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背景

领略千年皮影戏魅力，传承正在消失的艺术, 皮影戏的神奇，在于小小皮影在指尖上飞舞，时而刀光剑影、时而策马扬鞭、时而缠绵悱恻，千军万马是他，单打独斗也是他。皮影戏可谓是闻名中外，它是把光影声色做到极致的一门古老艺术。 先辈门通过手艺演绎着皮影戏，同样我们也可以通过AI方式来实现。为了实现皮影戏，可以通过PaddleHub提供的人体骨骼关键点检测库完成将人体姿态检测，同时映射到皮影身上，让皮影动起来。 皮影素材 项目实现过程中使用的Python版本为3.7.0，其他依赖库的版本分别为cv2 4.5.1.48、matplotlib 2.2.2、numpy 1.19.3、tensorflow 2.4.1。

本项目具体流程如下：

一、安装依赖库以及模型

1、安装PaddlePaddle

PaddlePaddle官网：

python -m pip install paddlepaddle==2.0.0 -i https://mirror.baidu.com/pypi/simple

测试安装成功：

2、安装paddlehub：

paddlehub官网：

pip install paddlehub -i https://pypi.doubanio.com/simple

测试安装成功：

3、导入人体骨骼关键节点检测模型

hub install human_pose_estimation_resnet50_mpii==1.1.1

二、检测是否安装成功

1、检测图片骨骼节点

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()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)

2、拼接皮影素材

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('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*180def 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
   
     x2:            if y1>y2:                result_angele = 180 - the_angle            elif y1
    
     r[i][j]>200 and 0<=zero_y+i
    
   

三、让皮影动起来

具体实现步骤如下：

1、 准备素材 2、将视频中每一帧保存成图片 3、分析图片中的人体姿势， 并转换为皮影姿势，输出 结果 4、合并图像到视频，得到最终的结果

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()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 * 180def 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_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" : "./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='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],                      result[0]['data']['right_elbow'][0], result[0]['data']['right_elbow'][1]) * 1.6)    image_flag = append_img_by_sk_points(image_flag, body_img_path_map['left_wrist'], key_point_y=10,                                         first_point=result[0]['data']['right_elbow'],                                         second_point=result[0]['data']['right_wrist'], append_img_max_height_rate=1.5,                                         append_img_max_height=append_img_max_height)    # 左手臂    image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_elbow'], key_point_y=25,                                         first_point=result[0]['data']['left_shoulder'],                                         second_point=result[0]['data']['left_elbow'], append_img_max_height_rate=1.2)    # 左手肘    append_img_max_height = int(        get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],                      result[0]['data']['left_elbow'][0], result[0]['data']['left_elbow'][1]) * 1.6)    image_flag = append_img_by_sk_points(image_flag, body_img_path_map['right_wrist'], key_point_y=10,                                         first_point=result[0]['data']['left_elbow'],                                         second_point=result[0]['data']['left_wrist'], append_img_max_height_rate=1.5,                                         append_img_max_height=append_img_max_height)    # 头    image_flag = append_img_by_sk_points(image_flag, body_img_path_map['head'], key_point_y=10,                                         first_point=result[0]['data']['head_top'],                                         second_point=result[0]['data']['upper_neck'], append_img_max_height_rate=1.2,                                         middle_flip=True)    # 身体    append_img_reset_width = max(        int(get_distences(result[0]['data']['left_shoulder'][0], result[0]['data']['left_shoulder'][1],                          result[0]['data']['right_shoulder'][0], result[0]['data']['right_shoulder'][1]) * 1.2),        min_width * 3)    image_flag = append_img_by_sk_points(image_flag, body_img_path_map['body'], key_point_y=20,                                         first_point=result[0]['data']['upper_neck'],                                         second_point=result[0]['data']['pelvis'],                                         append_img_reset_width=append_img_reset_width, append_img_max_height_rate=1.2)    result_img = np.concatenate((image, image_flag), axis=1)    return result_img##pos_img_path = 'test3.jpg'##result_img =  get_combine_img(pos_img_path, pose_estimation, body_img_path_map)##img_show_bgr(result_img, size=10)input_video = 'work/test.mp4'def transform_video_to_image(video_file_path, img_path):    '''    将视频中每一帧保存成图片    '''    video_capture = cv2.VideoCapture(video_file_path)    fps = video_capture.get(cv2.CAP_PROP_FPS)    count = 0    while(True):        ret, frame = video_capture.read()        if ret:            cv2.imwrite(img_path + '%d.jpg' % count, frame)            count += 1        else:            break    video_capture.release()    print('视频图片保存成功, 共有 %d 张' % count)    return fpsfps = transform_video_to_image(input_video, 'work/mp4_img/')def analysis_pose(input_frame_path, output_frame_path, is_print=True):    '''    分析图片中的人体姿势， 并转换为皮影姿势，输出结果    '''    file_items = os.listdir(input_frame_path)    file_len = len(file_items)    for i, file_item in enumerate(file_items):        if is_print:            print(i+1,'/', file_len, ' ', os.path.join(output_frame_path, file_item))        combine_img = get_combine_img(os.path.join(input_frame_path, file_item))        cv2.imwrite(os.path.join(output_frame_path, file_item), combine_img)analysis_pose('work/mp4_img/', 'work/mp4_img_analysis/', is_print=False)def combine_image_to_video(comb_path, output_file_path, fps=30, is_print=False):    '''        合并图像到视频    '''    fourcc = cv2.VideoWriter_fourcc(*'MP4V')    file_items = os.listdir(comb_path)    file_len = len(file_items)    # print(comb_path, file_items)    if file_len > 0:        temp_img = cv2.imread(os.path.join(comb_path, file_items[0]))        img_height, img_width = temp_img.shape[0], temp_img.shape[1]        out = cv2.VideoWriter(output_file_path, fourcc, fps, (img_width, img_height))        for i in range(file_len):            pic_name = os.path.join(comb_path, str(i) + ".jpg")            if is_print:                print(i + 1, '/', file_len, ' ', pic_name)            img = cv2.imread(pic_name)            out.write(img)        out.release()combine_image_to_video('work/mp4_img_analysis/', 'work/mp4_analysis.mp4', fps)

四、总结

本项目在弘扬传统文化的同时，还能让我学习到AI的知识，体会到 PaddleHub 的强大, 这是一个很有意义的项目。

实现效果视频链接：

转载地址：https://blog.csdn.net/zycwlkq/article/details/116463136 如侵犯您的版权，请留言回复原文章的地址，我们会给您删除此文章，给您带来不便请您谅解！