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extract/1.py

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+import cv2
+import os
+# 其它格式的图片也可以
+img_array = []
+path = "C:/Users/hasee/Downloads/Video/extract/image/"  # 图片文件路径 # 获取该目录下的所有文件名
+for i in range(250):
+    #挨个读取图片
+    img = cv2.imread(path+str(i)+".jpg")
+    #获取图片高，宽，通道数信息
+    height, width, layers = img.shape
+    #设置尺寸
+    size = (width, height)
+    #将图片添加到一个大“数组中”
+    img_array.append(img)
+print("this is ok")
+# avi：视频类型，mp4也可以
+# cv2.VideoWriter_fourcc(*'DIVX')：编码格式，不同的编码格式有不同的视频存储类型
+# fps：视频帧率
+# size:视频中图片大小
+fps=25
+videopath='C:/Users/hasee/Downloads/Video/extract/test10.avi'#图片保存地址及格式
+out1 = cv2.VideoWriter(videopath,cv2.VideoWriter_fourcc(*'DIVX'),fps, size)
+for i in range(len(img_array)):
+    #写成视频操作
+    out1.write(img_array[i])
+out1.release()
+print("all is ok")
+

extract/2.py

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+import cv2
+
+vc = cv2.VideoCapture("../v_c_0_10.mp4")
+
+if vc.isOpened():
+    ret, frame = vc.read()
+else:
+    ret = False
+
+
+# loop read video frame
+i=0
+while ret:
+    ret, frame = vc.read()
+    if i<250:
+        image_path="./image/"+str(i)+".jpg"
+        cv2.imwrite(image_path, frame)
+    i=i+1
+    cv2.waitKey(40)

extract/getim.py

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+from __future__ import division
+from PIL import Image
+import argparse
+import torch
+import torchvision
+import torch.nn as nn
+import numpy as np
+import torchvision.utils as vutils
+import matplotlib.pyplot as plt
+import numpy as np
+import cv2
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+
+def remove_the_blackborder(image_p):
+    image = cv2.imread(image_p,cv2.IMREAD_COLOR)  # 读取图片
+    img = cv2.medianBlur(image, 5)  # 中值滤波，去除黑色边际中可能含有的噪声干扰
+    b = cv2.threshold(img, 3, 255, cv2.THRESH_BINARY)  # 调整裁剪效果
+    binary_image = b[1]  # 二值图--具有三通道
+    binary_image = cv2.cvtColor(binary_image, cv2.COLOR_BGR2GRAY)
+    try:
+        edges_y, edges_x = np.where(binary_image == 255)  ##h, w
+        bottom = min(edges_y)
+        top = max(edges_y)
+        height = top - bottom
+
+        left = min(edges_x)
+        right = max(edges_x)
+        height = top - bottom
+        width = right - left
+
+        res_image = image[bottom:bottom + height, left:left + width]
+
+    except:
+        res_image=image
+        print(image_p)
+    return res_image
+
+
+
+def load_image(image_path, transform=None, max_size=None, shape=None):
+    # image=Image.fromarray(image)
+    # image = cv2.imread(image_path)
+    image = Image.open(image_path)
+    #image = Image.open(image_path)  # 读入图片， 下面是一些图片的预处理操作
+    if max_size:
+        scale = max_size / max(image.size)
+        size = np.array(image.size) * scale
+        # print(size)         [400.         306.78733032]
+        image = image.resize(size.astype(int), Image.ANTIALIAS)  # 改变图片大小
+
+    if shape:
+        image = image.resize(shape, Image.LANCZOS)  # Image.LANCZOS是插值的一种方式
+
+    if transform:
+        # print(image)         # PIL的JpegImageFile格式(size=(W，H))
+        image = transform(image).unsqueeze(0)
+        # print(image.shape)   #   [C, H, W]
+    return image
+
+
+
+# fcontent = load_image("./image/0.jpg", transform, shape=[256, 256])
+# for i in range(249):
+#     name="./image/"+str(i+1)+".jpg"
+#     content = load_image(name, transform, shape=[256, 256])
+#     fcontent=torch.cat((fcontent,content),-4)
+#     print(fcontent.shape)