使用python opencv实现瞳孔追踪

使用python+Opencv实现瞳孔识别与跟踪

使用opencv做瞳孔识别是十分方便的，它有很多好用的库可以直接使用。
废话不多说，直接上代码。

import cv2
import numpy as npcap = cv2.VideoCapture("eyes.mp4")while (True):ret, frame = cap.read()if ret is False:breakroi = frame[100: 500, 157: 800]  #利用切片工具，选出感兴趣roi区域#  cv2.imshow("show",roi)rows, cols, _ = roi.shape   #保存视频尺寸以备用gray_roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)   #转灰度gray_roi = cv2.GaussianBlur(gray_roi, (7, 7), 0)    #高斯滤波一次_, threshold = cv2.threshold(gray_roi, 8, 255, cv2.THRESH_BINARY_INV)  #二值化，依据需要改变阈值contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) #画连通域contours = sorted(contours, key=lambda x: cv2.contourArea(x), reverse=True)for cnt in contours:(x, y, w, h) = cv2.boundingRect(cnt)#cv2.drawContours(roi, [cnt], -1, (0, 0, 255), 3)cv2.rectangle(roi, (x, y), (x + w, y + h), (255, 0, 0), 2)cv2.line(roi, (x + int(w/2), 0), (x + int(w/2), rows), (0, 255, 0), 2)cv2.line(roi, (0, y + int(h/2)), (cols, y + int(h/2)), (0, 255, 0), 2)breakcv2.imshow("Roi", roi)cv2.imshow("Threshold", threshold)key = cv2.waitKey(30)if cv2.waitKey(1) & 0xff == ord('q'):break
cap.release()
cv2.destroyAllWindows()

效果如下