使用飞桨Paddlehub实现皮影戏
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使用飞桨Paddlehub实现皮影戏
前言
飞桨(PaddlePaddle)是集深度学习核心框架、工具组件和服务平台为一体的技术先进、功能完备的开源深度学习平台，已被中国企业广泛使 用，深度契合企业应用需求，拥有活跃的开发者社区生态。提供丰富的官方支持模型集合，我们这里将要使用到其中的骨骼节点检测模型， 通过PaddleHub提供的人体骨骼关键点检测预训练模型，我们就可以快速实现皮影戏的效果。
PaddleHub可以便捷地获取PaddlePaddle生态下的预训练模型，完成模型的管理和一键预测。配合使用Fine-tune API，可以基于大规模预训练模型快速完成迁移学习，让预训练模型能更好地服务于用户特定场景的应用。
实现步骤
1、安装依赖包和模型
这里win+r再输入cmd可以进入windows命令行界面，输入以下代码可以快速安装
安装PaddlePaddle
python -m pip install paddlepaddle==2.0.2 -i https://mirror.baidu.com/pypi/simple
安装PaddleHub
pip install PaddleHub
安装人体骨骼关键节点检测模型：通过PaddleHub来安装人体骨骼关键点检测模型：human_pose_estimation_resnet50_mpii。
hub install human_pose_estimation_resnet50_mpii==1.1.1
2、测试是否成功以及拼接素材
本次使用的是pycharm，需要安装以下依赖库：cv2 4.5.1.48、matplotlib 3.0.3、numpy 1.16.2、tensorflow 2.4.1等。
检测是否安装成功
选取一张图片输入，若检测成功会检测到人体骨骼关键点信息
代码如下：
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):
im = imread(img_path)
plt.figure(figsize=(size, size))
plt.axis("off")
plt.imshow(im)
def img_show_bgr(image, size=8):
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
plt.figure(figsize=(size, size))
plt.imshow(image)
plt.axis("off")
plt.show()
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
result = pose_estimation.keypoint_detection(paths=['test1.jpg'], visualization=True, output_dir="output_pose/")
print(result)
拼接皮影素材
拼接皮影戏需要素材，素材请到这个网址中下载，如文章第一张图那些：https://aistudio.baidu.com/aistudio/projectdetail/764130?fromQRCode=1&shared=1
代码如下：
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('work/imgs/body01.jpg')
pose_estimation = hub.Module(name="human_pose_estimation_resnet50_mpii")
result = pose_estimation.keypoint_detection(paths=['test4.jpg'], visualization=True, output_dir="work/output_pose/")
print(result)
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
'''
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" : "./work/shadow_play_material/right_hip.jpg",
"right_knee" : "./work/shadow_play_material/right_knee.jpg",
"left_hip" : "./work/shadow_play_material/left_hip.jpg",
"left_knee" : "./work/shadow_play_material/left_knee.jpg",
"left_elbow" : "./work/shadow_play_material/left_elbow.jpg",
"left_wrist" : "./work/shadow_play_material/left_wrist.jpg",
"right_elbow" : "./work/shadow_play_material/right_elbow.jpg",
"right_wrist" : "./work/shadow_play_material/right_wrist.jpg",
"head" : "./work/shadow_play_material/head.jpg",
"body" : "./work/shadow_play_material/body.jpg"
def get_combine_img(img_path, pose_estimation=pose_estimation, body_img_path_map=body_img_path_map, backgroup_img_path= 'work/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_points(image_flag, body_img_path_map['left_hip'], key_point_y=0, first_point=result[0]['data']['left_hip'],
second_point=result[0]['data']['left_knee'], append_img_reset_width=append_img_reset_width)
# 左小腿
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']['left_hip'][1])*1.5), min_width)
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_knee'], key_point_y=10, first_point=result[0]['data']['left_knee'],
second_point=result[0]['data']['left_ankle'], append_img_reset_width=append_img_reset_width)
# 右手臂
image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_elbow'], key_point_y=25, first_point=result[0]['data']['right_shoulder'],
second_point=result[0]['data']['right_elbow'], append_img_max_height_rate=1.2)
# 右手肘
append_img_max_height = int(get_distences(result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1]以上是关于使用飞桨Paddlehub实现皮影戏的主要内容，如果未能解决你的问题，请参考以下文章