# coding:utf-8 import numpy as np import cv2 import os import time t1 = time.time() # 加载已经训练好的模型 weightsPath = r'C:\Users\Administrator\Desktop\my_yolov3_10000.weights' # 权重文件 configPath = r"my_yolov3.cfg" # 模型配置文件 labelsPath = r"myData.names" # 模型类别标签文件 # 初始化一些参数 LABELS = open(labelsPath).read().strip().split("\n") # 物体类别 COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),dtype="uint8") # 颜色 boxes = [] confidences = [] classIDs = [] net = cv2.dnn.readNetFromDarknet(configPath, weightsPath) # 读入待检测的图像 image=cv2.imread(r"C:\Users\Administrator\Desktop\p4.jpg") (H, W) = image.shape[:2] # 得到 YOLO 需要的输出层 ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # 从输入图像构造一个blob,然后通过加载的模型,给我们提供边界框和相关概率 blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),swapRB=True, crop=False) net.setInput(blob) layerOutputs = net.forward(ln) # 在每层输出上循环 for output in layerOutputs: # 对每个检测进行循环 for detection in output: scores = detection[5:] classID = np.argmax(scores) confidence = scores[classID] # 过滤掉那些置信度较小的检测结果 if confidence > 0.5: # 框后接框的宽度和高度 box = detection[0:4] * np.array([W, H, W, H]) (centerX, centerY, width, height) = box.astype("int") # 边框的左上角 x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # 更新检测出来的框 boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) classIDs.append(classID) # 极大值抑制 idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.2, 0.3) if len(idxs) > 0: for i in idxs.flatten(): print(LABELS[classIDs[i]]) # if LABELS[classIDs[i]] == "person": (x, y) = (boxes[i][0], boxes[i][1]) (w, h) = (boxes[i][2], boxes[i][3]) # 在原图上绘制边框和类别 color = [int(c) for c in COLORS[classIDs[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color, 2) text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i]) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2) t2 = time.time() print(int(t2 -t1)) cv2.namedWindow('Image', cv2.WINDOW_NORMAL) cv2.imshow("Image", image) cv2.waitKey(0)
opencv调用yolo3
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