def get_training_data_set():
'''
获得训练数据集
'''
image_loader = ImageLoader('train-images.idx3-ubyte', 60000)
label_loader = ImageLoader('train-labels.idx1-ubyte', 60000)
return image_loader.load(), label_loader.load()
def get_test_data_set():
'''
获得测试数据集
'''
image_loader = ImageLoader('t10k-images.idx3-ubyte', 10000)
label_loader = ImageLoader('t10k-labels.idx1-ubyte', 10000)
return image_loader.load(), label_loader.load()
def __init__(self, dim, start_url):
self.start_url = start_url
self.url_frontier = []
self.url_history = []
self.img_urls_history = []
self.image_loader = il.ImageLoader(dim)
# header object, necessary to get proper response when requesting urls
self.header = {
'User-Agent':
'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',
'Accept':
'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',
'Referer': 'https://cssspritegenerator.com',
'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',
'Accept-Encoding': 'none',
'Accept-Language': 'en-US,en;q=0.8',
'Connection': 'keep-alive'
}
from ImageLoader import *
Width = 416 #Width of network's input image
Height = 416 #Height of network's input image
# Give the configuration and weight files for the model and load the network using them.
modelConfiguration = "yolov3.cfg"
modelWeights = "yolov3.weights"
net = cv.dnn.readNetFromDarknet(modelConfiguration, modelWeights)
net.setPreferableBackend(cv.dnn.DNN_BACKEND_OPENCV)
net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
dataset = ImageLoader()
loader = DataLoader(dataset,
batch_size=256,
shuffle=False,
num_workers=(8 if device == "cuda" else 0))
for idx, (img_names, img_IDs) in enumerate(loader):
start = time.time()
img_IDs = img_IDs.numpy()
imgs = []
for f in img_names:
f = os.path.join('data/images/train2014/', f)
if not os.path.isfile(f):
print("Input image file ", args.image, " doesn't exist")
sys.exit(1)
img = cv.imread(f)
import Scraper
import ImageLoader as il
import ImageRankerNN as ir
import numpy as np
query = 'car'
dim = 224
start_url = "https://www.google.com/search?q=%s&source=lnms&tbm=isch" % query
scraper = Scraper.GoogleScraper(224, start_url)
image_loader = il.ImageLoader(dim=dim)
# image_ranker = ir.Ranker_NN(1, 1000, 500)
# our_model = image_ranker.convolutional_neural_network()
i = 1
while i < 4:
img_urls = scraper.parseNextURL()
imgs, urls = image_loader.loadImages(img_urls)
number_correct, number_all, target_array = image_loader.sort_images(imgs)
scraper.appendURLFrontier(urls, target_array)
i += 1
