【目标检测】yolov5模型转换从pytorch到onnx到openvino（部署方式）

待我称王封你为后i 2022-10-17 04:08 845阅读 0赞

# 1.环境 #

yolov5环境中有onnx就可以，可以在另外的环境中单独安装openvino。

# yolov5的环境 + onnx
    onnx==1.9.0
    
    # openvino的环境
    openvino_2021.1.110

# 2.步骤 #

# 1. yolov5s pytorch model to onnx model （develop分支下）
    python3 models/export.py --weights ./weights/yolov5s.pt --img-size 640 --batch-size 1
    
    # 2. yolov5s onnx to openvino model
    python3 /opt/intel/openvino_2021.1.110/deployment_tools/model_optimizer/mo.py --input_model yolov5s.onnx --model_name yolov5s -s 255 --reverse_input_channels --output Conv_551,Conv_503,Conv_455
    
    # 3.1.1 test openvino model with CPU
    python3 yolo__openvino_demo.py -m yolov5s.xml -i voc/img/2007_000027.jpg -at yolov5 -d CPU
    
    # 3.1.2 test openvino model with GPU
    python3 yolo__openvino_demo.py -m yolov5s.xml -i voc/img/2007_000027.jpg -at yolov5 -d GPU
    
    # 3.2.1 test video openvino model with CPU
    python3 yolo__openvino_demo.py -m yolov5s.xml -i 2007_000027.mp4 -at yolov5 -d CPU
    
    # 3.2.2 test video openvino model with GPU
    python3 yolo__openvino_demo.py -m yolov5s.xml -i 2007_000027.mp4 -at yolov5 -d GPU

重点：

（1）转onnx的说明：

下面的代码支持yolov5的release v3-v5版本。使用[develop分支][develop]下转换onnx模型。确认最后有三个conv（40×40,20×20,80×80三个尺度之前的conv）。下面是v3（利用netron可视化获得下面conv的三个节点，就是转openvino时的--output三个参数）。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70][]

（2）转openvino的说明：

获得节点，点一下第一个conv，出现（以此类推），然后修改转onnx的节点（Conv\_551---Conv\_608）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1][]

参考代码：[https://github.com/Chen-MingChang/pytorch\_YOLO\_OpenVINO\_demo/blob/main/yolo\_openvino\_demo.py][https_github.com_Chen-MingChang_pytorch_YOLO_OpenVINO_demo_blob_main_yolo_openvino_demo.py]

#!/usr/bin/env python
    """
     Copyright (C) 2018-2019 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.
    """
    from __future__ import print_function, division
    
    import logging
    import os
    import sys
    from argparse import ArgumentParser, SUPPRESS
    from math import exp as exp
    from time import time
    import numpy as np
    
    import ngraph
    import cv2
    from openvino.inference_engine import IENetwork, IECore
    
    logging.basicConfig(format="[ %(levelname)s ] %(message)s", level=logging.INFO, stream=sys.stdout)
    log = logging.getLogger()
    
    
    def build_argparser():
        parser = ArgumentParser(add_help=False)
        args = parser.add_argument_group('Options')
        args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
        args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
                          required=True, type=str)
        args.add_argument("-at", "--architecture_type", help='Required. Specify model\' architecture type.',
                          type=str, required=True, choices=('yolov3', 'yolov4', 'yolov5', 'yolov4-p5', 'yolov4-p6', 'yolov4-p7'))
        args.add_argument("-i", "--input", help="Required. Path to an image/video file. (Specify 'cam' to work with "
                                                "camera)", required=True, type=str)
        args.add_argument("-l", "--cpu_extension",
                          help="Optional. Required for CPU custom layers. Absolute path to a shared library with "
                               "the kernels implementations.", type=str, default=None)
        args.add_argument("-d", "--device",
                          help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is"
                               " acceptable. The sample will look for a suitable plugin for device specified. "
                               "Default value is CPU", default="CPU", type=str)
        args.add_argument("--labels", help="Optional. Labels mapping file", default=None, type=str)
        args.add_argument("-t", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
                          default=0.5, type=float)
        args.add_argument("-iout", "--iou_threshold", help="Optional. Intersection over union threshold for overlapping "
                                                           "detections filtering", default=0.4, type=float)
        args.add_argument("-ni", "--number_iter", help="Optional. Number of inference iterations", default=1, type=int)
        args.add_argument("-pc", "--perf_counts", help="Optional. Report performance counters", default=False,
                          action="store_true")
        args.add_argument("-r", "--raw_output_message", help="Optional. Output inference results raw values showing",
                          default=False, action="store_true")
        args.add_argument("--no_show", help="Optional. Don't show output", action='store_true')
        return parser
    
    
    class YoloParams:
        # ------------------------------------------- Extracting layer parameters ------------------------------------------
        # Magic numbers are copied from yolo samples
        def __init__(self, param, side, yolo_type):
            self.coords = 4 if 'coords' not in param else int(param['coords'])
            self.classes = 80 if 'classes' not in param else int(param['classes'])
            self.side = side
    
            if yolo_type == 'yolov4':
                self.num = 3
                self.anchors = [12.0,16.0, 19.0,36.0, 40.0,28.0, 36.0,75.0, 76.0,55.0, 72.0,146.0, 142.0,110.0, 192.0,243.0, 
                                459.0,401.0] 
            elif yolo_type == 'yolov4-p5':
                self.num = 4 
                self.anchors = [13.0,17.0, 31.0,25.0, 24.0,51.0, 61.0,45.0, 48.0,102.0, 119.0,96.0, 97.0,189.0, 217.0,184.0,
                                171.0,384.0, 324.0,451.0, 616.0,618.0, 800.0,800.0] 
            elif yolo_type == 'yolov4-p6':
                self.num = 4 
                self.anchors = [13.0,17.0, 31.0,25.0, 24.0,51.0, 61.0,45.0, 61.0,45.0, 48.0,102.0, 119.0,96.0, 97.0,189.0, 
                                97.0,189.0, 217.0,184.0, 171.0,384.0, 324.0,451.0, 324.0,451.0, 545.0,357.0, 616.0,618.0, 1024.0,1024.0]
            elif yolo_type == 'yolov4-p7':
                self.num = 5 
                self.anchors = [13.0,17.0,  22.0,25.0,  27.0,66.0,  55.0,41.0, 57.0,88.0,  112.0,69.0,  69.0,177.0,  136.0,138.0,
                                136.0,138.0,  287.0,114.0,  134.0,275.0,  268.0,248.0, 268.0,248.0,  232.0,504.0,  445.0,416.0,  640.0,640.0,
                                812.0,393.0,  477.0,808.0,  1070.0,908.0,  1408.0,1408.0]
            else:
                self.num = 3 
                self.anchors = [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0,
                                198.0,
                                373.0, 326.0]
    
        def log_params(self):
            params_to_print = {'classes': self.classes, 'num': self.num, 'coords': self.coords, 'anchors': self.anchors}
            [log.info("         {:8}: {}".format(param_name, param)) for param_name, param in params_to_print.items()]
    
    
    def letterbox(img, size=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
        # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
        shape = img.shape[:2]  # current shape [height, width]
        w, h = size
    
        # Scale ratio (new / old)
        r = min(h / shape[0], w / shape[1])
        if not scaleup:  # only scale down, do not scale up (for better test mAP)
            r = min(r, 1.0)
    
        # Compute padding
        ratio = r, r  # width, height ratios
        new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
        dw, dh = w - new_unpad[0], h - new_unpad[1]  # wh padding
        if auto:  # minimum rectangle
            dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
        elif scaleFill:  # stretch
            dw, dh = 0.0, 0.0
            new_unpad = (w, h)
            ratio = w / shape[1], h / shape[0]  # width, height ratios
    
        dw /= 2  # divide padding into 2 sides
        dh /= 2
    
        if shape[::-1] != new_unpad:  # resize
            img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
        top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
        left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    
        top2, bottom2, left2, right2 = 0, 0, 0, 0
        if img.shape[0] != h:
            top2 = (h - img.shape[0])//2
            bottom2 = top2
            img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color)  # add border
        elif img.shape[1] != w:
            left2 = (w - img.shape[1])//2
            right2 = left2
            img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color)  # add border
        return img
    
    
    def scale_bbox(x, y, height, width, class_id, confidence, im_h, im_w, resized_im_h=640, resized_im_w=640):
        gain = min(resized_im_w / im_w, resized_im_h / im_h)  # gain  = old / new
        pad = (resized_im_w - im_w * gain) / 2, (resized_im_h - im_h * gain) / 2  # wh padding
        x = int((x - pad[0])/gain)
        y = int((y - pad[1])/gain)
    
        w = int(width/gain)
        h = int(height/gain)
     
        xmin = max(0, int(x - w / 2))
        ymin = max(0, int(y - h / 2))
        xmax = min(im_w, int(xmin + w))
        ymax = min(im_h, int(ymin + h))
        # Method item() used here to convert NumPy types to native types for compatibility with functions, which don't
        # support Numpy types (e.g., cv2.rectangle doesn't support int64 in color parameter)
        return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id.item(), confidence=confidence.item())
    
    
    def entry_index(side, coord, classes, location, entry):
        side_power_2 = side ** 2
        n = location // side_power_2
        loc = location % side_power_2
        return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)
    
    
    def parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold, yolo_type):
        # ------------------------------------------ Validating output parameters ------------------------------------------    
        out_blob_n, out_blob_c, out_blob_h, out_blob_w = blob.shape
        predictions = 1.0/(1.0+np.exp(-blob)) 
                       
        assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
                                         "be equal to width. Current height = {}, current width = {}" \
                                         "".format(out_blob_h, out_blob_w)
    
        # ------------------------------------------ Extracting layer parameters -------------------------------------------
        orig_im_h, orig_im_w = original_im_shape
        resized_image_h, resized_image_w = resized_image_shape
        objects = list()
     
        side_square = params.side[1] * params.side[0]
    
        # ------------------------------------------- Parsing YOLO Region output -------------------------------------------
        bbox_size = int(out_blob_c/params.num) #4+1+num_classes
        #print('bbox_size = ' + str(bbox_size))
        #print('bbox_size = ' + str(bbox_size))
        for row, col, n in np.ndindex(params.side[0], params.side[1], params.num):
            bbox = predictions[0, n*bbox_size:(n+1)*bbox_size, row, col]
            
            x, y, width, height, object_probability = bbox[:5]
            class_probabilities = bbox[5:]
            if object_probability < threshold:
                continue
            #print('resized_image_w = ' + str(resized_image_w))
            #print('out_blob_w = ' + str(out_blob_w))
    
            x = (2*x - 0.5 + col)*(resized_image_w/out_blob_w)
            y = (2*y - 0.5 + row)*(resized_image_h/out_blob_h)
            if int(resized_image_w/out_blob_w) == 8 & int(resized_image_h/out_blob_h) == 8: #80x80, 
                idx = 0
            elif int(resized_image_w/out_blob_w) == 16 & int(resized_image_h/out_blob_h) == 16: #40x40
                idx = 1
            elif int(resized_image_w/out_blob_w) == 32 & int(resized_image_h/out_blob_h) == 32: # 20x20
                idx = 2
            elif int(resized_image_w/out_blob_w) == 64 & int(resized_image_h/out_blob_h) == 64: # 20x20
                idx = 3
            elif int(resized_image_w/out_blob_w) == 128 & int(resized_image_h/out_blob_h) == 128: # 20x20
                idx = 4
       
            if yolo_type == 'yolov4-p5' or yolo_type == 'yolov4-p6' or yolo_type == 'yolov4-p7':
                width = (2*width)**2* params.anchors[idx * 8 + 2 * n] 
                height = (2*height)**2 * params.anchors[idx * 8 + 2 * n + 1]
            else:
                width = (2*width)**2* params.anchors[idx * 6 + 2 * n] 
                height = (2*height)**2 * params.anchors[idx * 6 + 2 * n + 1]
    
            class_id = np.argmax(class_probabilities * object_probability)
            confidence = object_probability
            objects.append(scale_bbox(x=x, y=y, height=height, width=width, class_id=class_id, confidence=confidence,
                                      im_h=orig_im_h, im_w=orig_im_w, resized_im_h=resized_image_h, resized_im_w=resized_image_w))
        return objects
    
    
    def intersection_over_union(box_1, box_2):
        width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
        height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
        if width_of_overlap_area < 0 or height_of_overlap_area < 0:
            area_of_overlap = 0
        else:
            area_of_overlap = width_of_overlap_area * height_of_overlap_area
        box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
        box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
        area_of_union = box_1_area + box_2_area - area_of_overlap
        if area_of_union == 0:
            return 0
        return area_of_overlap / area_of_union
    
    
    def main():
        args = build_argparser().parse_args()
    
        model_xml = args.model
        model_bin = os.path.splitext(model_xml)[0] + ".bin"
    
        # ------------- 1. Plugin initialization for specified device and load extensions library if specified -------------
        log.info("Creating Inference Engine...")
        ie = IECore()
        if args.cpu_extension and 'CPU' in args.device:
            ie.add_extension(args.cpu_extension, "CPU")
    
        # -------------------- 2. Reading the IR generated by the Model Optimizer (.xml and .bin files) --------------------
        log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
        net = IENetwork(model=model_xml, weights=model_bin)
    
        # ---------------------------------- 3. Load CPU extension for support specific layer ------------------------------
        #if "CPU" in args.device:
        #    supported_layers = ie.query_network(net, "CPU")
        #    not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
        #    if len(not_supported_layers) != 0:
        #        log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
        #                  format(args.device, ', '.join(not_supported_layers)))
        #        log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
        #                  "or --cpu_extension command line argument")
        #        sys.exit(1)
        #
        #assert len(net.inputs.keys()) == 1, "Sample supports only YOLO V3 based single input topologies"
    
        # ---------------------------------------------- 4. Preparing inputs -----------------------------------------------
        log.info("Preparing inputs")
        input_blob = next(iter(net.inputs))
    
        #  Defaulf batch_size is 1
        net.batch_size = 1
    
        # Read and pre-process input images
        n, c, h, w = net.inputs[input_blob].shape
    
        ng_func = ngraph.function_from_cnn(net)
        yolo_layer_params = {}
        for node in ng_func.get_ordered_ops():
            layer_name = node.get_friendly_name()
            if layer_name not in net.outputs:
                continue
            shape = list(node.inputs()[0].get_source_output().get_node().shape)
            yolo_params = YoloParams(node._get_attributes(), shape[2:4], args.architecture_type)
            yolo_layer_params[layer_name] = (shape, yolo_params)
    
        if args.labels:
            with open(args.labels, 'r') as f:
                labels_map = [x.strip() for x in f]
        else:
            labels_map = None
    
        input_stream = 0 if args.input == "cam" else args.input
    
        is_async_mode = True
        cap = cv2.VideoCapture(input_stream)
        number_input_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        number_input_frames = 1 if number_input_frames != -1 and number_input_frames < 0 else number_input_frames
    
        wait_key_code = 1
    
        # Number of frames in picture is 1 and this will be read in cycle. Sync mode is default value for this case
        if number_input_frames != 1:
            ret, frame = cap.read()
        else:
            is_async_mode = False
            wait_key_code = 0
    
        # ----------------------------------------- 5. Loading model to the plugin -----------------------------------------
        log.info("Loading model to the plugin")
        exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device)
    
        cur_request_id = 0
        next_request_id = 1
        render_time = 0
        parsing_time = 0
    
        # ----------------------------------------------- 6. Doing inference -----------------------------------------------
        log.info("Starting inference...")
        print("To close the application, press 'CTRL+C' here or switch to the output window and press ESC key")
        print("To switch between sync/async modes, press TAB key in the output window")
        while cap.isOpened():
            # Here is the first asynchronous point: in the Async mode, we capture frame to populate the NEXT infer request
            # in the regular mode, we capture frame to the CURRENT infer request
            if is_async_mode:
                ret, next_frame = cap.read()
            else:
                ret, frame = cap.read()
    
            if not ret:
                break
    
            if is_async_mode:
                request_id = next_request_id
                in_frame = letterbox(frame, (w, h))
            else:
                request_id = cur_request_id
                in_frame = letterbox(frame, (w, h))
    
            in_frame0 = in_frame
            # resize input_frame to network size
            in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
            in_frame = in_frame.reshape((n, c, h, w))
    
            # Start inference
            start_time = time()
            exec_net.start_async(request_id=request_id, inputs={input_blob: in_frame})
            det_time = time() - start_time
    
            # Collecting object detection results
            objects = list()
            if exec_net.requests[cur_request_id].wait(-1) == 0:
                output = exec_net.requests[cur_request_id].outputs
                start_time = time()
    
                for layer_name, out_blob in output.items():
                    #out_blob = out_blob.reshape(net.layers[layer_name].out_data[0].shape)
                    layer_params = yolo_layer_params[layer_name]#YoloParams(net.layers[layer_name].params, out_blob.shape[2])
                    out_blob.shape = layer_params[0]
                    #log.info("Layer {} parameters: ".format(layer_name))
                    #layer_params.log_params()
                    objects += parse_yolo_region(out_blob, in_frame.shape[2:],
                                                 #in_frame.shape[2:], layer_params,
                                                 frame.shape[:-1], layer_params[1],
                                                 args.prob_threshold, args.architecture_type)
                parsing_time = time() - start_time
    
            # Filtering overlapping boxes with respect to the --iou_threshold CLI parameter
            objects = sorted(objects, key=lambda obj : obj['confidence'], reverse=True)
            for i in range(len(objects)):
                if objects[i]['confidence'] == 0:
                    continue
                for j in range(i + 1, len(objects)):
                    if intersection_over_union(objects[i], objects[j]) > args.iou_threshold:
                        objects[j]['confidence'] = 0
    
            # Drawing objects with respect to the --prob_threshold CLI parameter
            objects = [obj for obj in objects if obj['confidence'] >= args.prob_threshold]
    
            if len(objects) and args.raw_output_message:
                log.info("\nDetected boxes for batch {}:".format(1))
                log.info(" Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR ")
    
            origin_im_size = frame.shape[:-1]
            for obj in objects:
                # Validation bbox of detected object
                if obj['xmax'] > origin_im_size[1] or obj['ymax'] > origin_im_size[0] or obj['xmin'] < 0 or obj['ymin'] < 0:
                    continue
                color = (int(min(obj['class_id'] * 12.5, 255)),
                         min(obj['class_id'] * 7, 255), min(obj['class_id'] * 5, 255))
                det_label = labels_map[obj['class_id']] if labels_map and len(labels_map) >= obj['class_id'] else \
                    str(obj['class_id'])
    
                if args.raw_output_message:
                    log.info(
                        "{:^9} | {:10f} | {:4} | {:4} | {:4} | {:4} | {} ".format(det_label, obj['confidence'], obj['xmin'],
                                                                                  obj['ymin'], obj['xmax'], obj['ymax'],
                                                                                  color))
    
                cv2.rectangle(frame, (obj['xmin'], obj['ymin']), (obj['xmax'], obj['ymax']), color, 2)
                cv2.putText(frame,
                            "#" + det_label + ' ' + str(round(obj['confidence'] * 100, 1)) + ' %',
                            (obj['xmin'], obj['ymin'] - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
    
            # Draw performance stats over frame
            inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
                "Inference time: {:.3f} ms".format(det_time * 1e3)
            render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_time * 1e3)
            async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
                "Async mode is off. Processing request {}".format(cur_request_id)
            parsing_message = "YOLO parsing time is {:.3f} ms".format(parsing_time * 1e3)
    
            cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
            cv2.putText(frame, render_time_message, (15, 45), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
            cv2.putText(frame, async_mode_message, (10, int(origin_im_size[0] - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
                        (10, 10, 200), 1)
            cv2.putText(frame, parsing_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
    
            start_time = time()
            if not args.no_show:
                cv2.imshow("DetectionResults", frame)
            render_time = time() - start_time
    
            if is_async_mode:
                cur_request_id, next_request_id = next_request_id, cur_request_id
                frame = next_frame
    
            if not args.no_show:
                key = cv2.waitKey(wait_key_code)
        
                # ESC key
                if key == 27:
                    break
                # Tab key
                if key == 9:
                    exec_net.requests[cur_request_id].wait()
                    is_async_mode = not is_async_mode
                    log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))
    
        cv2.destroyAllWindows()
    
    
    if __name__ == '__main__':
        sys.exit(main() or 0

[develop]: https://download.csdn.net/download/qq_35975447/19360445
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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzM1OTc1NDQ3_size_16_color_FFFFFF_t_70 1]: /images/20221014/acdb6a2d51ab406c860f0d950a555258.png
[https_github.com_Chen-MingChang_pytorch_YOLO_OpenVINO_demo_blob_main_yolo_openvino_demo.py]: https://github.com/Chen-MingChang/pytorch_YOLO_OpenVINO_demo/blob/main/yolo_openvino_demo.py