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aihub/deep-learning/face-paint
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aihub/deep-learning/face-paint/seg_demo.py
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aihub/deep-learning/face-paint/seg_demo.py
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import argparse
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import base64
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import datetime
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import os
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import sys
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import cv2
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import numpy as np
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from tqdm import tqdm
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from paddleseg.utils import get_sys_env, logger, get_image_list
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from infer import Predictor
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import os
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import dlib
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import collections
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from typing import Union, List
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import numpy as np
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from PIL import Image
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import matplotlib.pyplot as plt
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import torch
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from PIL import Image
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def get_bg_img(bg_img_path, img_shape):
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if bg_img_path is None:
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bg = 255 * np.ones(img_shape)
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elif not os.path.exists(bg_img_path):
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raise Exception('The --bg_img_path is not existed: {}'.format(
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bg_img_path))
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else:
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bg = cv2.imread(bg_img_path)
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return bg
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def makedirs(save_dir):
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dirname = save_dir if os.path.isdir(save_dir) else \
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os.path.dirname(save_dir)
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if not os.path.exists(dirname):
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os.makedirs(dirname)
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def seg_image(args):
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assert os.path.exists(args['img_path']), \
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"The --img_path is not existed: {}.".format(args['img_path'])
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logger.info("Input: image")
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logger.info("Create predictor...")
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predictor = Predictor(args)
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logger.info("Start predicting...")
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img = cv2.imread(args['re_save_path'])
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bg_img = get_bg_img(args['bg_img_path'], img.shape)
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out_img = predictor.run(img, bg_img)
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# print(type(out_img))
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cv2.imwrite(args['save_dir'], out_img)
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file = open(args['save_dir'], 'rb')
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base64_str = base64.b64encode(file.read()).decode('utf-8')
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print(len(base64_str))
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return base64_str
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# img_ = Image.open(out_img)
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# print(img_)
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def get_dlib_face_detector(predictor_path: str = "shape_predictor_68_face_landmarks.dat"):
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if not os.path.isfile(predictor_path):
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model_file = "shape_predictor_68_face_landmarks.dat.bz2"
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os.system(f"wget http://dlib.net/files/{model_file}")
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os.system(f"bzip2 -dk {model_file}")
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detector = dlib.get_frontal_face_detector()
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shape_predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')
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def detect_face_landmarks(img: Union[Image.Image, np.ndarray]):
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if isinstance(img, Image.Image):
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img = np.array(img)
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faces = []
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dets = detector(img)
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for d in dets:
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shape = shape_predictor(img, d)
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faces.append(np.array([[v.x, v.y] for v in shape.parts()]))
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return faces
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return detect_face_landmarks
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def display_facial_landmarks(
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img: Image,
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landmarks: List[np.ndarray],
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fig_size=[15, 15]
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):
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plot_style = dict(
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marker='o',
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markersize=4,
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linestyle='-',
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lw=2
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)
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pred_type = collections.namedtuple('prediction_type', ['slice', 'color'])
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pred_types = {
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'face': pred_type(slice(0, 17), (0.682, 0.780, 0.909, 0.5)),
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'eyebrow1': pred_type(slice(17, 22), (1.0, 0.498, 0.055, 0.4)),
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'eyebrow2': pred_type(slice(22, 27), (1.0, 0.498, 0.055, 0.4)),
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'nose': pred_type(slice(27, 31), (0.345, 0.239, 0.443, 0.4)),
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'nostril': pred_type(slice(31, 36), (0.345, 0.239, 0.443, 0.4)),
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'eye1': pred_type(slice(36, 42), (0.596, 0.875, 0.541, 0.3)),
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'eye2': pred_type(slice(42, 48), (0.596, 0.875, 0.541, 0.3)),
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'lips': pred_type(slice(48, 60), (0.596, 0.875, 0.541, 0.3)),
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'teeth': pred_type(slice(60, 68), (0.596, 0.875, 0.541, 0.4))
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}
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fig = plt.figure(figsize=fig_size)
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ax = fig.add_subplot(1, 1, 1)
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ax.imshow(img)
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ax.axis('off')
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for face in landmarks:
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for pred_type in pred_types.values():
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ax.plot(
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face[pred_type.slice, 0],
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face[pred_type.slice, 1],
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color=pred_type.color, **plot_style
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)
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plt.show()
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import PIL.Image
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import PIL.ImageFile
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import numpy as np
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import scipy.ndimage
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def align_and_crop_face(
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img: Image.Image,
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landmarks: np.ndarray,
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expand: float = 1.0,
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output_size: int = 1024,
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transform_size: int = 4096,
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enable_padding: bool = True,
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):
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# Parse landmarks.
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# pylint: disable=unused-variable
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lm = landmarks
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lm_chin = lm[0: 17] # left-right
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lm_eyebrow_left = lm[17: 22] # left-right
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lm_eyebrow_right = lm[22: 27] # left-right
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lm_nose = lm[27: 31] # top-down
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lm_nostrils = lm[31: 36] # top-down
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lm_eye_left = lm[36: 42] # left-clockwise
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lm_eye_right = lm[42: 48] # left-clockwise
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lm_mouth_outer = lm[48: 60] # left-clockwise
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lm_mouth_inner = lm[60: 68] # left-clockwise
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# Calculate auxiliary vectors.
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eye_left = np.mean(lm_eye_left, axis=0)
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eye_right = np.mean(lm_eye_right, axis=0)
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eye_avg = (eye_left + eye_right) * 0.5
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eye_to_eye = eye_right - eye_left
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mouth_left = lm_mouth_outer[0]
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mouth_right = lm_mouth_outer[6]
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mouth_avg = (mouth_left + mouth_right) * 0.5
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eye_to_mouth = mouth_avg - eye_avg
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# Choose oriented crop rectangle.
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x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
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x /= np.hypot(*x)
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x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
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x *= expand
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y = np.flipud(x) * [-1, 1]
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c = eye_avg + eye_to_mouth * 0.1
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quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
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qsize = np.hypot(*x) * 2
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# Shrink.
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shrink = int(np.floor(qsize / output_size * 0.5))
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if shrink > 1:
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rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
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img = img.resize(rsize, PIL.Image.ANTIALIAS)
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quad /= shrink
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qsize /= shrink
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# Crop.
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border = max(int(np.rint(qsize * 0.1)), 3)
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crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
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min(crop[3] + border, img.size[1]))
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if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
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img = img.crop(crop)
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quad -= crop[0:2]
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# Pad.
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pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
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int(np.ceil(max(quad[:, 1]))))
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pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
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max(pad[3] - img.size[1] + border, 0))
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if enable_padding and max(pad) > border - 4:
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pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
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img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
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h, w, _ = img.shape
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y, x, _ = np.ogrid[:h, :w, :1]
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mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
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1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
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blur = qsize * 0.02
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img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
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img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
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img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
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quad += pad[:2]
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# Transform.
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img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)
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if output_size < transform_size:
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img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
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return img
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def start(
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config=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/inference_models/portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax/deploy.yaml',
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img_path=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/1.jpg',
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bg_img_path=r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/2.jpg',
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re_save_path=r'temp/1_.jpg',
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save_dir=r'temp/1.jpg',
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use_gpu=True,
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test_speed=False, use_optic_flow=False, use_post_process=False):
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args = {
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'config': config,
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'img_path': img_path,
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'bg_img_path': bg_img_path,
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're_save_path': re_save_path,
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'save_dir': save_dir,
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'use_gpu': use_gpu,
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'test_speed': test_speed,
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'use_optic_flow': use_optic_flow,
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'use_post_process': use_post_process
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}
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print(type(args))
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# 先动漫化后增加背景效果更佳
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# 加载网络或本地文件
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save_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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os.mkdir(save_)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = torch.hub.load("bryandlee/animegan2-pytorch:main", "generator", device=device).eval()
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face2paint = torch.hub.load("bryandlee/animegan2-pytorch:main", "face2paint", device=device, side_by_side=True)
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img = Image.open(args['img_path']).convert("RGB")
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# img = Image.open("/content/sample.jpg").convert("RGB")
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face_detector = get_dlib_face_detector()
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landmarks = face_detector(img)
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out = ''
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for landmark in landmarks:
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face = align_and_crop_face(img, landmark, expand=1.3)
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p_face = face2paint(model=model, img=face, size=512)
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# display(p_face)
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# p_face.save('1.png') # 此输出为对比图片
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# 裁剪为需要的部分输出
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x_, y_ = p_face.size
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out = p_face.crop((int(x_ / 2), 0, x_, y_))
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img_ = out
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x, y = img_.size
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print(x, y)
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all_list = []
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for i in range(5):
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newIm = Image.new('RGB', (int(x * 1.5), int(y * 1.5)), 'white')
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newIm.paste(img_, (int(x * 0.5), int(y * 0.45)))
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# args['re_save_path'] = newIm
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newIm.save(args['re_save_path'])
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base64_ = seg_image(args)
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all_list.append({f'{i}': base64_})
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if __name__ == "__main__":
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image_path = r'/home/JLWL/PaddleSeg-release-2.6/contrib/PP-HumanSeg/src/data/images/human.jpg'
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file_after = open(image_path, 'rb')
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base64_after_str = base64.b64encode(file_after.read()).decode('utf-8')
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print(len(base64_after_str))
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imgdata = base64.b64decode(base64_after_str)
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# 将图片保存为文件
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if os.path.exists('temp'):
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pass
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else:
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os.mkdir('temp')
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name_ = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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new_image_path = f'temp/{name_}.jpg'
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with open(new_image_path, 'wb') as f:
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f.write(imgdata)
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start(
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# config=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\inference_models\portrait_pp_humansegv2_lite_256x144_inference_model_with_softmax\deploy.yaml',
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img_path=new_image_path,
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# bg_img_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\bg_1.jpg',
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# re_save_path=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images\_1.jpg',
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# save_dir=r'E:\PaddleSeg-release-2.6\contrib\PP-HumanSeg\src\data\images_result\1.jpg',
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# use_gpu=True,
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# test_speed=False, use_optic_flow=False, use_post_process=False)
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)
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