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https://github.com/TMElyralab/MuseTalk.git
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137 lines
5.2 KiB
Python
Executable File
137 lines
5.2 KiB
Python
Executable File
from PIL import Image
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import numpy as np
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import cv2
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import copy
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def get_crop_box(box, expand):
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x, y, x1, y1 = box
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x_c, y_c = (x+x1)//2, (y+y1)//2
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w, h = x1-x, y1-y
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s = int(max(w, h)//2*expand)
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crop_box = [x_c-s, y_c-s, x_c+s, y_c+s]
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return crop_box, s
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def face_seg(image, mode="raw", fp=None):
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"""
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对图像进行面部解析，生成面部区域的掩码。
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Args:
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image (PIL.Image): 输入图像。
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Returns:
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PIL.Image: 面部区域的掩码图像。
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"""
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seg_image = fp(image, mode=mode) # 使用 FaceParsing 模型解析面部
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if seg_image is None:
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print("error, no person_segment") # 如果没有检测到面部，返回错误
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return None
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seg_image = seg_image.resize(image.size) # 将掩码图像调整为输入图像的大小
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return seg_image
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def get_image(image, face, face_box, upper_boundary_ratio=0.5, expand=1.5, mode="raw", fp=None):
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"""
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将裁剪的面部图像粘贴回原始图像，并进行一些处理。
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Args:
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image (numpy.ndarray): 原始图像（身体部分）。
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face (numpy.ndarray): 裁剪的面部图像。
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face_box (tuple): 面部边界框的坐标 (x, y, x1, y1)。
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upper_boundary_ratio (float): 用于控制面部区域的保留比例。
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expand (float): 扩展因子，用于放大裁剪框。
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mode: 融合mask构建方式
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Returns:
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numpy.ndarray: 处理后的图像。
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"""
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# 将 numpy 数组转换为 PIL 图像
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body = Image.fromarray(image[:, :, ::-1]) # 身体部分图像(整张图)
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face = Image.fromarray(face[:, :, ::-1]) # 面部图像
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x, y, x1, y1 = face_box # 获取面部边界框的坐标
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crop_box, s = get_crop_box(face_box, expand) # 计算扩展后的裁剪框
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x_s, y_s, x_e, y_e = crop_box # 裁剪框的坐标
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face_position = (x, y) # 面部在原始图像中的位置
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# 从身体图像中裁剪出扩展后的面部区域（下巴到边界有距离）
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face_large = body.crop(crop_box)
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ori_shape = face_large.size # 裁剪后图像的原始尺寸
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# 对裁剪后的面部区域进行面部解析，生成掩码
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mask_image = face_seg(face_large, mode=mode, fp=fp)
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mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s)) # 裁剪出面部区域的掩码
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mask_image = Image.new('L', ori_shape, 0) # 创建一个全黑的掩码图像
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mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s)) # 将面部掩码粘贴到全黑图像上
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# 保留面部区域的上半部分（用于控制说话区域）
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width, height = mask_image.size
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top_boundary = int(height * upper_boundary_ratio) # 计算上半部分的边界
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modified_mask_image = Image.new('L', ori_shape, 0) # 创建一个新的全黑掩码图像
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modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary)) # 粘贴上半部分掩码
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# 对掩码进行高斯模糊，使边缘更平滑
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blur_kernel_size = int(0.05 * ori_shape[0] // 2 * 2) + 1 # 计算模糊核大小
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mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0) # 高斯模糊
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#mask_array = np.array(modified_mask_image)
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mask_image = Image.fromarray(mask_array) # 将模糊后的掩码转换回 PIL 图像
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# 将裁剪的面部图像粘贴回扩展后的面部区域
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face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
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body.paste(face_large, crop_box[:2], mask_image)
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body = np.array(body) # 将 PIL 图像转换回 numpy 数组
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return body[:, :, ::-1] # 返回处理后的图像（BGR 转 RGB）
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def get_image_blending(image, face, face_box, mask_array, crop_box):
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body = Image.fromarray(image[:,:,::-1])
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face = Image.fromarray(face[:,:,::-1])
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x, y, x1, y1 = face_box
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x_s, y_s, x_e, y_e = crop_box
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face_large = body.crop(crop_box)
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mask_image = Image.fromarray(mask_array)
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mask_image = mask_image.convert("L")
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face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s))
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body.paste(face_large, crop_box[:2], mask_image)
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body = np.array(body)
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return body[:,:,::-1]
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def get_image_prepare_material(image, face_box, upper_boundary_ratio=0.5, expand=1.5, fp=None, mode="raw"):
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body = Image.fromarray(image[:,:,::-1])
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x, y, x1, y1 = face_box
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#print(x1-x,y1-y)
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crop_box, s = get_crop_box(face_box, expand)
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x_s, y_s, x_e, y_e = crop_box
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face_large = body.crop(crop_box)
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ori_shape = face_large.size
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mask_image = face_seg(face_large, mode=mode, fp=fp)
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mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s))
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mask_image = Image.new('L', ori_shape, 0)
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mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s))
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# keep upper_boundary_ratio of talking area
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width, height = mask_image.size
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top_boundary = int(height * upper_boundary_ratio)
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modified_mask_image = Image.new('L', ori_shape, 0)
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modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
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blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
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mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
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return mask_array, crop_box
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