add sdk python demo (#554)

* check in python demos

* check in text detector python demo

* check in roatated object python demo

* check in pose python demo

* ignore the output class number when testing metrics with sdk as a backend

* fix object_detection

* rollback segmentation_model and python/segmentor.cpp

.gitignore

@@ -146,3 +146,5 @@ bin/
 
 # ncnn
 mmdeploy/backend/ncnn/onnx2ncnn
+
+/mmdeploy-*

demo/csrc/image_classification.cpp

@@ -35,8 +35,10 @@ int main(int argc, char *argv[]) {
     fprintf(stderr, "failed to apply classifier, code: %d\n", (int)status);
     return 1;
   }
-
-  fprintf(stderr, "label: %d, score: %.4f\n", res->label_id, res->score);
+  for (int i = 0; i < res_count[0]; ++i) {
+    fprintf(stderr, "label: %d, score: %.4f\n", res->label_id, res->score);
+    ++res;
+  }
 
   mmdeploy_classifier_release_result(res, res_count, 1);
 

demo/csrc/object_detection.cpp

@@ -52,7 +52,7 @@ int main(int argc, char *argv[]) {
     }
 
     // skip detections less than specified score threshold
-    if (bboxes[i].score < 0.1) {
+    if (bboxes[i].score < 0.3) {
       continue;
     }
 

demo/python/image_classification.py

@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+from mmdeploy_python import Classifier
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+    classifier = Classifier(args.model_path, args.device_name, 0)
+    result = classifier([img])
+    for label_id, score in result[0]:
+        print(label_id, score)
+
+
+if __name__ == '__main__':
+    main()

demo/python/image_restorer.py

@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+from mmdeploy_python import Restorer
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+
+    restorer = Restorer(args.model_path, args.device_name, 0)
+    result = restorer([img])[0]
+
+    # convert to BGR
+    result = result[..., ::-1]
+    cv2.imwrite('output_restorer.bmp', result)
+
+
+if __name__ == '__main__':
+    main()

demo/python/image_segmentation.py

@@ -0,0 +1,51 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import numpy as np
+from mmdeploy_python import Segmentor
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def get_palette(num_classes=256):
+    state = np.random.get_state()
+    # random color
+    np.random.seed(42)
+    palette = np.random.randint(0, 256, size=(num_classes, 3))
+    np.random.set_state(state)
+    return [tuple(c) for c in palette]
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+
+    segmentor = Segmentor(args.model_path, args.device_name, 0)
+    seg = segmentor([img])[0]
+
+    palette = get_palette()
+    color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8)
+    for label, color in enumerate(palette):
+        color_seg[seg == label, :] = color
+    # convert to BGR
+    color_seg = color_seg[..., ::-1]
+
+    img = img * 0.5 + color_seg * 0.5
+    img = img.astype(np.uint8)
+    cv2.imwrite('output_segmentation.png', img)
+
+
+if __name__ == '__main__':
+    main()

demo/python/object_detection.py

@@ -0,0 +1,50 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import numpy as np
+from mmdeploy_python import Detector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+    detector = Detector(args.model_path, args.device_name, 0)
+    bboxes, labels, masks = detector([img])[0]
+    assert (isinstance(bboxes, np.ndarray))
+    assert (isinstance(labels, np.ndarray))
+    assert (isinstance(masks, list))
+
+    indices = [i for i in range(len(bboxes))]
+    for index, bbox, label_id in zip(indices, bboxes, labels):
+        [left, top, right, bottom], score = bbox[0:4].astype(int), bbox[4]
+        if score < 0.3:
+            continue
+
+        cv2.rectangle(img, (left, top), (right, bottom), (0, 255, 0))
+
+        if masks[index].size:
+            mask = masks[index]
+            blue, green, red = cv2.split(img)
+            mask_img = blue[top:top + mask.shape[0], left:left + mask.shape[1]]
+            cv2.bitwise_or(mask, mask_img, mask_img)
+            img = cv2.merge([blue, green, red])
+
+    cv2.imwrite('output_detection.png', img)
+
+
+if __name__ == '__main__':
+    main()

demo/python/ocr.py

@@ -0,0 +1,44 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+from mmdeploy_python import TextDetector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        '--textdet',
+        default='',
+        help='the directory path of mmdeploy text-detector sdk model')
+    parser.add_argument(
+        '--textrecog',
+        default='',
+        help='the directory path of mmdeploy text-recognizer sdk model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+
+    if args.textdet:
+        detector = TextDetector(args.textdet, args.device_name, 0)
+        bboxes = detector([img])[0]
+
+        pts = (bboxes[:, 0:8] + 0.5).reshape(len(bboxes), -1, 2).astype(int)
+        cv2.polylines(img, pts, True, (0, 255, 0), 2)
+        cv2.imwrite('output_ocr.png', img)
+
+    if args.textrecog:
+        print('API of TextRecognizer does not support bbox as argument yet')
+
+
+if __name__ == '__main__':
+    main()

demo/python/pose_detection.py

@@ -0,0 +1,52 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import numpy as np
+from mmdeploy_python import PoseDetector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--bbox',
+        default=None,
+        nargs='+',
+        help='bounding box of an object in format (x, y, w, h)')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+
+    bboxes = []
+    if args.bbox is None:
+        bbox = [0, 0, img.shape[1], img.shape[0]]
+    else:
+        # x, y, w, h -> left, top, right, bottom
+        bbox = np.array(args.bbox, dtype=int)
+        bbox[2:] += bbox[:2]
+    bboxes.append(bbox)
+
+    detector = PoseDetector(args.model_path, args.device_name, 0)
+    result = detector([img], [bboxes])[0]
+
+    _, point_num, _ = result.shape
+    points = result[:, :, :2].reshape(point_num, 2)
+    for [x, y] in points.astype(int):
+        cv2.circle(img, (x, y), 1, (0, 255, 0), 2)
+
+    cv2.imwrite('output_pose.png', img)
+
+
+if __name__ == '__main__':
+    main()

demo/python/rotated_object_detection.py

@@ -0,0 +1,51 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from math import cos, sin
+
+import cv2
+import numpy as np
+from mmdeploy_python import RotatedDetector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='show how to use sdk python api')
+    parser.add_argument(
+        'model_path', help='the directory path of mmdeploy model')
+    parser.add_argument('image_path', help='the path of an image')
+    parser.add_argument(
+        '--device-name', default='cpu', help='the name of device, cuda or cpu')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    img = cv2.imread(args.image_path)
+    detector = RotatedDetector(args.model_path, args.device_name, 0)
+    rbboxes, labels = detector([img])[0]
+    # print(rbboxes, labels)
+    indices = [i for i in range(len(rbboxes))]
+    for index, rbbox, label_id in zip(indices, rbboxes, labels):
+        [cx, cy, w, h, angle], score = rbbox[0:5], rbbox[-1]
+        if score < 0.1:
+            continue
+        [wx, wy, hx, hy] = \
+            0.5 * np.array([w, w, -h, h]) * \
+            np.array([cos(angle), sin(angle), sin(angle), cos(angle)])
+        points = np.array([[[int(cx - wx - hx),
+                             int(cy - wy - hy)],
+                            [int(cx + wx - hx),
+                             int(cy + wy - hy)],
+                            [int(cx + wx + hx),
+                             int(cy + wy + hy)],
+                            [int(cx - wx + hx),
+                             int(cy - wy + hy)]]])
+        cv2.drawContours(img, points, -1, (0, 255, 0), 2)
+
+    cv2.imwrite('output_detection.png', img)
+
+
+if __name__ == '__main__':
+    main()