169 lines
4.6 KiB
Python
169 lines
4.6 KiB
Python
import ffmpeg
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import numpy as np
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import matplotlib
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import cv2
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import os
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import sys
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from matplotlib import pyplot as plt
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def openVid():
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fileName = input("video path:")
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while not os.path.isfile(fileName):
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print("file doesn't exist!")
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fileName = input("video path:")
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cap = cv2.VideoCapture(fileName)
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if not cap.isOpened():
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print("Video cannot be opened.")
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sys.exit()
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else:
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return cap
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def getFrameCount(cap):
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if not cap.isOpened():
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print("Video cannot be opened.")
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sys.exit()
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else:
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return cap.get(cv2.CAP_PROP_FRAME_COUNT)
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def getFrameRate(cap):
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if not cap.isOpened():
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print("Video cannot be opened.")
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sys.exit()
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else:
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return cap.get(cv2.CAP_PROP_FPS)
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if __name__ == "__main__":
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cap = openVid()
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frameRate = getFrameRate(cap)
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frameCount = getFrameCount(cap)
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for frameID in range(int(frameRate), int(frameCount), int(frameRate*100)):
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cap.set(cv2.CAP_PROP_POS_FRAMES, frameID)
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isSuccess, img = cap.read()
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if isSuccess:
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cv2.namedWindow("img", cv2.WINDOW_NORMAL)
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cv2.imshow('img', img)
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imgL = np.split(img, 2, 1)[0]
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imgR = np.split(img, 2, 1)[1]
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stereo = cv2.StereoSGBM_create(numDisparities=96, blockSize=11)
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disparity = stereo.compute(imgL, imgR)
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plt.title("DepthMap")
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plt.imshow(disparity)
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plt.pause(0.5)
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# ffmpeg.input("./vid/avatar.mkv")
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# Motion Vector
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#cap = cv2.VideoCapture('./vid/zootopia.mkv')
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# for i in range(1,10000):
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# cap.read()
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# params for ShiTomasi corner detection
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# feature_params = dict( maxCorners = 100,
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# qualityLevel = 0.3,
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# minDistance = 7,
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# blockSize = 7 )
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# Parameters for lucas kanade optical flow
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# lk_params = dict( winSize = (15,15),
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# maxLevel = 2,
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# criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03))
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# Create some random colors
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#color = np.random.randint(0,255,(100,3))
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# Take first frame and find corners in it
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#ret, old_frame = cap.read()
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#old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY)
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#p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params)
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# Create a mask image for drawing purposes
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#mask = np.zeros_like(old_frame)
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# while(1):
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# ret,frame = cap.read()
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# frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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# # calculate optical flow
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# p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params)
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# # Select good points
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# good_new = p1[st==1]
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# good_old = p0[st==1]
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# # draw the tracks
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# for i,(new,old) in enumerate(zip(good_new,good_old)):
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# a,b = new.ravel()
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# c,d = old.ravel()
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# mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2)
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# frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1)
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# img = cv2.add(frame,mask)
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# cv2.imshow('frame',img)
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# k = cv2.waitKey(30) & 0xff
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# if k == 27:
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# break
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# # Now update the previous frame and previous points
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# old_gray = frame_gray.copy()
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# p0 = good_new.reshape(-1,1,2)
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# cv2.destroyAllWindows()
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# cap.release()
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# 中文文件名无法识别
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# imgDirs = os.listdir("./pic_en")
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# def read_frame_as_jpeg(in_filename, frame_num):
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# out, err = (
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# ffmpeg
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# .input(in_filename)
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# .filter('select', 'gte(n,{})'.format(frame_num))
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# .output('pipe:', vframes=1, format='image2', vcodec='mjpeg')
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# .run(capture_stdout=True)
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# )
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# return out
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# ffmpeg.input("./vid/venom.mkv")
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# ffmpeg.
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#img = read_frame_as_jpeg("./vid/venom.mkv", 648)
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# print(type(img))
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#img = cv2.imdecode(img,0)
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# print(img)
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# cv2.imshow("img",img)
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#imgL = np.split(img, 2, 1)[0]
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#imgR = np.split(img, 2, 1)[1]
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#stereo = cv2.StereoBM_create(numDisparities=64, blockSize=11)
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#disparity = stereo.compute(imgL, imgR)
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# plt.imshow(disparity)
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# plt.show()
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# for imgDir in imgDirs:
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# dir = "./pic_en/"+imgDir
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# print(dir)
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# img = cv2.imread(dir)
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# imgL = np.split(img, 2, 1)[0]
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# imgR = np.split(img, 2, 1)[1]
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# print(img.shape)
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# print(imgL.shape)
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# print(imgR.shape)
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# cv2.imshow("img", img)
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# stereo = cv2.StereoSGBM_create(numDisparities=96, blockSize=11)
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# disparity = stereo.compute(imgL, imgR)
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# plt.imshow(disparity)
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# plt.show()
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# import numpy as np
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# import cv2
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# from matplotlib import pyplot as plt
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#
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# imgL = cv2.imread('tsukuba_l.png',0)
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# imgR = cv2.imread('tsukuba_r.png',0)
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#
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# stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15)
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# disparity = stereo.compute(imgL,imgR)
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# plt.imshow(disparity,'gray')
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# plt.show()
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