检测代码

import cv2
import numpy as np
import time

#from openvino.runtime import Core  # the version of openvino >= 2022.1 # openvino 2022.1.0 has requirement numpy<1.20,>=1.16.6
from openvino.inference_engine import IECore # the version of openvino <= 2021.4.2

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']  # class names

class OpenVinoYoloV5Detector():

    def __init__(self,IN_conf):
        # ie = Core()  # Initialize Core version>=2022.1
        # self.Net = ie.compile_model(model=IN_conf.get("weight_file"),device_name=IN_conf.get("device"))

        ie = IECore()  # Initialize IECore  openvino <= 2021.4.2
        self.Net = ie.load_network(network=IN_conf.get("weight_file"), device_name=IN_conf.get("device"))

        self.INPUT_HEIGHT = 640
        self.INPUT_WIDTH = 640

    # YOLOv5目标检测
    def detect(self, image):

        blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (self.INPUT_WIDTH, self.INPUT_HEIGHT), swapRB=True, crop=False)

        # openvino >= 2022.1
        # results = net([blob])[next(iter(net.outputs))]
        # results = self.Net([blob])[self.Net.output(0)]

        # openvino <= 2021.4.2
        results = self.Net.infer(inputs={"images": blob})
        results = results["output"]

        self.process_results(image, results)

    # YOLOv5的后处理函数，解析模型的输出
    def process_results(self,image, results,thresh=0.25):

        h, w, _ = image.shape

        class_ids = []
        boxes = []
        scores = []

        results = results[0]
        rows = results.shape[0]

        y_factor = h / self.INPUT_HEIGHT
        x_factor = w / self.INPUT_WIDTH

        for r in range(rows):
            row = results[r]
            score = row[4]

            if score >= 0.4 :

                classes_scores = row[5:]

                _, _, _, max_indexes = cv2.minMaxLoc(classes_scores)
                class_id = max_indexes[1]

                if classes_scores[class_id] > 0.25:
                    x, y, w, h = row[0].item(), row[1].item(), row[2].item(), row[3].item()

                    x1 = int((x - 0.5 * w) * x_factor)
                    y1 = int((y - 0.5 * h) * y_factor)
                    x2 = x1 + int(w * x_factor)
                    y2 = y1 + int(h * y_factor)
                    boxes.append((x1,y1, x2, y2))
                    class_ids.append(class_id)
                    scores.append(score)

        # default score_threshold=0.25, nms_threshold=0.45
        indices = cv2.dnn.NMSBoxes(bboxes=boxes, scores=scores, score_threshold=thresh, nms_threshold=0.45)

        for index in indices:
            x1, y1, x2,y2 = boxes[index][0], boxes[index][1], boxes[index][2], boxes[index][3]
            cv2.rectangle(frame, (x1,y1), (x2,y2), (0,255,0), 2)
            cv2.putText(frame, str(classes[class_ids[index]]), (x1, y1 + 20), cv2.FONT_HERSHEY_SIMPLEX, .5,(255,255,255))

if __name__ == '__main__':
    IN_conf = {
        "weight_file": "weights/yolov5n_openvino_model/yolov5n.xml",
        "device": "CPU"#"GPU"
    }
    detector = OpenVinoYoloV5Detector(IN_conf=IN_conf)

    url = 'bus.jpg'

    cap = cv2.VideoCapture(url)

    while True:
        r, frame = cap.read()
        if r:
            t1 = time.time()
            detector.detect(frame)
            cv2.imshow('OpenVinoYoloV5Detector.py', frame)
            t2 = time.time()
        else:
            print("读取%s结束" % str(url))
            break

    cv2.waitKey(0)
    cap.release()
    cv2.destroyAllWindows()