人工智能：OpenCV结合YOLO3目标检测自然世界中的物体，Python实现

柔情只为你懂 2023-05-30 06:12 3阅读 0赞

# AI：OpenCV结合YOLO3目标检测自然世界中的物体，Python实现 #

YOLO (You Only Look Once) 。YOLO的工作原理示意图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70][]

使用yolo做AI目标检测，可使用yolo做好的模型和数据，首先需要到yolo v3的官方github下载cfg，weight，和name分类器。下载连接：

https://pjreddie.com/media/files/yolov3.weights  
https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg  
https://github.com/pjreddie/darknet/blob/master/data/coco.names

yolov3已经为开发者训练好一些数据模型了，直接yolov3.weights。coco.names已经做好的分类有80种物体。yolov3.cfg定义了网络结构，yolov3.weights是预训练权重。下载完成后，把上述三个文件放到一个目录文件夹下，接下来就可以用yolo v3训练好的数据进行目标检测了。

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 1][]

本例使用OpenCV的卷积神经网络（CNN）模型和一个预训练（pre-trained）模型，让机器检测人类世界中的80种类物体。

Python代码：

import numpy as np
    import cv2 as cv
    
    
    # 通常，序列CNN网络在最终只会给出一个输出结果，
    # 在YOLO v3版本中，会输出多个预测层。每一个输出的预测层不与任何下一个层连接。
    def get_output_layers(net):
        layer_names = net.getLayerNames()
        output_layers = [layer_names[arr[0] - 1] for arr in net.getUnconnectedOutLayers()]
        return output_layers
    
    
    # 画框和分类文字。
    def draw_rec(image, x, y, width, height, color, label, number):
        cv.rectangle(img=image, pt1=(x, y), pt2=(x + width, y + height), color=color[0], thickness=2)
        text = "{}:{:.3f}".format(label, number)
        cv.putText(image, text, (x, y - 5), cv.FONT_HERSHEY_COMPLEX, fontScale=0.5, color=color, thickness=1)
    
    
    if __name__ == '__main__':
        weightsPath = "E:/code/python/yolo/yolov3.weights"
        configPath = "E:/code/python/yolo/yolov3.cfg"
        labelsPath = "E:/code/python/yolo/coco.names"
        imagePath = "./pic.jpg"
    
        conf_threshold = 0.5
        nms_threshold = 0.4
    
        LABELS = open(labelsPath).read().strip().split("\n")
        COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8")
    
        net = cv.dnn.readNetFromDarknet(configPath, weightsPath)
        net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
        net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
    
        # 待检测的图像.
        image = cv.imread(imagePath)
        (H, W) = image.shape[:2]
    
        scale = 1 / 255
    
        blob = cv.dnn.blobFromImage(image, scale, size=(416, 416), swapRB=True, crop=False)
        net.setInput(blob)
    
        outputs = net.forward(get_output_layers(net))
    
        boxes = []
        confidences = []
        classIDs = []
    
        # 循环处理每个输出的预测层。
        for output in outputs:
            for detection in output:
                scores = detection[5:]
                classID = np.argmax(scores)
                confidence = scores[classID]
    
                # 过滤置信度较小的检测结果
                if confidence > 0.5:
                    confidences.append(float(confidence))
                    classIDs.append(classID)
    
                    # 框的宽度和高度
                    box = detection[0:4] * np.array([W, H, W, H])
    
                    (centerX, centerY, width, height) = box.astype("int")
    
                    x = int(centerX - (width / 2))
                    y = int(centerY - (height / 2))
    
                    boxes.append([x, y, int(width), int(height)])
    
        # 最大值抑制。
        idxs = cv.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)
    
        print(boxes)
        if len(idxs) > 0:
            for i in idxs.flatten():
                (x, y) = (boxes[i][0], boxes[i][1])
                (w, h) = (boxes[i][2], boxes[i][3])
    
                # 原图上绘制边框和分类.
                color = [int(c) for c in COLORS[classIDs[i]]]
                # cv.rectangle(img=image, pt1=(x, y), pt2=(x + w, y + h), color=color, thickness=1)
                # text = "{}:{:.3f}".format(LABELS[classIDs[i]], confidences[i])
                # cv.putText(image, text, (x, y - 5), cv.FONT_HERSHEY_COMPLEX, 0.5, color=color, thickness=1)
                draw_rec(image, x, y, w, h, color, LABELS[classIDs[i]], confidences[i])
    
        cv.imshow("image", image)
        cv.waitKey(0)

pic.jpg原图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 2][]

程序运行后，进行目标检测，把每一类检测结果用框框起来，并标出该类物体的名称，结果如下：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 3][]

AI检测出三种物体：人（person），马（horse），和狗（dog）。

再来一张合照照片，原图：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 4][]

运行程序，对上图进行目标检测，AI识别出两类物体，人（person）和领带（tie）：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 5][]

这里解释一下为什么要在程序代码中使用卷积神经网络(CNN)的最大值（极大值）抑制对输出层进行二次过滤：

# 最大值抑制。
        idxs = cv.dnn.NMSBoxes(boxes, confidences, conf_threshold, nms_threshold)

虽然经过前期的目标检测，程序代码已经把置信度低于0.5的层去除掉，但是仍很大可能存在互相重叠的检测结果层。如图所示：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 6][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 7][]

上面这两张图，左边：未经过CNN（卷积神经网络）做最大值抑制，右侧：经过CNN最大值抑制处理的。

本例基于Python3.7.4。OpenCV4.1.1.26。yolo v3。

何为coco.names？

> This particular model is trained on COCO dataset (common objects in context) from Microsoft. It is capable of detecting 80 common objects.

coco.names涵盖的80种分类：

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

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[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA_size_16_color_FFFFFF_t_70 1]: https://img-blog.csdnimg.cn/20191107185601195.png?x-oss-process=image/watermark,type_ZmFuZ3poZW5naGVpdGk,shadow_10,text_aHR0cHM6Ly96aGFuZ3BoaWwuYmxvZy5jc2RuLm5ldA==,size_16,color_FFFFFF,t_70
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