爬虫与反爬虫技术简介

if __name__ == '__main__':    url = 'http://www.porters.vip/confusion/flight.html'    resp = requests.get(url)    sel = Selector(resp.text)    em = sel.css('em.rel').extract()    for element in range(0,1):        element = Selector(em[element])        element_b = element.css('b').extract()        b1 = Selector(element_b.pop(0))        base_price = b1.css('i::text').extract()        print('css偏移前的价格：',base_price)         alternate_price = []        for eb in element_b:            eb = Selector(eb)            style = eb.css('b::attr("style")').get()            position = ''.join(re.findall('left:(.*)px', style))            value = eb.css('b::text').get()            alternate_price.append({'position': position, 'value': value})        print('css偏移值：',alternate_price)         for al in alternate_price:            position = int(al.get('position'))            value = al.get('value')            plus = True if position >= 0 else False            index = int(position / 16)            base_price[index] = value        print('css偏移后的价格：',base_price)

for member in root.findall('object'):           class_id = class_text.index(member[0].text)            xmin = int(member[4][0].text)           ymin = int(member[4][1].text)           xmax = int(member[4][2].text)           ymax = int(member[4][3].text)            # round(x, 6) 这里我设置了6位有效数字，可根据实际情况更改           center_x = round(((xmin + xmax) / 2.0) * scale / float(image.shape[1]), 6)           center_y = round(((ymin + ymax) / 2.0) * scale / float(image.shape[0]), 6)           box_w = round(float(xmax - xmin) * scale / float(image.shape[1]), 6)           box_h = round(float(ymax - ymin) * scale / float(image.shape[0]), 6)            file_txt.write(str(class_id))           file_txt.write(' ')           file_txt.write(str(center_x))           file_txt.write(' ')           file_txt.write(str(center_y))           file_txt.write(' ')           file_txt.write(str(box_w))           file_txt.write(' ')           file_txt.write(str(box_h))           file_txt.write('\n')        file_txt.close()

parser = argparse.ArgumentParser()parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path')parser.add_argument('--cfg', type=str, default='./models/yolov5s.yaml', help='model.yaml path')parser.add_argument('--data', type=str, default='data/custom.yaml', help='data.yaml path')parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path')# parser.add_argument('--epochs', type=int, default=300)parser.add_argument('--epochs', type=int, default=50)# parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs')parser.add_argument('--batch-size', type=int, default=8, help='total batch size for all GPUs')parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')parser.add_argument('--rect', action='store_true', help='rectangular training')parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')parser.add_argument('--notest', action='store_true', help='only test final epoch')parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers')parser.add_argument('--project', default='runs/train', help='save to project/name')parser.add_argument('--entity', default=None, help='W&B entity')parser.add_argument('--name', default='exp', help='save to project/name')parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')parser.add_argument('--quad', action='store_true', help='quad dataloader')parser.add_argument('--linear-lr', action='store_true', help='linear LR')parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')parser.add_argument('--upload_dataset', action='store_true', help='Upload dataset as W&B artifact table')parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval for W&B')parser.add_argument('--save_period', type=int, default=-1, help='Log model after every "save_period" epoch')parser.add_argument('--artifact_alias', type=str, default="latest", help='version of dataset artifact to be used')opt = parser.parse_args()