import os
import cv2
import paddlehub as hub
import matplotlib.pyplot as plt
from matplotlib.image import imread
import numpy as np
def show_img(img_path, size=8):
'''
文件读取图片显示
'''
im = imread(img_path)
plt.figure(figsize=(size, size))
plt.axis("off")
plt.imshow(im)
def img_show_bgr(image, size=8):
'''
cv读取的图片显示
'''
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(size, size))
plt.imshow(image)
plt.axis("off")
plt.show()
show_img('imgs/01.jpg')
def get_true_angel(value):
'''
转转得到角度值
'''
return value / np.pi * 180
def get_angle(x1, y1, x2, y2):
'''
计算旋转角度
'''
global the_angle
dx = abs(x1 - x2)
dy = abs(y1 - y2)
result_angele = 0
if x1 == x2:
if y1 > y2:
result_angele = 180
else:
if y1 != y2:
the_angle = int(get_true_angel(np.arctan(dx / dy)))
if x1 < x2:
if y1 > y2:
result_angele = -(180 - the_angle)
elif y1 < y2:
result_angele = -the_angle
elif y1 == y2:
result_angele = -90
elif x1 > x2:
if y1 > y2:
result_angele = 180 - the_angle
elif y1 < y2:
result_angele = the_angle
elif y1 == y2:
result_angele = 90
if result_angele < 0:
result_angele = 360 + result_angele
return result_angele
def rotate_bound(image, angle, key_point_y):
'''
旋转图像,并取得关节点偏移量
'''
# 获取图像的尺寸
(h, w) = image.shape[:2]
# 旋转中心
(cx, cy) = (w / 2, h / 2)
# 关键点必须在中心的y轴上
(kx, ky) = cx, key_point_y
d = abs(ky - cy)
# 设置旋转矩阵
M = cv2.getRotationMatrix2D((cx, cy), -angle, 1.0)
cos = np.abs(M[0, 0])
sin = np.abs(M[0, 1])
# 计算图像旋转后的新边界
nW = int((h * sin) + (w * cos))
nH = int((h * cos) + (w * sin))
# 计算旋转后的相对位移
move_x = nW / 2 + np.sin(angle / 180 * np.pi) * d
move_y = nH / 2 - np.cos(angle / 180 * np.pi) * d
# 调整旋转矩阵的移动距离(t_{x}, t_{y})
M[0, 2] += (nW / 2) - cx
M[1, 2] += (nH / 2) - cy
return cv2.warpAffine(image, M, (nW, nH)), int(move_x), int(move_y)
def get_distences(x1, y1, x2, y2):
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
def append_img_by_sk_points(img, append_img_path, key_point_y, first_point, second_point, append_img_reset_width=None,
append_img_max_height_rate=1, middle_flip=False, append_img_max_height=None):
'''
将需要添加的肢体图片进行缩放
'''
append_image = cv2.imdecode(np.fromfile(append_img_path, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
# 根据长度进行缩放
sk_height = int(
get_distences(first_point[0], first_point[1], second_point[0], second_point[1]) * append_img_max_height_rate)
# 缩放制约
if append_img_max_height:
sk_height = min(sk_height, append_img_max_height)
sk_width = int(
sk_height / append_image.shape[0] * append_image.shape[1]) if append_img_reset_width is None else int(
append_img_reset_width)
if sk_width <= 0:
sk_width = 1
if sk_height <= 0:
sk_height = 1
# 关键点映射
key_point_y_new = int(key_point_y / append_image.shape[0] * append_image.shape[1])
# 缩放图片
append_image = cv2.resize(append_image, (sk_width, sk_height))
img_height, img_width, _ = img.shape
# 是否根据骨骼节点位置在 图像中间的左右来控制是否进行 左右翻转图片
# 主要处理头部的翻转, 默认头部是朝左
if middle_flip:
middle_x = int(img_width / 2)
if first_point[0] < middle_x and second_point[0] < middle_x:
append_image = cv2.flip(append_image, 1)
# 旋转角度
angle = get_angle(first_point[0], first_point[1], second_point[0], second_point[1])
append_image, move_x, move_y = rotate_bound(append_image, angle=angle, key_point_y=key_point_y_new)
app_img_height, app_img_width, _ = append_image.shape
zero_x = first_point[0] - move_x
zero_y = first_point[1] - move_y
(b, g, r) = cv2.split(append_image)
for i in range(0, r.shape[0]):
for j in range(0, r.shape[1]):
if 230 > r[i][j] > 200 and 0 <= zero_y + i < img_height and 0 <= zero_x + j < img_width:
img[zero_y + i][zero_x + j] = append_image[i][j]
return img
body_img_path_map = {
"right_hip": "shadow_play_material/right_hip.jpg",
"right_knee": "shadow_play_material/right_knee.jpg",
"left_hip": "shadow_play_material/left_hip.jpg",
"left_knee": "shadow_play_material/left_knee.jpg",
"left_elbow": "shadow_play_material/left_elbow.jpg",
"left_wrist": "shadow_play_material/left_wrist.jpg",
"right_elbow": "shadow_play_material/right_elbow.jpg",
"right_wrist": "shadow_play_material/right_wrist.jpg",
"head": "shadow_play_material/head.jpg",
"body": "shadow_play_material/body.jpg"
}
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
def get_combine_img(img_path, pose_estimation=pose_estimation, body_img_path_map=body_img_path_map,
backgroup_img_path='background.jpg'):
'''
识别图片中的关节点,并将皮影的肢体进行对应,最后与原图像拼接后输出
'''
result = pose_estimation.keypoint_detection(paths=[img_path])
image = cv2.imread(img_path)
# 背景图片
backgroup_image = cv2.imread(backgroup_img_path)
image_flag = cv2.resize(backgroup_image, (image.shape[1], image.shape[0]))
# 最小宽度
min_width = int(get_distences(result[0]['data']['head_top'][0], result[0]['data']['head_top'][1],
result[0]['data']['upper_neck'][0], result[0]['data']['upper_neck'][1]) / 3)
# 右大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['right_hip'][1]) * 1.6), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_hip'], key_point_y=10,
first_point=result[0]['data']['right_hip'],
second_point=result[0]['data']['right_knee'],
append_img_reset_width=append_img_reset_width)
# 右小腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['right_hip'][1]) * 1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_knee'], key_point_y=10,
first_point=result[0]['data']['right_knee'],
second_point=result[0]['data']['right_ankle'],
append_img_reset_width=append_img_reset_width)
# 左大腿
append_img_reset_width = max(int(get_distences(result[0]['data']['pelvis'][0], result[0]['data']['pelvis'][1],
result[0]['data']['left_hip'][0],
result[0]['data']['left_hip'][1]) * 1.6), min_width)
image_flag = append_img_by_sk