests/pipelines/test_hand_detection.py

@@ -0,0 +1,30 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import unittest
+
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from modelscope.utils.demo_utils import DemoCompatibilityCheck
+from modelscope.utils.test_utils import test_level
+
+
+class ObjectDetectionTest(unittest.TestCase, DemoCompatibilityCheck):
+
+    def setUp(self) -> None:
+        self.task = Tasks.image_object_detection
+        self.model_id = 'damo/cv_yolox-pai_hand-detection'
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_hand_detection_pipeline(self):
+        test_image = 'data/test/images/hand_detection.jpg'
+
+        hand_detection = pipeline(self.task, model=self.model_id)
+
+        result = hand_detection(test_image)
+        hand_detection.show_result(test_image, result,
+                                   'hand_detection_ret.jpg')
+
+        print(f'hand detection result={result}')
+
+
+if __name__ == '__main__':
+    unittest.main()

tests/trainers/easycv/test_easycv_trainer_hand_detection.py

@@ -0,0 +1,64 @@
+# Copyright (c) Alibaba, Inc. and its affiliates.
+import glob
+import os
+import shutil
+import tempfile
+import unittest
+
+import torch
+
+from modelscope.metainfo import Trainers
+from modelscope.msdatasets import MsDataset
+from modelscope.trainers import build_trainer
+from modelscope.utils.constant import DownloadMode, LogKeys, Tasks
+from modelscope.utils.logger import get_logger
+from modelscope.utils.test_utils import test_level
+
+
+class EasyCVTrainerTestHandDetection(unittest.TestCase):
+    model_id = 'damo/cv_yolox-pai_hand-detection'
+
+    def setUp(self):
+        self.logger = get_logger()
+        self.logger.info(('Testing %s.%s' %
+                          (type(self).__name__, self._testMethodName)))
+
+    def _train(self, tmp_dir):
+        cfg_options = {'train.max_epochs': 2}
+
+        trainer_name = Trainers.easycv
+
+        train_dataset = MsDataset.load(
+            dataset_name='hand_detection_dataset', split='subtrain')
+        eval_dataset = MsDataset.load(
+            dataset_name='hand_detection_dataset', split='subtrain')
+
+        kwargs = dict(
+            model=self.model_id,
+            train_dataset=train_dataset,
+            eval_dataset=eval_dataset,
+            work_dir=tmp_dir,
+            cfg_options=cfg_options)
+
+        trainer = build_trainer(trainer_name, kwargs)
+        trainer.train()
+
+    @unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
+    def test_trainer_single_gpu(self):
+        temp_file_dir = tempfile.TemporaryDirectory()
+        tmp_dir = temp_file_dir.name
+        if not os.path.exists(tmp_dir):
+            os.makedirs(tmp_dir)
+
+        self._train(tmp_dir)
+
+        results_files = os.listdir(tmp_dir)
+        json_files = glob.glob(os.path.join(tmp_dir, '*.log.json'))
+        self.assertEqual(len(json_files), 1)
+        self.assertIn(f'{LogKeys.EPOCH}_2.pth', results_files)
+
+        temp_file_dir.cleanup()
+
+
+if __name__ == '__main__':
+    unittest.main()