Add LPD_YuNet for License Plate Detection (#56)

* add lpd_yunet

* add quantization and quantized model

* quantize with inc instead

* update benchmark results

README.md

@@ -18,6 +18,7 @@ Guidelines:
 |-------|------------|----------------|--------------|-----------------|--------------|-------------|
 | [YuNet](./models/face_detection_yunet)                | 160x120  | 1.45   | 6.22    | 12.18  | 4.04   | 86.69 |
 | [SFace](./models/face_recognition_sface)              | 112x112  | 8.65   | 99.20   | 24.88  | 46.25  | ---   |
+| [LPD-YuNet](./models/license_plate_detection_yunet/)  | 320x240  | ---    | 168.03  | 56.12  | 154.20\* |       |
 | [DB-IC15](./models/text_detection_db)                 | 640x480  | 142.91 | 2835.91 | 208.41 | ---    | ---   |
 | [DB-TD500](./models/text_detection_db)                | 640x480  | 142.91 | 2841.71 | 210.51 | ---    | ---   |
 | [CRNN-EN](./models/text_recognition_crnn)             | 100x32   | 50.21  | 234.32  | 196.15 | 125.30 | ---   |

benchmark/config/license_plate_detection_yunet.yaml

@@ -0,0 +1,22 @@
+Benchmark:
+  name: "License Plate Detection Benchmark"
+  type: "Detection"
+  data:
+    path: "benchmark/data/license_plate_detection"
+    files: ["1.jpg", "2.jpg", "3.jpg", "4.jpg"]
+    sizes: # [[w1, h1], ...], Omit to run at original scale
+      - [320, 240]
+  metric:
+    warmup: 30
+    repeat: 10
+    reduction: "median"
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "LPD_YuNet"
+  modelPath: "models/license_plate_detection_yunet/license_plate_detection_lpd_yunet_2022may.onnx"
+  confThreshold: 0.8
+  nmsThreshold: 0.3
+  topK: 5000
+  keepTopK: 750

benchmark/download_data.py

@@ -201,6 +201,10 @@ data_downloaders = dict(
         url='https://drive.google.com/u/0/uc?id=1qScOzehV8OIzJJLuD_LMvZq15YcWd_VV&export=download',
         sha='c0d4f811d38c6f833364b9196a719307598213a1',
         filename='palm_detection.zip'),
+    license_plate_detection=Downloader(name='license_plate_detection',
+        url='https://drive.google.com/u/0/uc?id=1cf9MEyUqMMy8lLeDGd1any6tM_SsSmny&export=download',
+        sha='997acb143ddc4531e6e41365fb7ad4722064564c',
+        filename='license_plate_detection.zip'),
 )
 
 if __name__ == '__main__':

models/__init__.py

@@ -10,6 +10,7 @@ from .person_reid_youtureid.youtureid import YoutuReID
 from .image_classification_mobilenet.mobilenet_v1 import MobileNetV1
 from .image_classification_mobilenet.mobilenet_v2 import MobileNetV2
 from .palm_detection_mediapipe.mp_palmdet import MPPalmDet
+from .license_plate_detection_yunet.lpd_yunet import LPD_YuNet
 
 class Registery:
     def __init__(self, name):
@@ -35,4 +36,4 @@ MODELS.register(YoutuReID)
 MODELS.register(MobileNetV1)
 MODELS.register(MobileNetV2)
 MODELS.register(MPPalmDet)
-
+MODELS.register(LPD_YuNet)

models/license_plate_detection_yunet/LICENSE

@@ -0,0 +1,203 @@
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models/license_plate_detection_yunet/README.md

@@ -0,0 +1,22 @@
+# License Plate Detection with YuNet
+
+This model is contributed by Dong Xu (徐栋) from [watrix.ai](watrix.ai) (银河水滴).
+
+Please note that the model is trained with Chinese license plates, so the detection results of other license plates with this model may be limited.
+
+## Demo
+
+Run the following command to try the demo:
+```shell
+# detect on camera input
+python demo.py
+# detect on an image
+python demo.py --input /path/to/image
+```
+
+## License
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE)
+
+## Reference
+
+ - https://github.com/ShiqiYu/libfacedetection.train

models/license_plate_detection_yunet/demo.py

@@ -0,0 +1,120 @@
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from lpd_yunet import LPD_YuNet
+
+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
+
+backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
+targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
+help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
+help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
+try:
+    backends += [cv.dnn.DNN_BACKEND_TIMVX]
+    targets += [cv.dnn.DNN_TARGET_NPU]
+    help_msg_backends += "; {:d}: TIMVX"
+    help_msg_targets += "; {:d}: NPU"
+except:
+    print('This version of OpenCV does not support TIM-VX and NPU. Visit https://gist.github.com/fengyuentau/5a7a5ba36328f2b763aea026c43fa45f for more information.')
+
+parser = argparse.ArgumentParser(description='LPD-YuNet for License Plate Detection')
+parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
+parser.add_argument('--model', '-m', type=str, default='license_plate_detection_lpd_yunet_2022may.onnx', help='Path to the model.')
+parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
+parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
+parser.add_argument('--conf_threshold', type=float, default=0.9, help='Filter out faces of confidence < conf_threshold.')
+parser.add_argument('--nms_threshold', type=float, default=0.3, help='Suppress bounding boxes of iou >= nms_threshold.')
+parser.add_argument('--top_k', type=int, default=5000, help='Keep top_k bounding boxes before NMS.')
+parser.add_argument('--keep_top_k', type=int, default=750, help='Keep keep_top_k bounding boxes after NMS.')
+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 visualize(image, dets, line_color=(0, 255, 0), text_color=(0, 0, 255), fps=None):
+    output = image.copy()
+
+    if fps is not None:
+        cv.putText(output, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, text_color)
+
+    for det in dets:
+        bbox = det[:-1].astype(np.int32)
+        x1, y1, x2, y2, x3, y3, x4, y4 = bbox
+
+        # Draw the border of license plate
+        cv.line(output, (x1, y1), (x2, y2), line_color, 2)
+        cv.line(output, (x2, y2), (x3, y3), line_color, 2)
+        cv.line(output, (x3, y3), (x4, y4), line_color, 2)
+        cv.line(output, (x4, y4), (x1, y1), line_color, 2)
+
+    return output
+
+if __name__ == '__main__':
+    # Instantiate LPD-YuNet
+    model = LPD_YuNet(modelPath=args.model,
+                      confThreshold=args.conf_threshold,
+                      nmsThreshold=args.nms_threshold,
+                      topK=args.top_k,
+                      keepTopK=args.keep_top_k,
+                      backendId=args.backend,
+                      targetId=args.target)
+
+    # If input is an image
+    if args.input is not None:
+        image = cv.imread(args.input)
+        h, w, _ = image.shape
+
+        # Inference
+        model.setInputSize([w, h])
+        results = model.infer(image)
+
+        # Print results
+        print('{} license plates detected.'.format(results.shape[0]))
+
+        # Draw results on the input image
+        image = visualize(image, results)
+
+        # Save results if save is true
+        if args.save:
+            print('Resutls saved to result.jpg')
+            cv.imwrite('result.jpg', image)
+
+        # Visualize results in a new window
+        if args.vis:
+            cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
+            cv.imshow(args.input, image)
+            cv.waitKey(0)
+    else: # Omit input to call default camera
+        deviceId = 0
+        cap = cv.VideoCapture(deviceId)
+        w = int(cap.get(cv.CAP_PROP_FRAME_WIDTH))
+        h = int(cap.get(cv.CAP_PROP_FRAME_HEIGHT))
+        model.setInputSize([w, h])
+
+        tm = cv.TickMeter()
+        while cv.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            # Inference
+            tm.start()
+            results = model.infer(frame) # results is a tuple
+            tm.stop()
+
+            # Draw results on the input image
+            frame = visualize(frame, results, fps=tm.getFPS())
+
+            # Visualize results in a new Window
+            cv.imshow('LPD-YuNet Demo', frame)
+
+            tm.reset()
+

models/license_plate_detection_yunet/lpd_yunet.py

@@ -0,0 +1,135 @@
+from itertools import product
+
+import numpy as np
+import cv2 as cv
+
+class LPD_YuNet:
+    def __init__(self, modelPath, inputSize=[320, 240], confThreshold=0.8, nmsThreshold=0.3, topK=5000, keepTopK=750, backendId=0, targetId=0):
+        self.model_path = modelPath
+        self.input_size = np.array(inputSize)
+        self.confidence_threshold=confThreshold
+        self.nms_threshold = nmsThreshold
+        self.top_k = topK
+        self.keep_top_k = keepTopK
+        self.backend_id = backendId
+        self.target_id = targetId
+
+        self.output_names = ['loc', 'conf', 'iou']
+        self.min_sizes = [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]]
+        self.steps = [8, 16, 32, 64]
+        self.variance = [0.1, 0.2]
+
+        # load model
+        self.model = cv.dnn.readNet(self.model_path)
+        # generate anchors/priorboxes
+        self._priorGen()
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackend(self, backendId):
+        self.backend_id = backendId
+        self.model.setPreferableBackend(self.backend_id)
+
+    def setTarget(self, targetId):
+        self.target_id = targetId
+        self.model.setPreferableTarget(self.target_id)
+
+    def setInputSize(self, inputSize):
+        self.input_size = inputSize
+        # re-generate anchors/priorboxes
+        self._priorGen()
+
+    def _preprocess(self, image):
+        return cv.dnn.blobFromImage(image)
+
+    def infer(self, image):
+        assert image.shape[0] == self.input_size[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self.input_size[1])
+        assert image.shape[1] == self.input_size[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self.input_size[0])
+
+        # Preprocess
+        inputBlob = self._preprocess(image)
+
+        # Forward
+        self.model.setInput(inputBlob)
+        outputBlob = self.model.forward(self.output_names)
+
+        # Postprocess
+        results = self._postprocess(outputBlob)
+
+        return results
+
+    def _postprocess(self, blob):
+        # Decode
+        dets = self._decode(blob)
+
+        # NMS
+        keepIdx = cv.dnn.NMSBoxes(
+            bboxes=dets[:, 0:4].tolist(),
+            scores=dets[:, -1].tolist(),
+            score_threshold=self.confidence_threshold,
+            nms_threshold=self.nms_threshold,
+            top_k=self.top_k
+        ) # box_num x class_num
+        if len(keepIdx) > 0:
+            dets = dets[keepIdx]
+            return dets[:self.keep_top_k]
+        else:
+            return np.empty(shape=(0, 9))
+
+    def _priorGen(self):
+        w, h = self.input_size
+        feature_map_2th = [int(int((h + 1) / 2) / 2),
+                           int(int((w + 1) / 2) / 2)]
+        feature_map_3th = [int(feature_map_2th[0] / 2),
+                           int(feature_map_2th[1] / 2)]
+        feature_map_4th = [int(feature_map_3th[0] / 2),
+                           int(feature_map_3th[1] / 2)]
+        feature_map_5th = [int(feature_map_4th[0] / 2),
+                           int(feature_map_4th[1] / 2)]
+        feature_map_6th = [int(feature_map_5th[0] / 2),
+                           int(feature_map_5th[1] / 2)]
+
+        feature_maps = [feature_map_3th, feature_map_4th,
+                        feature_map_5th, feature_map_6th]
+
+        priors = []
+        for k, f in enumerate(feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])): # i->h, j->w
+                for min_size in min_sizes:
+                    s_kx = min_size / w
+                    s_ky = min_size / h
+
+                    cx = (j + 0.5) * self.steps[k] / w
+                    cy = (i + 0.5) * self.steps[k] / h
+
+                    priors.append([cx, cy, s_kx, s_ky])
+        self.priors = np.array(priors, dtype=np.float32)
+
+    def _decode(self, blob):
+        loc, conf, iou = blob
+        # get score
+        cls_scores = conf[:, 1]
+        iou_scores = iou[:, 0]
+        # clamp
+        _idx = np.where(iou_scores < 0.)
+        iou_scores[_idx] = 0.
+        _idx = np.where(iou_scores > 1.)
+        iou_scores[_idx] = 1.
+        scores = np.sqrt(cls_scores * iou_scores)
+        scores = scores[:, np.newaxis]
+
+        scale = self.input_size
+
+        # get four corner points for bounding box
+        bboxes = np.hstack((
+            (self.priors[:, 0:2] + loc[:,  4: 6] * self.variance[0] * self.priors[:, 2:4]) * scale,
+            (self.priors[:, 0:2] + loc[:,  6: 8] * self.variance[0] * self.priors[:, 2:4]) * scale,
+            (self.priors[:, 0:2] + loc[:, 10:12] * self.variance[0] * self.priors[:, 2:4]) * scale,
+            (self.priors[:, 0:2] + loc[:, 12:14] * self.variance[0] * self.priors[:, 2:4]) * scale
+        ))
+
+        dets = np.hstack((bboxes, scores))
+        return dets

tools/quantize/inc_configs/lpd_yunet.yaml

@@ -0,0 +1,40 @@
+#
+# Copyright (c) 2021 Intel Corporation
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+version: 1.0
+
+model:                                               # mandatory. used to specify model specific information.
+  name: lpd_yunet
+  framework: onnxrt_qlinearops                       # mandatory. supported values are tensorflow, pytorch, pytorch_ipex, onnxrt_integer, onnxrt_qlinear or mxnet; allow new framework backend extension.
+
+quantization:                                        # optional. tuning constraints on model-wise for advance user to reduce tuning space.
+  approach: post_training_static_quant               # optional. default value is post_training_static_quant.                          
+  calibration:
+    dataloader:
+      batch_size: 1
+      dataset:
+        dummy:
+          shape: [1, 3, 240, 320]
+          low: 0.0
+          high: 127.0
+          dtype: float32
+          label: True
+
+tuning:
+  accuracy_criterion:
+    relative:  0.02                                  # optional. default value is relative, other value is absolute. this example allows relative accuracy loss: 1%.
+  exit_policy:
+    timeout: 0                                       # optional. tuning timeout (seconds). default value is 0 which means early stop. combine with max_trials field to decide when to exit.
+  random_seed: 9527                                  # optional. random seed for deterministic tuning.

tools/quantize/quantize-inc.py

@@ -1,10 +1,10 @@
 import os
 import sys
-import numpy as ny
+import numpy as np
 import cv2 as cv
 
 import onnx
-from neural_compressor.experimental import Quantization, common as nc_Quantization, nc_common
+from neural_compressor.experimental import Quantization, common
 
 class Quantize:
     def __init__(self, model_path, config_path, custom_dataset=None):
@@ -20,7 +20,7 @@ class Quantize:
         output_name = '{}-int8-quantized.onnx'.format(self.model_path[:-5])
 
         model = onnx.load(self.model_path)
-        quantizer = nc_Quantization(self.config_path)
+        quantizer = Quantization(self.config_path)
         if self.custom_dataset is not None:
             quantizer.calib_dataloader = common.DataLoader(self.custom_dataset)
         quantizer.model = common.Model(model)
@@ -28,8 +28,11 @@ class Quantize:
         q_model.save(output_name)
 
 class Dataset:
-    def __init__(self, root):
+    def __init__(self, root, size=None, toTensor=False):
         self.root = root
+        self.size = size
+        self.toTensor = toTensor
+
         self.image_list = self.load_image_list(self.root)
 
     def load_image_list(self, path):
@@ -37,11 +40,16 @@ class Dataset:
         for f in os.listdir(path):
             if not f.endswith('.jpg'):
                 continue
-            image_list.append(f)
+            image_list.append(os.path.join(path, f))
         return image_list
 
     def __getitem__(self, idx):
         img = cv.imread(self.image_list[idx])
+        if self.size:
+            img = cv.resize(img, dsize=self.size)
+        if self.toTensor:
+            img = img.transpose(2, 0, 1) # hwc -> chw
+            img = img.astype(np.float32)
         return img, 1
 
     def __len__(self):
@@ -54,7 +62,10 @@ models=dict(
                              config_path='./inc_configs/mobilenet.yaml'),
     mppalm_det=Quantize(model_path='../../models/palm_detection_mediapipe/palm_detection_mediapipe_2022may.onnx',
                              config_path='./inc_configs/mppalmdet.yaml',
-                             custom_dataset=Dataset(root='../../benchmark/data/palm_detection'))
+                             custom_dataset=Dataset(root='../../benchmark/data/palm_detection')),
+    lpd_yunet=Quantize(model_path='../../models/license_plate_detection_yunet/license_plate_detection_lpd_yunet_2022may.onnx',
+                       config_path='./inc_configs/lpd_yunet.yaml',
+                       custom_dataset=Dataset(root='../../benchmark/data/license_plate_detection', size=(320, 240), toTensor=True)),
 )
 
 if __name__ == '__main__':