目录

• 1. 需要用的的库
• 2. 加载网络
• 3. 加载图片
• 4. 可视化结果

完成第一篇darknet环境配置后，可基于python进行YOLOv4的inference，查看YOLOv4的检测结果。需要用到的库有darknet目录下的darknet.py文件，以及编译出来的libdarknet.so。

1. 需要用的的库

import osimport cv2import numpy as npimport randomimport darknet

需要导入的库主要是opencv-python和darknet，darknet即darknet.py文件。

2. 加载网络

加载网络需要读取cfg文件，weights文件，data文件和names文件，如果缺少文件，会报读取失败的错误。yolov4.cfg和coco.data文件可以在darknet文件夹下的cfg文件夹中找到，coco.names文件可以在data文件夹中找到，yolov4.weights文件可以在这篇文章中介绍的网盘中下载。

netMain = NonemetaMain = NonealtNames = NoneconfigPath = "./cfg/yolov4.cfg"weightPath = "./weights/yolov4.weights"metaPath = "./data/coco.data"if not os.path.exists(configPath):    raise ValueError("Invalid config path `" + os.path.abspath(configPath)+"`")if not os.path.exists(weightPath):    raise ValueError("Invalid weight path `" + os.path.abspath(weightPath)+"`")if not os.path.exists(metaPath):    raise ValueError("Invalid data file path `" + os.path.abspath(metaPath)+"`")if netMain is None:    netMain = darknet.load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1)  # batch size = 1if metaMain is None:    metaMain = darknet.load_meta(metaPath.encode("ascii"))if altNames is None:    try:        with open(metaPath) as metaFH:            metaContents = metaFH.read()            import re            match = re.search("names *= *(.*)#34;, metaContents, re.IGNORECASE | re.MULTILINE)            if match:                result = match.group(1)            else:                result = None            try:                if os.path.exists(result):                    with open(result) as namesFH:                        namesList = namesFH.read().strip().split("
")                        altNames = [x.strip() for x in namesList]            except TypeError:                pass    except Exception:        pass

3. 加载图片

首先通过opencv读取图片，读取图片之后需要将图片由BGR格式转为RGB格式，接着resize到608x608大小并将图片转为darknet的IMAGE类型，输入到网络之后，检测结果保存在detections里面。

image_name = './data/dog.jpg'src_img = cv2.imread(image_name)bgr_img = src_img[:, :, ::-1]height, width = bgr_img.shape[:2]rsz_img = cv2.resize(bgr_img, (darknet.network_width(netMain), darknet.network_height(netMain)),                     interpolation=cv2.INTER_LINEAR)darknet_image, _ = darknet.array_to_image(rsz_img)detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25)

4. 可视化结果

需要先将检测结果转为x1y1x2y2的形式，再使用opencv的rectangle函数画图。

# convert xywh to xyxydef convert_back(x, y, w, h):    xmin = int(round(x - (w / 2)))    xmax = int(round(x + (w / 2)))    ymin = int(round(y - (h / 2)))    ymax = int(round(y + (h / 2)))    return xmin, ymin, xmax, ymax# Plotting functionsdef plot_one_box(x, img, color=None, label=None, line_thickness=None):    # Plots one bounding box on image img    tl = line_thickness or round(0.001 * max(img.shape[0:2])) + 1  # line thickness    color = color or [random.randint(0, 255) for _ in range(3)]    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))    cv2.rectangle(img, c1, c2, color, thickness=tl)    if label:        tf = max(tl - 1, 1)  # font thickness        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3        cv2.rectangle(img, c1, c2, color, -1)  # filled        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)random.seed(1)colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(metaMain.classes)]for detection in detections:    x, y, w, h = detection[2][0], \                 detection[2][1], \                 detection[2][2], \                 detection[2][3]    conf = detection[1]    x *= width / darknet.network_width(netMain)    w *= width / darknet.network_width(netMain)    y *= height / darknet.network_height(netMain)    h *= height / darknet.network_height(netMain)    xyxy = np.array([x - w / 2, y - h / 2, x + w / 2, y + h / 2])    label = detection[0].decode()    index = altNames.index(label)    label = f'{label} {conf:.2f}'    plot_one_box(xyxy, src_img, label=label, color=colors[index % metaMain.classes])cv2.imwrite('result.jpg', src_img)

最终结果保存在result.jpg图片里，结果如下图所示。

结果显示图片中dog的置信度为0.98，bicycle的置信度为0.92，truck的置信度为0.92，pottedplant的置信度没有显示，通过运行代码可知为0.34。通过darknet命令./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights -thresh 0.25 ./data/dog.jpg运行的结果如下，结果基本一致。

data/dog.jpg: Predicted in 21.041000 milli-seconds.bicycle: 92%dog: 98%truck: 92%pottedplant: 33%