100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
from PIL import Image
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import numpy as np
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import cv2
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from face_parsing import FaceParsing
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fp = FaceParsing()
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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):
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seg_image = fp(image)
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if seg_image is None:
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print("error, no person_segment")
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return None
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seg_image = seg_image.resize(image.size)
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return seg_image
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def get_image(image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2):
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#print(image.shape)
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#print(face.shape)
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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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#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_position = (x, y)
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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)
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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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mask_image = Image.fromarray(mask_array)
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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=1.2):
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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)
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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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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] |