一、安装依赖库以及模型
安装PaddlePaddle
安装PaddleHub
导入人体骨骼关键节点检测模型
二、测试及拼接皮影素材
打开pycharm软件，需安装以下依赖库：cv2 4.5.1.48、matplotlib 3.0.3、numpy
1.16.2、tensorflow 2.4.1等。
测试人体骨骼关键点检测模型是否安装成功。
①代码如下：
②打开output_pose文件夹会发现多出一张图片为检测图片骨骼节点图，若安装成功的话，结果如下图所示：
拼接皮影素材
代码如下：
import osimport cv2import paddlehub as hubimport matplotlib.pyplot as pltfrom matplotlib.image import imreadimport numpy as npdef 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 * 180def 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 = -90elif 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_angeledef 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) ** 0.5def 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 imgbody_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_p
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