Python編寫 利用OpenCV將多組圖像拼接後製作成視頻

參考OpenCV製作視頻，自己加了一些圖像拼接的代碼段。

from os.path import isfile, join
from imutils import paths
import numpy as np
import random
import imutils
import cv2
import os
import matplotlib.pyplot as plt

# define the aspect aware prepeocessor class
class AspectAwarePreprocessor:
    def __init__(self, width, height, inter=cv2.INTER_AREA):
        # store the target image width, height, and interpolation
        # method used when resizing
        self.width = width
        self.height = height
        self.inter = inter

    def preprocess(self, image):
        # grab the dimensions of the image and then initialize
        # the deltas to use when cropping
        (h, w) = image.shape[:2]
        dW = 0
        dH = 0
        # if the width is smaller than the height, then resize
        # along the width (i.e., the smaller dimension) and then
        # update the deltas to crop the height to the desired
        # dimension
        if w < h:
            image = imutils.resize(image, width=self.width,
                                   inter=self.inter)
            dH = int((image.shape[0] - self.height) / 2.0)

        # otherwise, the height is smaller than the width so
        # resize along the height and then update the deltas
        # crop along the width
        else:
            image = imutils.resize(image, height=self.height,
                                   inter=self.inter)
            dW = int((image.shape[1] - self.width) / 2.0)

        # now that our images have been resized, we need to
        # re-grab the width and height, followed by performing
        # the crop

        (h, w) = image.shape[:2]
        image = image[dH:h - dH, dW:w - dW]

        # finally, resize the image to the provided spatial
        # dimensions to ensure our output image is always a fixed
        # size
        return cv2.resize(image, (self.width, self.height),
                          interpolation=self.inter)


def convert_frames_to_video(pathIn, pathOut, fps):
    # initialize the frame, width, and height array
    frame_array = []
    width_array = []
    height_array = []
    left_files = list(paths.list_images(pathIn+'left/'))  # 待拼接圖像1
    left_files.sort()
    right_files = list(paths.list_images(pathIn + 'right/'))  # 待拼接圖像2  
    right_files.sort()
    predicted_files = list(paths.list_images(pathIn + 'predicted/'))  # 待拼接圖像3
    predicted_files.sort()

    assert len(left_files)==len(right_files)
    assert len(left_files) == len(predicted_files)

    print('length of files is ', len(left_files))

    # initialize the image preprocessor and the target size
    aap = AspectAwarePreprocessor(1500, 2500)
    size = (1500, 2500)
    for i in range(len(left_files)):
        # filename=pathIn + files[i]
        # reading each files
        left_img = cv2.cvtColor(cv2.imread(left_files[i]), cv2.COLOR_BGR2RGB)
        right_img = cv2.cvtColor(cv2.imread(right_files[i]), cv2.COLOR_BGR2RGB)
        predicted_img = cv2.cvtColor(cv2.imread(predicted_files[i]), cv2.COLOR_BGR2RGB)

        plt.figure(figsize=(25, 15))

        plt.gca().xaxis.set_major_locator(plt.NullLocator())
        plt.gca().yaxis.set_major_locator(plt.NullLocator())
        # plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.subplots_adjust(hspace=0, wspace=0)
        plt.margins(0, 0)

        plt.subplot(311)
        plt.axis('off')
        plt.imshow(left_img)
        plt.title('NJUST on Cityscapes left image')

        plt.subplot(312)
        plt.axis('off')
        plt.imshow(right_img)
        plt.title('NJUST on Cityscapes right image')

        plt.subplot(313)
        plt.axis('off')
        plt.imshow(predicted_img)
        plt.title('NJUST on Cityscapes predicted image')

        plt.tight_layout()  # 去掉空白區域
        # plt.savefig('./NJUST_Experiments.png', bbox_inches='tight')
        plt.savefig('./NJUST_Experiments.png')
        plt.clf()
        plt.cla()
        plt.close("all")

        img = cv2.imread('./NJUST_Experiments.png')  # 暫存臨時圖片

        height, width, channels = img.shape

        # resize the image
        img = aap.preprocess(img)

        # save image to a certain directory
        f = left_files[i]
        file_name, file_ext = os.path.splitext(f)
        file_num = '{}'.format(i)
        file_num = file_num.zfill(5)
        new_name = 'demo' + file_num + file_ext

        path = '/data/zd/video_images/'

        cv2.imwrite(os.path.join(path, new_name), img)

        if i % 10 == 0:
            print("[INFO] Processing {}th image".format(i))

        # inserting the frames into an image array

        frame_array.append(img)
        height_array.append(height)
        width_array.append(width)

    print('pathOut is ', pathOut)
    out = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'mp4v'), fps, size)

    print('Process finished, begin writing ...')
    for i in range(len(frame_array)):
        # writing to a image array
        out.write(frame_array[i])
    out.release()
    print('Done!')


def main():
    pathIn = '/data/zd/binocular-seg/val_results/'  # 輸入圖像的路徑
    pathOut = '/data/zd/binocular-seg/binocular_seg_video.mp4'  # 輸出視頻的路徑

    fps = 2.0
    convert_frames_to_video(pathIn, pathOut, fps)


if __name__ == "__main__":
    main()

效果示意：