使用Adobe Premiere CS3 添加视频特效与编辑

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opencv_python-3.3.0+contrib-cp36-cp36m-win_amd64.whl

# Basic options parser.add_argument("-style_image", help="Style target image", default='examples/inputs/seated-nude.jpg') parser.add_argument("-style_blend_weights", default=None) parser.add_argument("-content_image", help="Content target image", default='examples/inputs/tubingen.jpg') parser.add_argument("-image_size", help="Maximum height / width of generated image", type=int, default=450) parser.add_argument("-gpu", help="Zero-indexed ID of the GPU to use; for CPU mode set -gpu = c", default=0) # Optimization options parser.add_argument("-content_weight", type=float, default=5e0) parser.add_argument("-style_weight", type=float, default=1e2) parser.add_argument("-normalize_weights", action='store_true') parser.add_argument("-normalize_gradients", action='store_true') parser.add_argument("-tv_weight", type=float, default=1e-3) parser.add_argument("-num_iterations", type=int, default=1000) parser.add_argument("-init", choices=['random', 'image'], default='random') parser.add_argument("-init_image", default=None) parser.add_argument("-optimizer", choices=['lbfgs', 'adam'], default='lbfgs') parser.add_argument("-learning_rate", type=float, default=1e0) parser.add_argument("-lbfgs_num_correction", type=int, default=100) # Output options parser.add_argument("-print_iter", type=int, default=50) parser.add_argument("-save_iter", type=int, default=100) parser.add_argument("-output_image", default='out.png')这段代码是什么意思？

from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser('create image pairs') parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A', type=str, default='../dataset/50kshoes_edges') parser.add_argument('--fold_B', dest='fold_B', help='input directory for image B', type=str, default='../dataset/50kshoes_jpg') parser.add_argument('--fold_AB', dest='fold_AB', help='output directory', type=str, default='../dataset/test_AB') parser.add_argument('--num_imgs', dest='num_imgs', help='number of images',type=int, default=1000000) parser.add_argument('--use_AB', dest='use_AB', help='if true: (0001_A, 0001_B) to (0001_AB)',action='store_true') args = parser.parse_args() for arg in vars(args): print('[%s] = ' % arg, getattr(args, arg)) splits = os.listdir(args.fold_A) for sp in splits: img_fold_A = os.path.join(args.fold_A, sp) img_fold_B = os.path.join(args.fold_B, sp) img_list = os.listdir(img_fold_A) if args.use_AB: img_list = [img_path for img_path in img_list if '_A.' in img_path] num_imgs = min(args.num_imgs, len(img_list)) print('split = %s, use %d/%d images' % (sp, num_imgs, len(img_list))) img_fold_AB = os.path.join(args.fold_AB, sp) if not os.path.isdir(img_fold_AB): os.makedirs(img_fold_AB) print('split = %s, number of images = %d' % (sp, num_imgs)) for n in range(num_imgs): name_A = img_list[n] path_A = os.path.join(img_fold_A, name_A) if args.use_AB: name_B = name_A.replace('_A.', '_B.') else: name_B = name_A path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) im_B = cv2.imread(path_B, cv2.IMREAD_COLOR) im_AB = np.concatenate([im_A, im_B], 1) cv2.imwrite(path_AB, im_AB)，解释上述代码，并告诉我怎么设置文件夹格式

”img/Open-Knit_High-Slit_Maxi_Dress/img_00000008.jpg 41 “用python将这个字符串用空格拆分成name和id两部分

data-hover_modal 和data-click_modal使用例子

valuation Pretrained models from the model zoo can be evaluated using the `eval.py` script. For example, the model on cifar-10 can be evaluated as follows: ```shell python eval.py --config_exp configs/scan/scan_cifar10.yml --model $MODEL_PATH ``` Visualizing the prototype images is easily done by setting the `--visualize_prototypes` flag. For example on cifar-10:

Similarly, you might want to have a look at the clusters found on ImageNet (as shown at the top). First download the model (link in table above) and then execute the following command: ```shell python eval.py --config_exp configs/scan/wjd_eval.yml --model $MODEL_PATH_IMAGENET ```，这段话说了啥，能解释一下吗

<template> <el-carousel :interval="4000" type="card" height="200px"> <el-carousel-item v-for="item in 6" :key="item">

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import argparse import matplotlib import matplotlib.pyplot as plt from colorizers import * matplotlib.use('TKAgg') parser = argparse.ArgumentParser() parser.add_argument('-i','--img_path', type=str, default='imgs/ansel_adams3.jpg') parser.add_argument('--use_gpu', action='store_true', help='whether to use GPU') parser.add_argument('-o','--save_prefix', type=str, default='saved', help='will save into this file with {eccv16.png, siggraph17.png} suffixes') opt = parser.parse_args()