Python 物体关键点识别

 

处理图片

def get_color(frame):
print(‘go in get_color’)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
maxsum = -100
color = None
color_dict = getColorList()
for d in color_dict:
mask = cv2.inRange(frame, color_dict[d][0], color_dict[d][1])
cv2.imwrite(d + ‘.jpg’, mask)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
sum = 0
for c in cnts:
sum += cv2.contourArea©
if sum > maxsum:
maxsum = sum
color = d
return color
图像处理

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紧接着是图像处理，其中包括转为灰度图，读取颜色字典，然后腐化膨胀操作。

处理图片

def get_color(frame):
print(‘go in get_color’)
hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
maxsum = -100
color = None
color_dict = getColorList()
for d in color_dict:
mask = cv2.inRange(frame, color_dict[d][0], color_dict[d][1])
cv2.imwrite(d + ‘.jpg’, mask)
binary = cv2.threshold(mask, 127, 255, cv2.THRESH_BINARY)[1]
binary = cv2.dilate(binary, None, iterations=2)
img, cnts, hiera = cv2.findContours(binary.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
sum = 0
for c in cnts:
sum += cv2.contourArea©
if sum > maxsum:
maxsum = sum
color = d
return color

图片相减的办法

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然后是图片相减找到动态物体的代码，每循环5次保存一次图片，时间是很短的不用担心。然后通过absdiff函数对图片像素值作差找到动态物体，接着讲像素值相减非零的部分用矩形框圈出来。

while True:
ret, frame = camera.read()
if not ret:
break
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (21, 21), 0)
a=a+1
if a%5==0:
cv2.imwrite(“1.jpg”, frame)
firstframe=cv2.imread(“1.jpg”)
firstframe= cv2.cvtColor(firstframe, cv2.COLOR_BGR2GRAY)
firstframe= cv2.GaussianBlur(firstframe, (21, 21), 0)
frameDelta = cv2.absdiff(firstframe, gray)
thresh = cv2.threshold(frameDelta, 25, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.dilate(thresh, None, iterations=2)
cnts= cv2.findContours(thresh.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
x, y, w, h = cv2.boundingRect(thresh)
frame = cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 0, 255), 2)
cv2.imshow(“frame”, frame)
因为保存图片是每隔5次进行一次，在某个瞬间可能保存的图片不存在等原因，所以需要通过try的方法避免错误，最终的演示效果文末有。
try:
ret0, frame0 = camera.read()
cropped = frame0[y:y+h,x:x+w ] # 裁剪坐标为[y0:y1, x0:x1]
cv2.imwrite(“3.jpg”, cropped)
frame1 = cv2.imread(filename)
print(get_color(frame1))