Text Detection: add ppocr-v2 detect -WIP

benchmark/config/text_detection_ppdetect.yaml

@@ -0,0 +1,18 @@
+Benchmark:
+  name: "Text Detection Benchmark"
+  type: "Detection"
+  data:
+    path: "benchmark/data/text"
+    files: ["1.jpg", "2.jpg", "3.jpg"]
+    sizes: # [[w1, h1], ...], Omit to run at original scale
+      - [640, 480]
+  metric:
+    warmup: 30
+    repeat: 10
+    reduction: "median"
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "PPDetect"
+  modelPath: "models/text_detection_ppdetect/text_detection_ppdetect_2022_June.onnx"

models/__init__.py

@@ -11,7 +11,7 @@
 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
-
+from .text_detection_ppdetect.ppdetect import PPDetect
 class Registery:
     def __init__(self, name):
         self._name = name
@@ -37,3 +37,4 @@ def register(self, item):
 MODELS.register(MobileNetV2)
 MODELS.register(MPPalmDet)
 MODELS.register(LPD_YuNet)
+MODELS.register(PPDetect)

models/text_detection_ppdetect/LICENSE

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

@@ -0,0 +1,31 @@
+# PP-OCRv2 detect
+
+Real-time Scene Text Detection with Differentiable Binarization
+
+This model is ported from [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR).
+
+## 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
+```
+### Example outputs
+
+![cola](./examples/cola.jpg)
+
+![book](./examples/book.jpg)
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+## Reference
+
+- https://arxiv.org/abs/1911.08947
+- https://github.com/PaddlePaddle/PaddleOCR
+- https://github.com/BADBADBADBOY/DBnet-lite.pytorch
+- https://docs.opencv.org/master/d4/d43/tutorial_dnn_text_spotting.html

models/text_detection_ppdetect/demo.py

@@ -0,0 +1,123 @@
+# This file is part of OpenCV Zoo project.
+# It is subject to the license terms in the LICENSE file found in the same directory.
+#
+import argparse
+
+import numpy as np
+import cv2 as cv
+
+from ppdetect import PPDetect
+
+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='Real-time Scene Text Detection with Differentiable Binarization (https://arxiv.org/abs/1911.08947).')
+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='text_detection_ppdetect_2022_June.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('--width', type=int, default=736,
+                    help='Preprocess input image by resizing to a specific width. It should be multiple by 32.')
+parser.add_argument('--height', type=int, default=736,
+                    help='Preprocess input image by resizing to a specific height. It should be multiple by 32.')
+parser.add_argument('--binary_threshold', type=float, default=0.3, help='Threshold of the binary map.')
+parser.add_argument('--box_threshold', type=float, default=0.5, help='Threshold of the box.')
+parser.add_argument('--is_poly', type=str2bool, default=False, help='Set true for polygons.')
+parser.add_argument('--max_candidates', type=int, default=200, help='Max candidates of polygons.')
+parser.add_argument('--unclip_ratio', type=np.float64, default=2.0, help=' The unclip ratio of the detected text region, which determines the output size.')
+parser.add_argument('--save', '-s', type=str, 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, results, box_color=(0, 255, 0), text_color=(0, 0, 255), isClosed=True, thickness=2, 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)
+
+    pts = np.array(results[0])
+    output = cv.polylines(output, pts, isClosed, box_color, thickness)
+
+    return output
+
+if __name__ == '__main__':
+    # Instantiate DB
+    model = PPDetect(modelPath=args.model,
+                     binaryThreshold=args.binary_threshold,
+                     boxThresh=args.box_threshold,
+                     isPoly=args.is_poly,
+                     maxCandidates=args.max_candidates,
+                     unclipRatio=args.unclip_ratio,
+                     backendId=args.backend,
+                     targetId=args.target
+                     )
+
+    # If input is an image
+    if args.input is not None:
+        image = cv.imread(args.input)
+        image = cv.resize(image, [args.width, args.height])
+
+        # Inference
+        print(image.shape)
+        results = model.infer(image)
+
+        # Print results`
+        print('{} texts detected.'.format(len(results[0])))
+        for idx, (bbox, score) in enumerate(zip(results[0], results[1])):
+            print('{}: {} {} {} {}, {:.2f}'.format(idx, bbox[0], bbox[1], bbox[2], bbox[3], score))
+
+        # Draw results on the input image
+        image = visualize(image, results)
+
+        # Save results if save is true
+        if args.save:
+            print('Results saved to result.jpg\n')
+            cv.imwrite('results/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)
+
+        tm = cv.TickMeter()
+        while cv.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            frame = cv.resize(frame, [args.width, args.height])
+            # 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('{} Demo'.format(model.name), frame)
+
+            tm.reset()
+