电子商务网站建设概述/在线观看的seo综合查询

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

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# 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)​