Merge pull request #104 from Sidd1609:YoloX

models/object_detection_yolox/LICENSE

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models/object_detection_yolox/README.md

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+# YOLOX
+
+Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.
+
+Key features of the YOLOX object detector
+- **Anchor-free detectors** significantly reduce the number of design parameters
+- **A decoupled head for classification, regression, and localization** improves the convergence speed
+- **SimOTA advanced label assignment strategy** reduces training time and avoids additional solver hyperparameters
+- **Strong data augmentations like MixUp and Mosiac** to boost YOLOX performance
+
+Note:
+- This version of YoloX: YoloX_s
+
+## 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
+```
+Note: 
+- image result saved as "result.jpg"
+
+
+## Results
+
+Here are some of the sample results that were observed using the model (**yolox_s.onnx**),
+
+![1_res.jpg](./samples/1_res.jpg)
+![2_res.jpg](./samples/2_res.jpg)
+![3_res.jpg](./samples/3_res.jpg)
+
+<!--  
+Video inference result,
+![WebCamR.gif](./examples/results/WebCamR.gif)
+-->
+
+## Model metrics:
+
+The model is evaluated on [COCO 2017 val](https://cocodataset.org/#download). Results are showed below:
+
+<table>
+<tr><th>Average Precision </th><th>Average Recall</th></tr>
+<tr><td>
+
+|  area  |  IoU  |  Average Precision(AP)  |
+|:-------|:------|:------------------------|
+|  all  |  0.50:0.95  |  0.405  |
+|  all  |  0.50  |  0.593  |
+|  all  |  0.75  |  0.437  |
+|  small  |  0.50:0.95  |  0.232  |
+|  medium  |  0.50:0.95  |  0.448  |
+|  large  |  0.50:0.95  |  0.541  |
+
+ </td><td>
+
+  area  |  IoU  |  Average Recall(AR)  |
+|:-------|:------|:----------------|
+|  all  |  0.50:0.95  |  0.326  |
+|  all  |  0.50:0.95  |  0.531  |
+|  all  |  0.50:0.95 |  0.574  |
+|  small  |  0.50:0.95  |  0.365  |
+|  medium  |  0.50:0.95  |  0.634  |
+|  large  |  0.50:0.95  |  0.724  |
+</td></tr> </table>
+
+| class         | AP     | class        | AP     | class          | AP     |
+|:--------------|:-------|:-------------|:-------|:---------------|:-------|
+| person        | 54.109 | bicycle      | 31.580 | car            | 40.447 |
+| motorcycle    | 43.477 | airplane     | 66.070 | bus            | 64.183 |
+| train         | 64.483 | truck        | 35.110 | boat           | 24.681 |
+| traffic light | 25.068 | fire hydrant | 64.382 | stop sign      | 65.333 |
+| parking meter | 48.439 | bench        | 22.653 | bird           | 33.324 |
+| cat           | 66.394 | dog          | 60.096 | horse          | 58.080 |
+| sheep         | 49.456 | cow          | 53.596 | elephant       | 65.574 |
+| bear          | 70.541 | zebra        | 66.461 | giraffe        | 66.780 |
+| backpack      | 13.095 | umbrella     | 41.614 | handbag        | 12.865 |
+| tie           | 29.453 | suitcase     | 39.089 | frisbee        | 61.712 |
+| skis          | 21.623 | snowboard    | 31.326 | sports ball    | 39.820 |
+| kite          | 41.410 | baseball bat | 27.311 | baseball glove | 36.661 |
+| skateboard    | 49.374 | surfboard    | 35.524 | tennis racket  | 45.569 |
+| bottle        | 37.270 | wine glass   | 33.088 | cup            | 39.835 |
+| fork          | 31.620 | knife        | 15.265 | spoon          | 14.918 |
+| bowl          | 43.251 | banana       | 27.904 | apple          | 17.630 |
+| sandwich      | 32.789 | orange       | 29.388 | broccoli       | 23.187 |
+| carrot        | 23.114 | hot dog      | 33.716 | pizza          | 52.541 |
+| donut         | 47.980 | cake         | 36.160 | chair          | 29.707 |
+| couch         | 46.175 | potted plant | 24.781 | bed            | 44.323 |
+| dining table  | 30.022 | toilet       | 64.237 | tv             | 57.301 |
+| laptop        | 58.362 | mouse        | 57.774 | remote         | 24.271 |
+| keyboard      | 48.020 | cell phone   | 32.376 | microwave      | 57.220 |
+| oven          | 36.168 | toaster      | 28.735 | sink           | 38.159 |
+| refrigerator  | 52.876 | book         | 15.030 | clock          | 48.622 |
+| vase          | 37.013 | scissors     | 26.307 | teddy bear     | 45.676 |
+| hair drier    | 7.255  | toothbrush   | 19.374 |                |        |
+
+## License
+
+All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
+
+#### Contributor Details
+
+- Google Summer of Code'22
+- Contributor: Sri Siddarth Chakaravarthy
+- Github Profile: https://github.com/Sidd1609
+- Organisation: OpenCV
+- Project: Lightweight object detection models using OpenCV 
+
+## Reference
+
+- YOLOX article: https://arxiv.org/abs/2107.08430
+- YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
+- YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
+- YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox

models/object_detection_yolox/YoloX.py

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+import numpy as np
+import cv2
+
+class YoloX:
+    def __init__(self, modelPath, confThreshold=0.35, nmsThreshold=0.5, objThreshold=0.5, backendId=0, targetId=0):
+        self.num_classes = 80
+        self.net = cv2.dnn.readNet(modelPath)
+        self.input_size = (640, 640)
+        self.mean = np.array([0.485, 0.456, 0.406], dtype=np.float32).reshape(1, 1, 3)
+        self.std = np.array([0.229, 0.224, 0.225], dtype=np.float32).reshape(1, 1, 3)
+        self.strides = [8, 16, 32]
+        self.confThreshold = confThreshold
+        self.nmsThreshold = nmsThreshold
+        self.objThreshold = objThreshold
+        self.backendId = backendId
+        self.targetId = targetId
+        self.net.setPreferableBackend(self.backendId)
+        self.net.setPreferableTarget(self.targetId)
+
+    @property
+    def name(self):
+        return self.__class__.__name__
+
+    def setBackend(self, backenId):
+        self.backendId = backendId
+        self.net.setPreferableBackend(self.backendId)
+
+    def setTarget(self, targetId):
+        self.targetId = targetId
+        self.net.setPreferableTarget(self.targetId)
+
+    def preprocess(self, img):
+        blob = np.transpose(img, (2, 0, 1))
+        return blob[np.newaxis, :, :, :]
+
+    def infer(self, srcimg):
+        input_blob = self.preprocess(srcimg)
+
+        self.net.setInput(input_blob)
+        outs = self.net.forward(self.net.getUnconnectedOutLayersNames())
+
+        predictions = self.postprocess(outs[0])
+        return predictions
+
+    def postprocess(self, outputs):
+        grids = []
+        expanded_strides = []
+        hsizes = [self.input_size[0] // stride for stride in self.strides]
+        wsizes = [self.input_size[1] // stride for stride in self.strides]
+
+        for hsize, wsize, stride in zip(hsizes, wsizes, self.strides):
+            xv, yv = np.meshgrid(np.arange(hsize), np.arange(wsize))
+            grid = np.stack((xv, yv), 2).reshape(1, -1, 2)
+            grids.append(grid)
+            shape = grid.shape[:2]
+            expanded_strides.append(np.full((*shape, 1), stride))
+
+        grids = np.concatenate(grids, 1)
+        expanded_strides = np.concatenate(expanded_strides, 1)
+        outputs[..., :2] = (outputs[..., :2] + grids) * expanded_strides
+        outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides
+
+        predictions = outputs[0]
+
+        boxes = predictions[:, :4]
+        scores = predictions[:, 4:5] * predictions[:, 5:]
+
+        boxes_xyxy = np.ones_like(boxes)
+        boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2] / 2.
+        boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3] / 2.
+        boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2] / 2.
+        boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3] / 2.
+
+        # multi-class nms
+        final_dets = []
+        for cls_ind in range(scores.shape[1]):
+            cls_scores = scores[:, cls_ind]
+            valid_score_mask = cls_scores > self.confThreshold
+            if valid_score_mask.sum() == 0:
+                continue
+            else:
+                # call nms
+                indices = cv2.dnn.NMSBoxes(boxes_xyxy.tolist(), cls_scores.tolist(), self.confThreshold, self.nmsThreshold)
+
+                classids_ = np.ones((len(indices), 1)) * cls_ind
+                final_dets.append(
+                    np.concatenate([boxes_xyxy[indices], cls_scores[indices, None], classids_], axis=1)
+                )
+
+        if len(final_dets) == 0:
+            return np.array([])
+
+        return np.concatenate(final_dets, 0)

models/object_detection_yolox/demo.py

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+import numpy as np
+import cv2
+import argparse
+
+from yolox import YoloX
+
+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 = [cv2.dnn.DNN_BACKEND_OPENCV, cv2.dnn.DNN_BACKEND_CUDA]
+targets = [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_CUDA, cv2.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 += [cv2.dnn.DNN_BACKEND_TIMVX]
+    targets += [cv2.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://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.')
+
+classes = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+           'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
+           'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+           'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
+           'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+           'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
+           'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
+           'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
+           'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
+           'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+           'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
+           'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+           'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
+           'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
+
+def letterbox(srcimg, target_size=(640, 640)):
+    padded_img = np.ones((target_size[0], target_size[1], 3)) * 114.0
+    ratio = min(target_size[0] / srcimg.shape[0], target_size[1] / srcimg.shape[1])
+    resized_img = cv2.resize(
+        srcimg, (int(srcimg.shape[1] * ratio), int(srcimg.shape[0] * ratio)), interpolation=cv2.INTER_LINEAR
+    ).astype(np.float32)
+    padded_img[: int(srcimg.shape[0] * ratio), : int(srcimg.shape[1] * ratio)] = resized_img
+
+    return padded_img, ratio
+
+def unletterbox(bbox, letterbox_scale):
+    return bbox / letterbox_scale
+
+def vis(dets, srcimg, letterbox_scale, fps=None):
+    res_img = srcimg.copy()
+
+    if fps is not None:
+        fps_label = "FPS: %.2f" % fps
+        cv2.putText(res_img, fps_label, (10, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
+
+    for det in dets:
+        box = unletterbox(det[:4], letterbox_scale).astype(np.int32)
+        score = det[-2]
+        cls_id = int(det[-1])
+
+        x0, y0, x1, y1 = box
+
+        text = '{}:{:.1f}%'.format(classes[cls_id], score * 100)
+        font = cv2.FONT_HERSHEY_SIMPLEX
+        txt_size = cv2.getTextSize(text, font, 0.4, 1)[0]
+        cv2.rectangle(res_img, (x0, y0), (x1, y1), (0, 255, 0), 2)
+        cv2.rectangle(res_img, (x0, y0 + 1), (x0 + txt_size[0] + 1, y0 + int(1.5 * txt_size[1])), (255, 255, 255), -1)
+        cv2.putText(res_img, text, (x0, y0 + txt_size[1]), font, 0.4, (0, 0, 0), thickness=1)
+
+    return res_img
+
+if __name__=='__main__':
+    parser = argparse.ArgumentParser(description='Nanodet inference using OpenCV an contribution by Sri Siddarth Chakaravarthy part of GSOC_2022')
+    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='object_detection_yolox_2022nov.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('--confidence', default=0.5, type=float, help='Class confidence')
+    parser.add_argument('--nms', default=0.5, type=float, help='Enter nms IOU threshold')
+    parser.add_argument('--obj', default=0.5, type=float, help='Enter object threshold')
+    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()
+
+    model_net = YoloX(modelPath= args.model,
+                      confThreshold=args.confidence,
+                      nmsThreshold=args.nms,
+                      objThreshold=args.obj,
+                      backendId=args.backend,
+                      targetId=args.target)
+
+    tm = cv2.TickMeter()
+    tm.reset()
+    if args.input is not None:
+        image = cv2.imread(args.input)
+        input_blob = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        input_blob, letterbox_scale = letterbox(input_blob)
+
+        # Inference
+        tm.start()
+        preds = model_net.infer(input_blob)
+        tm.stop()
+        print("Inference time: {:.2f} ms".format(tm.getTimeMilli()))
+
+        img = vis(preds, image, letterbox_scale)
+
+        if args.save:
+            print('Resutls saved to result.jpg\n')
+            cv2.imwrite('result.jpg', img)
+
+        if args.vis:
+            cv2.namedWindow(args.input, cv2.WINDOW_AUTOSIZE)
+            cv2.imshow(args.input, img)
+            cv2.waitKey(0)
+
+    else:
+        print("Press any key to stop video capture")
+        deviceId = 0
+        cap = cv2.VideoCapture(deviceId)
+
+        while cv2.waitKey(1) < 0:
+            hasFrame, frame = cap.read()
+            if not hasFrame:
+                print('No frames grabbed!')
+                break
+
+            input_blob = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            input_blob, letterbox_scale = letterbox(input_blob)
+
+            # Inference
+            tm.start()
+            preds = model_net.infer(input_blob)
+            tm.stop()
+
+            img = vis(preds, frame, letterbox_scale, fps=tm.getFPS())
+
+            cv2.imshow("YoloX Demo", img)
+
+            tm.reset()