AI项目六：基于YOLOV5的CPU版本部署openvino_yolov5 导出 openvino

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一、CPU版本DEMO测试

1、创建一个新的虚拟环境

conda create -n course_torch_openvino python=3.8

2、激活环境

conda activate course_torch_openvino

3、安装pytorch cpu版本

pip install torch torchvision torchaudio  -i https://pypi.tuna.tsinghua.edu.cn/simple

4、安装

使用的是yolov5-5版本，github上下载。

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

5、运行demo

python demo.py

完整代码 

import cv2
import numpy as np
import torch
import time
 
# model = torch.hub.load('./yolov5', 'custom', path='./weights/ppe_yolo_n.pt',source='local')  # local repo
model = torch.hub.load('./yolov5', 'custom', 'weights/poker_n.pt',source='local')
model.conf = 0.4
 
cap = cv2.VideoCapture(0)
 
fps_time = time.time()
 
while True:
 
    ret,frame = cap.read()
 
    frame = cv2.flip(frame,1)
 
    img_cvt = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)
 
    # Inference
    results = model(img_cvt)
    result_np = results.pandas().xyxy[0].to_numpy()
 
    for box in result_np:
        l,t,r,b = box[:4].astype('int')
        
        cv2.rectangle(frame,(l,t),(r,b),(0,255,0),5)
        cv2.putText(frame,str(box[-1]),(l,t-20),cv2.FONT_ITALIC,1,(0,255,0),2)
 
    now = time.time()
    fps_text = 1/(now - fps_time)
    fps_time =  now
 
    cv2.putText(frame,str(round(fps_text,2)),(50,50),cv2.FONT_ITALIC,1,(0,255,0),2)
 
 
    cv2.imshow('demo',frame)
 
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break
 
cap.release()
cv2.destroyAllWindows()

运行正常

二、YOLOV5转换成openvino

1、安装onnx

pip install onnx==1.11.0

2、修改文件

修改export.py 的第121行,修改成

opset_version=10

3、导出onnx

使用训练好的best.pt文件，把best.pt转成onnx文件

转换命令为：

python export.py --weights ../weights/best.pt --img 640 --batch 1

4、转成openvino

转换前先安装环境

pip install openvino-dev[onnx]==2021.4.0 
pip install openvino==2021.4.0

验证一下，输入mo -h

接下来转换模型，使用下面命令导出模型

mo --input_model weights/best.onnx  --model_name weights/ir_model   -s 255 --reverse_input_channels --output Conv_294,Conv_245,Conv_196

会生成3个文件， ir_model.xml就是要用的文件。

5、运行

python yolov5_demo.py -i cam -m weights/ir_model.xml   -d CPU

代码：

 
import logging
import os
import sys
from argparse import ArgumentParser, SUPPRESS
from math import exp as exp
from time import time,sleep
import numpy as np
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("-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("-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)
 
    return parser
 
 
class YoloParams:
    # ------------------------------------------- Extracting layer parameters ------------------------------------------
    # Magic numbers are copied from yolo samples
    def __init__(self,  side):
        self.num = 3 #if 'num' not in param else int(param['num'])
        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
        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] #if 'anchors' not in param else [float(a) for a in param['anchors'].split(',')]
 
 
 
 
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):
    # ------------------------------------------ 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)) 
    
                   
    # ------------------------------------------ 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 * params.side
 
    # ------------------------------------------- Parsing YOLO Region output -------------------------------------------
    bbox_size = int(out_blob_c/params.num) #4+1+num_classes
    index=0
    for row, col, n in np.ndindex(params.side, params.side, 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
        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
 
        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)
        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))
        if index >30:
            break
        index+=1
    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()
 
    # ------------- 1. Plugin initialization for specified device and load extensions library if specified -------------
    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) --------------------
    model = args.model
    net = ie.read_network(model=model)
 
    # ---------------------------------------------- 4. Preparing inputs -----------------------------------------------
    input_blob = next(iter(net.input_info))
 
    #  Defaulf batch_size is 1
    net.batch_size = 1
 
    # Read and pre-process input images
    n, c, h, w = net.input_info[input_blob].input_data.shape
    
    # labels_map = [x.strip() for x in f]
    labels_map = ['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']
 
    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 -----------------------------------------
    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 -----------------------------------------------
    initial_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    initial_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    origin_im_size = (initial_h,initial_w)
    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})
        
 
        # Collecting object detection results
        objects = list()
        if exec_net.requests[cur_request_id].wait(-1) == 0:
            output = exec_net.requests[cur_request_id].output_blobs
            start_time = time()
            
            for layer_name, out_blob in output.items():
                layer_params = YoloParams(side=out_blob.buffer.shape[2])
                objects += parse_yolo_region(out_blob.buffer, in_frame.shape[2:],
                                             frame.shape[:-1], layer_params,
                                             args.prob_threshold)
                
                
            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]
 
        
      
 
        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 = (0,255,0)
            det_label = labels_map[obj['class_id']] if labels_map and len(labels_map) >= obj['class_id'] else \
                str(obj['class_id'])
 
 
            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_ITALIC, 1, color, 2)
 
        # Draw performance stats over frame
        async_mode_message = "Async mode: ON"if is_async_mode else "Async mode: OFF"
        
        cv2.putText(frame, async_mode_message, (10, int(origin_im_size[0] - 20)), cv2.FONT_ITALIC, 1,
                    (10, 10, 200), 2)
 
        fps_time = time() - start_time
        if fps_time !=0:
            fps = 1 / fps_time
            cv2.putText(frame, 'fps:'+str(round(fps,2)), (50, 50), cv2.FONT_ITALIC, 1, (0, 255, 0), 2)
        
        cv2.imshow("DetectionResults", frame)
 
 
        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame
 
        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)

三、总结

通过openvino加速，CPU没有GPU下，从原本的20帧左右提升到50多帧，效果还可以，就 是用自己的模型，训练出来的效果不怎么好。

使用树莓派等嵌入板子使用openvino效果还可以。

如有侵权，或需要完整代码，请及时联系博主。