add PPHumanSeg from PaddleHub conversion (#5)

* add PPHumanSeg impl and demo

* add benchmark impl and results for PPHumanSeg

README.md

@@ -34,7 +34,7 @@ Hardware Setup:
 | [CRNN](./models/text_recognition_crnn)   | 100x32  | 50.21  | 234.32  |
 | [SFace](./models/face_recognition_sface) | 112x112 | 8.69 | 96.79 |
 | [PP-ResNet](./models/image_classification_ppresnet) | 224x224 | 56.05 | 602.58
-
+| [PP-HumanSeg](./models/human_segmentation_pphumanseg) | 192x192 | 19.92 | 105.32 |
 
 ## License
 

benchmark/config/human_segmentation_pphumanseg.yaml

@@ -0,0 +1,18 @@
+Benchmark:
+  name: "Human Segmentation Benchmark"
+  data:
+    path: "benchmark/data/human_segmentation"
+    files: ["messi5.jpg", "100040721_1.jpg", "detect.jpg"]
+    toRGB: True
+    resize: [192, 192]
+  metric:
+    warmup: 3
+    repeat: 10
+    batchSize: 1
+    reduction: 'median'
+  backend: "default"
+  target: "cpu"
+
+Model:
+  name: "PPHumanSeg"
+  modelPath: "models/human_segmentation_pphumanseg/human_segmentation_pphumanseg.onnx"

benchmark/download_data.py

@@ -177,6 +177,10 @@ data_downloaders = dict(
         url='https://drive.google.com/u/0/uc?id=1qcsrX3CIAGTooB-9fLKYwcvoCuMgjzGU&export=download',
         sha='987546f567f9f11d150eea78951024b55b015401',
         filename='image_classification.zip'),
+    human_segmentation=Downloader(name='human_segmentation',
+        url='https://drive.google.com/u/0/uc?id=1Kh0qXcAZCEaqwavbUZubhRwrn_8zY7IL&export=download',
+        sha='ac0eedfd8568570cad135acccd08a134257314d0',
+        filename='human_segmentation.zip')
 )
 
 if __name__ == '__main__':

models/__init__.py

@@ -3,6 +3,7 @@ from .text_detection_db.db import DB
 from .text_recognition_crnn.crnn import CRNN
 from .face_recognition_sface.sface import SFace
 from .image_classification_ppresnet.ppresnet import PPResNet
+from .human_segmentation_pphumanseg.pphumanseg import PPHumanSeg
 
 class Registery:
     def __init__(self, name):
@@ -21,3 +22,4 @@ MODELS.register(DB)
 MODELS.register(CRNN)
 MODELS.register(SFace)
 MODELS.register(PPResNet)
+MODELS.register(PPHumanSeg)

models/human_segmentation_pphumanseg/LICENSE

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

@@ -0,0 +1,23 @@
+# PPHumanSeg
+
+This model is ported from [PaddleHub](https://github.com/PaddlePaddle/PaddleHub) using [this script from OpenCV](https://github.com/opencv/opencv/blob/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle/paddle_humanseg.py).
+
+## 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://arxiv.org/abs/1512.03385
+- https://github.com/opencv/opencv/tree/master/samples/dnn/dnn_model_runner/dnn_conversion/paddlepaddle
+- https://github.com/PaddlePaddle/PaddleHub

models/human_segmentation_pphumanseg/demo.py

@@ -0,0 +1,141 @@
+# 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 argparse
+
+import numpy as np
+import cv2 as cv
+
+from pphumanseg import PPHumanSeg
+
+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='PPHumanSeg (https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.2/contrib/PP-HumanSeg)')
+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='human_segmentation_pphumanseg.onnx', help='Path to the model.')
+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 get_color_map_list(num_classes):
+    """
+    Returns the color map for visualizing the segmentation mask,
+    which can support arbitrary number of classes.
+
+    Args:
+        num_classes (int): Number of classes.
+
+    Returns:
+        (list). The color map.
+    """
+
+    num_classes += 1
+    color_map = num_classes * [0, 0, 0]
+    for i in range(0, num_classes):
+        j = 0
+        lab = i
+        while lab:
+            color_map[i * 3] |= (((lab >> 0) & 1) << (7 - j))
+            color_map[i * 3 + 1] |= (((lab >> 1) & 1) << (7 - j))
+            color_map[i * 3 + 2] |= (((lab >> 2) & 1) << (7 - j))
+            j += 1
+            lab >>= 3
+    color_map = color_map[3:]
+    return color_map
+
+def visualize(image, result, weight=0.6, fps=None):
+    """
+    Convert predict result to color image, and save added image.
+
+    Args:
+        image (str): The input image.
+        result (np.ndarray): The predict result of image.
+        weight (float): The image weight of visual image, and the result weight is (1 - weight). Default: 0.6
+        fps (str): The FPS to be drawn on the input image.
+
+    Returns:
+        vis_result (np.ndarray): The visualized result.
+    """
+    color_map = get_color_map_list(256)
+    color_map = [color_map[i:i + 3] for i in range(0, len(color_map), 3)]
+    color_map = np.array(color_map).astype(np.uint8)
+    # Use OpenCV LUT for color mapping
+    c1 = cv.LUT(result, color_map[:, 0])
+    c2 = cv.LUT(result, color_map[:, 1])
+    c3 = cv.LUT(result, color_map[:, 2])
+    pseudo_img = np.dstack((c1, c2, c3))
+
+    vis_result = cv.addWeighted(image, weight, pseudo_img, 1 - weight, 0)
+
+    if fps is not None:
+        cv.putText(vis_result, 'FPS: {:.2f}'.format(fps), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
+
+    return vis_result
+
+
+if __name__ == '__main__':
+    # Instantiate PPHumanSeg
+    model = PPHumanSeg(modelPath=args.model)
+
+    if args.input is not None:
+        # Read image and resize to 192x192
+        image = cv.imread(args.input)
+        h, w, _ = image.shape
+        image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
+        _image = cv.resize(image, dsize=(192, 192))
+
+        # Inference
+        result = model.infer(_image)
+        result = cv.resize(result[0, :, :], dsize=(w, h), interpolation=cv.INTER_NEAREST)
+
+        # Draw results on the input image
+        image = visualize(image, result)
+
+        # Save results if save is true
+        if args.save:
+            print('Results saved to result.jpg\n')
+            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))
+
+        tm = cv.TickMeter()
+        while cv.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            _frame = cv.cvtColor(frame, cv.COLOR_BGR2RGB)
+            _frame = cv.resize(_frame, dsize=(192, 192))
+
+            # Inference
+            tm.start()
+            result = model.infer(_frame)
+            tm.stop()
+            result = cv.resize(result[0, :, :], dsize=(w, h), interpolation=cv.INTER_NEAREST)
+
+            # Draw results on the input image
+            frame = visualize(frame, result, fps=tm.getFPS())
+
+            # Visualize results in a new window
+            cv.imshow('PPHumanSeg Demo', frame)
+
+            tm.reset()

models/human_segmentation_pphumanseg/pphumanseg.py

@@ -0,0 +1,55 @@
+# 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 PPHumanSeg:
+    def __init__(self, modelPath):
+        self._modelPath = modelPath
+        self._model = cv.dnn.readNet(self._modelPath)
+
+        self._inputNames = ''
+        self._outputNames = ['save_infer_model/scale_0.tmp_1']
+        self._inputSize = [192, 192]
+        self._mean = np.array([0.5, 0.5, 0.5])[np.newaxis, np.newaxis, :]
+        self._std = np.array([0.5, 0.5, 0.5])[np.newaxis, np.newaxis, :]
+
+    @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.astype(np.float32, copy=False) / 255.0
+        image -= self._mean
+        image /= self._std
+        return cv.dnn.blobFromImage(image)
+
+    def infer(self, image):
+        assert image.shape[0] == self._inputSize[1], '{} (height of input image) != {} (preset height)'.format(image.shape[0], self._inputSize[1])
+        assert image.shape[1] == self._inputSize[0], '{} (width of input image) != {} (preset width)'.format(image.shape[1], self._inputSize[0])
+
+        # Preprocess
+        inputBlob = self._preprocess(image)
+
+        # Forward
+        self._model.setInput(inputBlob, self._inputNames)
+        outputBlob = self._model.forward(self._outputNames)
+
+        # Postprocess
+        results = self._postprocess(outputBlob)
+
+        return results
+
+    def _postprocess(self, outputBlob):
+        result = np.argmax(outputBlob[0], axis=1).astype(np.uint8)
+        return result