opecv人像左右安人肩膀，上面按头顶剪切

要用OpenCV对人像进行左右安肩膀、上面按头顶剪切，可以按照以下步骤进行：

1. 导入必要的库和模块：cv2、numpy

import cv2
import numpy as np

2. 读取原始图片

img = cv2.imread('path/to/image')

3. 对图片进行剪切，只保留人像的上半部分

height, width = img.shape[:2]
half_height = int(height / 2)
upper_body_img = img[0:half_height, :]

4. 检测人像的关键点

protoFile = "path/to/pose_deploy_linevec.prototxt"
weightsFile = "path/to/pose_iter_440000.caffemodel"
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)

keypoints_list = []
threshold = 0.1

inWidth = upper_body_img.shape[1]
inHeight = upper_body_img.shape[0]
net.setInput(cv2.dnn.blobFromImage(upper_body_img, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False))
output = net.forward()

H = output.shape[2]
W = output.shape[3]
for i in range(15):
    probMap = output[0, i, :, :]
    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
    x = (inWidth * point[0]) / W
    y = (inHeight * point[1]) / H
    if prob > threshold:
        keypoints_list.append((int(x), int(y)))
    else:
        keypoints_list.append(None)

5. 根据关键点来截取人像

left_shoulder = keypoints_list[5]
right_shoulder = keypoints_list[2]
top_head = keypoints_list[0]

if left_shoulder is not None and right_shoulder is not None and top_head is not None:
    x1 = left_shoulder[0]
    y1 = top_head[1]
    x2 = right_shoulder[0]
    y2 = y1 + (x2 - x1)
    cropped_img = upper_body_img[y1:y2, x1:x2]

6. 显示剪切后的图片

cv2.imshow('image', cropped_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

将上述代码整合起来，可以得到以下完整代码：

import cv2
import numpy as np

img = cv2.imread('path/to/image')
height, width = img.shape[:2]
half_height = int(height / 2)
upper_body_img = img[0:half_height, :]

protoFile = "path/to/pose_deploy_linevec.prototxt"
weightsFile = "path/to/pose_iter_440000.caffemodel"
net = cv2.dnn.readNetFromCaffe(protoFile, weightsFile)

keypoints_list = []
threshold = 0.1

inWidth = upper_body_img.shape[1]
inHeight = upper_body_img.shape[0]
net.setInput(cv2.dnn.blobFromImage(upper_body_img, 1.0 / 255, (inWidth, inHeight), (0, 0, 0), swapRB=False, crop=False))
output = net.forward()

H = output.shape[2]
W = output.shape[3]
for i in range(15):
    probMap = output[0, i, :, :]
    minVal, prob, minLoc, point = cv2.minMaxLoc(probMap)
    x = (inWidth * point[0]) / W
    y = (inHeight * point[1]) / H
    if prob > threshold:
        keypoints_list.append((int(x), int(y)))
    else:
        keypoints_list.append(None)

left_shoulder = keypoints_list[5]
right_shoulder = keypoints_list[2]
top_head = keypoints_list[0]

if left_shoulder is not None and right_shoulder is not None and top_head is not None:
    x1 = left_shoulder[0]
    y1 = top_head[1]
    x2 = right_shoulder[0]
    y2 = y1 + (x2 - x1)
    cropped_img = upper_body_img[y1:y2, x1:x2]

cv2.imshow('image', cropped_img)
cv2.waitKey(0)
cv2.destroyAllWindows()

运行代码后，就可以看到剪切后的人像图片。