Add YoutuReID for person ReID (#24)

* workable wrapper and demo (demo without visualization)

* add visualization for demo

* impl benchmark for YoutuReID

* update benchmark results on x86_64, arm and cuda

README.md

@@ -26,6 +26,7 @@ Guidelines:
 | [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 | 67.97 |
 | [WeChatQRCode](./models/qrcode_wechatqrcode) | 100x100 | 7.04 | 37.68 | --- |
 | [DaSiamRPN](./models/object_tracking_dasiamrpn) | 1280x720 | 36.15 | 705.48 | 76.82 |
+| [YoutuReID](./models/person_reid_youtureid) | 128x256 | 35.81 | 521.98 | 90.07 |
 
 Hardware Setup:
 - `CPU x86_64`: INTEL CPU i7-5930K @ 3.50GHz, 6 cores, 12 threads.

benchmark/config/person_reid_youtureid.yaml

@@ -0,0 +1,17 @@
+Benchmark:
+  name: "Person ReID Benchmark"
+  type: "Base"
+  data:
+    path: "benchmark/data/person_reid"
+    files: ["0030_c1_f0056923.jpg", "0042_c5_f0068994.jpg", "0056_c8_f0017063.jpg"]
+    sizes: [[128, 256]]
+  metric:
+    warmup: 30
+    repeat: 10
+    reduction: "median"
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "YoutuReID"
+  modelPath: "models/person_reid_youtureid/person_reid_youtu_2021nov.onnx"

benchmark/download_data.py

@@ -188,7 +188,11 @@ data_downloaders = dict(
     object_tracking=Downloader(name='object_tracking',
         url='https://drive.google.com/u/0/uc?id=1_cw5pUmTF-XmQVcQAI8fIp-Ewi2oMYIn&export=download',
         sha='0bdb042632a245270013713bc48ad35e9221f3bb',
-        filename='object_tracking.zip')
+        filename='object_tracking.zip'),
+    person_reid=Downloader(name='person_reid',
+        url='https://drive.google.com/u/0/uc?id=1G8FkfVo5qcuyMkjSs4EA6J5e16SWDGI2&export=download',
+        sha='5b741fbf34c1fbcf59cad8f2a65327a5899e66f1',
+        filename='person_reid')
 )
 
 if __name__ == '__main__':

models/__init__.py

@@ -6,6 +6,7 @@ from .image_classification_ppresnet.ppresnet import PPResNet
 from .human_segmentation_pphumanseg.pphumanseg import PPHumanSeg
 from .qrcode_wechatqrcode.wechatqrcode import WeChatQRCode
 from .object_tracking_dasiamrpn.dasiamrpn import DaSiamRPN
+from .person_reid_youtureid.youtureid import YoutuReID
 
 class Registery:
     def __init__(self, name):
@@ -26,4 +27,5 @@ MODELS.register(SFace)
 MODELS.register(PPResNet)
 MODELS.register(PPHumanSeg)
 MODELS.register(WeChatQRCode)
-MODELS.register(DaSiamRPN)
+MODELS.register(DaSiamRPN)
+MODELS.register(YoutuReID)

models/person_reid_youtureid/LICENSE

@@ -0,0 +1,202 @@
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models/person_reid_youtureid/README.md

@@ -0,0 +1,22 @@
+# Youtu ReID Baseline
+
+This model is provided by Tencent Youtu Lab [[Credits]](https://github.com/opencv/opencv/blob/394e640909d5d8edf9c1f578f8216d513373698c/samples/dnn/person_reid.py#L6-L11).
+
+Note:
+- Model source: https://github.com/ReID-Team/ReID_extra_testdata
+
+## Demo
+
+Run the following command to try the demo:
+```shell
+python demo.py --input1 /path/to/person1 --input2 /path/to/person2
+```
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+## Reference:
+
+- OpenCV DNN Sample: https://github.com/opencv/opencv/blob/4.x/samples/dnn/person_reid.py
+- Model source: https://github.com/ReID-Team/ReID_extra_testdata

models/person_reid_youtureid/demo.py

@@ -0,0 +1,100 @@
+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+import os
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from youtureid import YoutuReID
+
+def str2bool(v):
+    if v.lower() in ['on', 'yes', 'true', 'y', 't']:
+        return True
+    elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
+        return False
+    else:
+        raise NotImplementedError
+
+parser = argparse.ArgumentParser(
+    description="ReID baseline models from Tencent Youtu Lab")
+parser.add_argument('--query_dir', '-q', type=str, help='Query directory.')
+parser.add_argument('--gallery_dir', '-g', type=str, help='Gallery directory.')
+parser.add_argument('--topk', type=int, default=10, help='Top-K closest from gallery for each query.')
+parser.add_argument('--model', '-m', type=str, default='person_reid_youtu_2021nov.onnx', help='Path to the model.')
+parser.add_argument('--save', '-s', type=str2bool, default=False, help='Set true to save results. This flag is invalid when using camera.')
+parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
+args = parser.parse_args()
+
+def readImageFromDirectory(img_dir, w=128, h=256):
+    img_list = []
+    file_list = os.listdir(img_dir)
+    for f in file_list:
+        img = cv.imread(os.path.join(img_dir, f))
+        img = cv.resize(img, (w, h))
+        img_list.append(img)
+    return img_list, file_list
+
+def visualize(results, query_dir, gallery_dir, output_size=(128, 384)):
+    def addBorder(img, color, borderSize=5):
+        border = cv.copyMakeBorder(img, top=borderSize, bottom=borderSize, left=borderSize, right=borderSize, borderType=cv.BORDER_CONSTANT, value=color)
+        return border
+
+    results_vis = dict.fromkeys(results.keys(), None)
+    for f, topk_f in results.items():
+        query_img = cv.imread(os.path.join(query_dir, f))
+        query_img = cv.resize(query_img, output_size)
+        query_img = addBorder(query_img, [0, 0, 0])
+        cv.putText(query_img, 'Query', (10, 30), cv.FONT_HERSHEY_COMPLEX, 1., (0, 255, 0), 2)
+
+        gallery_img_list = []
+        for idx, gallery_f in enumerate(topk_f):
+            gallery_img = cv.imread(os.path.join(gallery_dir, gallery_f))
+            gallery_img = cv.resize(gallery_img, output_size)
+            gallery_img = addBorder(gallery_img, [255, 255, 255])
+            cv.putText(gallery_img, 'G{:02d}'.format(idx), (10, 30), cv.FONT_HERSHEY_COMPLEX, 1., (0, 255, 0), 2)
+            gallery_img_list.append(gallery_img)
+
+        results_vis[f] = np.concatenate([query_img] + gallery_img_list, axis=1)
+
+    return results_vis
+
+if __name__ == '__main__':
+    # Instantiate YoutuReID for person ReID
+    net = YoutuReID(modelPath=args.model)
+
+    # Read images from dir
+    query_img_list, query_file_list = readImageFromDirectory(args.query_dir)
+    gallery_img_list, gallery_file_list = readImageFromDirectory(args.gallery_dir)
+
+    # Query
+    topk_indices = net.query(query_img_list, gallery_img_list, args.topk)
+
+    # Index to filename
+    results = dict.fromkeys(query_file_list, None)
+    for f, indices in zip(query_file_list, topk_indices):
+        topk_matches = []
+        for idx in indices:
+            topk_matches.append(gallery_file_list[idx])
+        results[f] = topk_matches
+        # Print
+        print('Query: {}'.format(f))
+        print('\tTop-{} from gallery: {}'.format(args.topk, str(topk_matches)))
+
+    # Visualize
+    results_vis = visualize(results, args.query_dir, args.gallery_dir)
+
+    if args.save:
+        for f, img in results_vis.items():
+            cv.imwrite('result-{}'.format(f), img)
+
+    if args.vis:
+        for f, img in results_vis.items():
+            cv.namedWindow('result-{}'.format(f), cv.WINDOW_AUTOSIZE)
+            cv.imshow('result-{}'.format(f), img)
+            cv.waitKey(0)
+            cv.destroyAllWindows()

models/person_reid_youtureid/youtureid.py

@@ -0,0 +1,60 @@
+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
+# Third party copyrights are property of their respective owners.
+
+import numpy as np
+import cv2 as cv
+
+class YoutuReID:
+    def __init__(self, modelPath):
+        self._model = cv.dnn.readNet(modelPath)
+        self._input_size = (128, 256) # fixed
+        self._output_dim = 768
+        self._mean = (0.485, 0.456, 0.406)
+        self._std = (0.229, 0.224, 0.225)
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackend(self, backend_id):
+        self._model.setPreferableBackend(backend_id)
+
+    def setTarget(self, target_id):
+        self._model.setPreferableTarget(target_id)
+
+    def _preprocess(self, image):
+        image = image[:, :, ::-1]
+        image = (image / 255.0 - self._mean) / self._std
+        return cv.dnn.blobFromImage(image.astype(np.float32))
+        # return cv.dnn.blobFromImage(image, scalefactor=(1.0/255.0), size=self._input_size, mean=self._mean) / self._std
+
+    def infer(self, image):
+        # Preprocess
+        inputBlob = self._preprocess(image)
+
+        # Forward
+        self._model.setInput(inputBlob)
+        features = self._model.forward()
+        return np.reshape(features, (features.shape[0], features.shape[1]))
+
+    def query(self, query_img_list, gallery_img_list, topK=5):
+        query_features_list = []
+        for q in query_img_list:
+            query_features_list.append(self.infer(q))
+        query_features = np.concatenate(query_features_list, axis=0)
+        query_norm = np.linalg.norm(query_features, ord=2, axis=1, keepdims=True)
+        query_arr = query_features / (query_norm + np.finfo(np.float32).eps)
+
+        gallery_features_list = []
+        for g in gallery_img_list:
+            gallery_features_list.append(self.infer(g))
+        gallery_features = np.concatenate(gallery_features_list, axis=0)
+        gallery_norm = np.linalg.norm(gallery_features, ord=2, axis=1, keepdims=True)
+        gallery_arr = gallery_features / (gallery_norm + np.finfo(np.float32).eps)
+
+        dist = np.matmul(query_arr, gallery_arr.T)
+        idx = np.argsort(-dist, axis=1)
+        return [i[0:topK] for i in idx]