import ffmpeg import numpy as np import matplotlib import cv2 import os import sys from matplotlib import pyplot as plt ''' TODO: 0:读取视频 √ 1:获取视差 √ 2:获取运动矢量 √ 3:确定舒适度 4:加舒适度水印 ... ''' # 打开视频文件 def openVid(): fileName = input("video path:") while not os.path.isfile(fileName): print("file doesn't exist!") fileName = input("video path:") cap = cv2.VideoCapture(fileName) if cap.isOpened(): return cap else: print("cannot open video.") sys.exit() # 获取视频总帧数 def getFrameCount(cap): if cap.isOpened(): return cap.get(cv2.CAP_PROP_FRAME_COUNT) else: print("cannot open video.") sys.exit() # 获取帧速率 def getFrameRate(cap): if cap.isOpened(): return cap.get(cv2.CAP_PROP_FPS) else: print("cannot open video.") sys.exit() # 给出左右画面，计算景深 def getDepthMap(imgL, imgR): stereo = cv2.StereoSGBM_create(numDisparities=64, blockSize=3) return stereo.compute(imgL, imgR) # 给出前后两帧，计算帧间运动矢量 def getMotionVector(prvs, next): hsv = np.zeros_like(imgR) # 将运动矢量按hsv显示，以色调h表示运动方向，以明度v表示运动位移 hsv[..., 1] = 255 # 饱和度置为最高 # 转为灰度以计算光流 prvs = cv2.cvtColor(prvs, cv2.COLOR_BGR2GRAY) next = cv2.cvtColor(next, cv2.COLOR_BGR2GRAY) flow = cv2.calcOpticalFlowFarneback( prvs, next, None, 0.5, 3, 15, 3, 5, 1.2, 0) # 计算两帧间的光流，即运动矢量的直角坐标表示 mag, ang = cv2.cartToPolar( flow[..., 0], flow[..., 1]) # 运动矢量的直角坐标表示转换为极坐标表示 hsv[..., 0] = ang*180/np.pi/2 # 角度对应色调 hsv[..., 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) # 位移量对应明度 return hsv if __name__ == "__main__": cap = openVid() isDemo = int(input("is Demo(0/1)?")) frameRate = getFrameRate(cap) frameCount = getFrameCount(cap) isSuccess, img = cap.read() if not isSuccess: print("video read error.") sys.exit() # 分割左右画面 imgL = np.split(img, 2, 1)[0] imgR = np.split(img, 2, 1)[1] prvs = imgR # 上一帧的右画面，用于运动矢量计算 # 每秒取10帧进行计算 for frameID in range(round(cap.get(cv2.CAP_PROP_POS_FRAMES)), round(frameCount), round(frameRate/10)): cap.set(cv2.CAP_PROP_POS_FRAMES, frameID) isSuccess, img = cap.read() if not isSuccess: print("video read error.") sys.exit() # 分割左右画面 imgL = np.split(img, 2, 1)[0] imgR = np.split(img, 2, 1)[1] next = imgR # 当前帧的右画面，用于运动矢量计算 hsv = getMotionVector(prvs, next) # 计算深度图 disparity = getDepthMap(imgL, imgR) # 显示计算结果 print("time: ", round(frameID/frameRate, 2)) print("AVG depth: ", round(np.mean(disparity), 2)) print("AVG motion: ", round(np.mean(hsv[..., 2]), 2)) print() # 当为demo模式时显示当前帧画面、运动矢量图和景深图 if isDemo: # 显示当前帧 cv2.namedWindow("img", cv2.WINDOW_NORMAL) cv2.imshow('img', img) # 显示当前帧的运动矢量的hsv表示 bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) # hsv转为rgb用于显示 cv2.namedWindow("MotionVector", cv2.WINDOW_NORMAL) cv2.imshow("MotionVector", bgr) # 显示当前帧的景深图 plt.title("DepthMap") plt.imshow(disparity) plt.pause(0.2) prvs = next # 当前帧覆盖上一帧，继续计算 print("success") # ffmpeg.input("./vid/avatar.mkv") # Motion Vector #cap = cv2.VideoCapture('./vid/zootopia.mkv') # for i in range(1,10000): # cap.read() # params for ShiTomasi corner detection # feature_params = dict( maxCorners = 100, # qualityLevel = 0.3, # minDistance = 7, # blockSize = 7 ) # Parameters for lucas kanade optical flow # lk_params = dict( winSize = (15,15), # maxLevel = 2, # criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) # Create some random colors #color = np.random.randint(0,255,(100,3)) # Take first frame and find corners in it #ret, old_frame = cap.read() #old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) #p0 = cv2.goodFeaturesToTrack(old_gray, mask = None, **feature_params) # Create a mask image for drawing purposes #mask = np.zeros_like(old_frame) # while(1): # ret,frame = cap.read() # frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # # calculate optical flow # p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # # Select good points # good_new = p1[st==1] # good_old = p0[st==1] # # draw the tracks # for i,(new,old) in enumerate(zip(good_new,good_old)): # a,b = new.ravel() # c,d = old.ravel() # mask = cv2.line(mask, (a,b),(c,d), color[i].tolist(), 2) # frame = cv2.circle(frame,(a,b),5,color[i].tolist(),-1) # img = cv2.add(frame,mask) # cv2.imshow('frame',img) # k = cv2.waitKey(30) & 0xff # if k == 27: # break # # Now update the previous frame and previous points # old_gray = frame_gray.copy() # p0 = good_new.reshape(-1,1,2) # cv2.destroyAllWindows() # cap.release() # 中文文件名无法识别 # imgDirs = os.listdir("./pic_en") # def read_frame_as_jpeg(in_filename, frame_num): # out, err = ( # ffmpeg # .input(in_filename) # .filter('select', 'gte(n,{})'.format(frame_num)) # .output('pipe:', vframes=1, format='image2', vcodec='mjpeg') # .run(capture_stdout=True) # ) # return out # ffmpeg.input("./vid/venom.mkv") # ffmpeg. #img = read_frame_as_jpeg("./vid/venom.mkv", 648) # print(type(img)) #img = cv2.imdecode(img,0) # print(img) # cv2.imshow("img",img) #imgL = np.split(img, 2, 1)[0] #imgR = np.split(img, 2, 1)[1] #stereo = cv2.StereoBM_create(numDisparities=64, blockSize=11) #disparity = stereo.compute(imgL, imgR) # plt.imshow(disparity) # plt.show() # for imgDir in imgDirs: # dir = "./pic_en/"+imgDir # print(dir) # img = cv2.imread(dir) # imgL = np.split(img, 2, 1)[0] # imgR = np.split(img, 2, 1)[1] # print(img.shape) # print(imgL.shape) # print(imgR.shape) # cv2.imshow("img", img) # stereo = cv2.StereoSGBM_create(numDisparities=96, blockSize=11) # disparity = stereo.compute(imgL, imgR) # plt.imshow(disparity) # plt.show() # import numpy as np # import cv2 # from matplotlib import pyplot as plt # # imgL = cv2.imread('tsukuba_l.png',0) # imgR = cv2.imread('tsukuba_r.png',0) # # stereo = cv2.StereoBM_create(numDisparities=16, blockSize=15) # disparity = stereo.compute(imgL,imgR) # plt.imshow(disparity,'gray') # plt.show()