第一步：使用人眼识别器实时对眼睛进行识别并提取
import numpy as np import cv2 cap = cv2.VideoCapture('C:/Users/zhangjing/Documents/Bandicam/phone_7.mp4')# 调用摄像头 classfier = cv2.CascadeClassifier("C:/Users/zhangjing/Anaconda3/Lib/site-packages/cv2/data/haarcascade_eye_tree_eyeglasses.xml")# 人眼识别器分类器 frame_cnt = 0 #读frame while cap.isOpened(): read, frame = cap.read() if not read: break grey = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)# 灰度转换 Rects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=3, minSize=(32, 32))# 人眼检测 if len(Rects) > 0: for Rect in Rects: x, y, w, h = Rect print(x,y,w,h) # cv2.imshow("eye", frame[y + 10:y + h - 10, x:x + w]) # cv2.imshow("fame", frame) cv2.imwrite('C:/Users/zhangjing/Documents/Bandicam/phone_7_eye/'+str(frame_cnt)+'.png',frame[y + 10:y + h - 10, x + 10:x + w- 10])# 保存图片 frame_cnt= frame_cnt+1 if cv2.waitKey(5) & 0xFF == ord('q'): break cap.release() cv2.destroyAllWindows()第二步：对保存的图片进行统一尺寸的剪辑
import os.path from PIL import Image def ResizeImage(): img_filepath = 'C:/Users/zhangjing/Documents/Bandicam/phone_7_eye/'#输入的图片路径 fileout = 'C:/Users/zhangjing/Documents/Bandicam/phone_6_cut/'#输出的图片路径 width = 70#宽度裁剪尺寸 height = 70#高度裁剪尺寸 type = 'png'#图片格式 picture_name = os.listdir(img_filepath) for i in range(len(picture_name)): img_rd = Image.open(img_filepath+picture_name[i]) out = img_rd.resize((width, height),Image.ANTIALIAS) #resize image with high-quality out.save(img_filepath+picture_name[i], type)第三步： 对剪好的图片进行视频合成
def video_make(): write_image_path = 'C:/Users/zhangjing/Documents/Bandicam/phone_7_cut/'#写入视频的路径 new_video = 'C:/Users/zhangjing/Documents/Bandicam/phone_7_cut/'#图片的路径 print('video make ...') all_folds = os.listdir(write_image_path) fps = 30 # 保存视频的FPS，可以适当调整 size = (70, 70)#图片的尺寸 # 可以用(*'DVIX')或(*'X264'),如果都不行先装ffmepg: sudo apt-get install ffmepg fourcc = cv2.VideoWriter_fourcc(*'mp4v') videoWriter = cv2.VideoWriter(new_video +'test_640.mp4', fourcc, fps, size) for i in range(0, len(all_folds)): frame = cv2.imread(write_image_path + str(i) + '.png') videoWriter.write(frame) videoWriter.release() print('video make end!!')第四步： 对眼睛视频做光流分析保存图片
import cv2 import numpy as np cap = cv2.VideoCapture('C:/Users/zhangjing/Documents/Bandicam/phone_7_eye/test_640.mp4') #获取第一帧 ret, frame1 = cap.read() prvs = cv2.cvtColor(frame1,cv2.COLOR_BGR2GRAY) hsv = np.zeros_like(frame1) #遍历每一行的第1列 hsv[...,1] = 255 frame_cnt = 0 while(1): ret, frame2 = cap.read() next = cv2.cvtColor(frame2,cv2.COLOR_BGR2GRAY) #返回一个两通道的光流向量，实际上是每个点的像素位移值 flow = cv2.calcOpticalFlowFarneback(prvs,next, None, 0.5, 3, 15, 3, 5, 1.2, 0) print(flow.shape) # print(flow) #笛卡尔坐标转换为极坐标，获得极轴和极角 mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1]) hsv[...,0] = ang*180/np.pi/2 hsv[...,2] = cv2.normalize(mag,None,0,255,cv2.NORM_MINMAX) rgb = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR) frame_cnt = frame_cnt + 1 # cv2.imshow('rgb', rgb) cv2.imwrite('C:/Users/zhangjing/Documents/Bandicam/phone_7_cut/' + str(frame_cnt) + '.png', rgb) k = cv2.waitKey(30) & 0xff if k == 27: break elif k == ord('s'): cv2.imwrite('opticalfb.png',frame2) cv2.imwrite('opticalhsv.png',rgb) prvs = next cap.release() cv2.destroyAllWindows()第二种光流法
import cv2 cap = cv2.VideoCapture('C:/Users/zhangjing/Documents/Bandicam/phone.mp4') # ShiTomasi corner detection的参数 feature_params = dict(maxCorners=100, qualityLevel=0.3, minDistance=7, blockSize=7) # 光流法参数 # maxLevel 未使用的图像金字塔层数 lk_params = dict(winSize=(15, 15), maxLevel=2, criteria=(cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 10, 0.03)) # 创建随机生成的颜色 color = np.random.randint(0, 255, (100, 3)) ret, old_frame = cap.read() # 取出视频的第一帧 old_gray = cv2.cvtColor(old_frame, cv2.COLOR_BGR2GRAY) # 灰度化 p0 = cv2.goodFeaturesToTrack(old_gray, mask=None, **feature_params) mask = np.zeros_like(old_frame) # 为绘制创建掩码图片 fram_cnt = 0 while True: _, frame = cap.read() frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # 计算光流以获取点的新位置 p1, st, err = cv2.calcOpticalFlowPyrLK(old_gray, frame_gray, p0, None, **lk_params) # 选择good points good_new = p1[st == 1] good_old = p0[st == 1] # 绘制跟踪框 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('image', img) fram_cnt = fram_cnt + 1 k = cv2.waitKey(30) # & 0xff if k == 27: break old_gray = frame_gray.copy() p0 = good_new.reshape(-1, 1, 2) cv2.destroyAllWindows() cap.release()第五步对输出的光流图进行视频合成同步骤三
结果展示
