使用cv2检测运动的物体

使用cv2检测运动的物体

# encoding:utf-8
import cv2
import imutils

cap = cv2.VideoCapture(0)
avg = None
while(True):
# 抓取原始Numpy数组来表示图像并且初始化
ret, frame = cap.read()
# 调整帧尺寸，转换为灰阶图像并进行模糊
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)

# 如果平均帧是None，初始化它
if avg is None:
avg = gray.copy().astype("float")

cv2.accumulateWeighted(gray, avg, 0.5)
frameDelta = cv2.absdiff(gray, cv2.convertScaleAbs(avg))

# 对变化的图像进行阈值化，膨胀阈值图像来填补
thresh = cv2.threshold(frameDelta, 5, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
_, cnts, _ = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)

# 遍历轮廓线
for c in cnts:
if cv2.contourArea(c) < 5000:
continue
# 计算轮廓线的外框，为当前帧画上外框
# 更新文本
(x, y, w, h) = cv2.boundingRect(c)
cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)

text = "people"
cv2.putText(frame, "Room Status: {}".format(text), (10, 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 2)
cv2.putText(frame,"people", (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.35, (0, 0, 255), 1)
cv2.namedWindow("capture")
cv2.imshow("capture", frame)

if cv2.waitKey(1) & 0xFF == ord('q'):
break

cap.release()
cv2.destroyAllWindows()