[Python+OpenCV项目实战]——OpenCV的DNN模块

Myth丶恋晨 2022-11-26 07:59 333阅读 0赞

### 文章目录 ###

*  一、代码
 *   *  1.1 utils\_paths.py
     *  1.2 utils\_paths.py
 *  二、结果

代码目录：  
![在这里插入图片描述][20200811114144270.png]

# 一、代码 #

## 1.1 utils\_paths.py ##

import os
    
    
    image_types = (".jpg", ".jpeg", ".png", ".bmp", ".tif", ".tiff")
    
    
    def list_images(basePath, contains=None):
        # return the set of files that are valid
        return list_files(basePath, validExts=image_types, contains=contains)
    
    
    def list_files(basePath, validExts=None, contains=None):
        # loop over the directory structure
        for (rootDir, dirNames, filenames) in os.walk(basePath):
            # loop over the filenames in the current directory
            for filename in filenames:
                # if the contains string is not none and the filename does not contain
                # the supplied string, then ignore the file
                if contains is not None and filename.find(contains) == -1:
                    continue
    
                # determine the file extension of the current file
                ext = filename[filename.rfind("."):].lower()
    
                # check to see if the file is an image and should be processed
                if validExts is None or ext.endswith(validExts):
                    # construct the path to the image and yield it
                    imagePath = os.path.join(rootDir, filename)
                    yield imagePath

## 1.2 utils\_paths.py ##

# 导入工具包
    import utils_paths
    import numpy as np
    import cv2
    
    # 标签文件处理
    rows = open("synset_words.txt").read().strip().split("\n")
    classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]
    
    # Caffe所需配置文件
    net = cv2.dnn.readNetFromCaffe("bvlc_googlenet.prototxt",
    	"bvlc_googlenet.caffemodel")
    
    # 图像路径
    imagePaths = sorted(list(utils_paths.list_images("images/")))
    
    # 图像数据预处理
    image = cv2.imread(imagePaths[0])
    resized = cv2.resize(image, (224, 224))
    # image scalefactor size mean swapRB 
    blob = cv2.dnn.blobFromImage(resized, 1, (224, 224), (104, 117, 123))
    print("First Blob: {}".format(blob.shape))
    
    # 得到预测结果
    net.setInput(blob)
    preds = net.forward()
    
    # 排序，取分类可能性最大的
    idx = np.argsort(preds[0])[::-1][0]
    text = "Label: {}, {:.2f}%".format(classes[idx],
    	preds[0][idx] * 100)
    cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
    	0.7, (0, 0, 255), 2)
    
    # 显示
    cv2.imshow("Image", image)
    cv2.waitKey(0)
    
    # Batch数据制作
    images = []
    
    # 方法一样，数据是一个batch
    for p in imagePaths[1:]:
    	image = cv2.imread(p)
    	image = cv2.resize(image, (224, 224))
    	images.append(image)
    
    # blobFromImages函数，注意有s
    blob = cv2.dnn.blobFromImages(images, 1, (224, 224), (104, 117, 123))
    print("Second Blob: {}".format(blob.shape))
    
    # 获取预测结果
    net.setInput(blob)
    preds = net.forward()
    for (i, p) in enumerate(imagePaths[1:]):
    	image = cv2.imread(p)
    	idx = np.argsort(preds[i])[::-1][0]
    	text = "Label: {}, {:.2f}%".format(classes[idx],
    		preds[i][idx] * 100)
    	cv2.putText(image, text, (5, 25),  cv2.FONT_HERSHEY_SIMPLEX,
    		0.7, (0, 0, 255), 2)
    	cv2.imshow("Image", image)
    	cv2.waitKey(0)

# 二、结果 #

![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]  
![在这里插入图片描述][watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 2]

[20200811114144270.png]: /images/20221124/12df3d6ad59b4bf4805f8a6838c71d7e.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70]: /images/20221124/027fb30263334baba207af5d437fa57c.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 1]: /images/20221124/1c36d675ffbb46e6a70c4f698ad6d273.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3dhMXR6eQ_size_16_color_FFFFFF_t_70 2]: /images/20221124/21ec3a0390f04087aac596338e40b11b.png