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# -*- coding: utf-8 -*-
"""
# File name: crop.py
# Time : 2021/9/30 21:20
# Author: xyguoo@163.com
# Description:
"""
import os
import PIL.Image
import PIL.ImageFile
import numpy as np
import scipy.ndimage
import cv2
PIL.ImageFile.LOAD_TRUNCATED_IMAGES = True # avoid "Decompressed Data Too Large" error
def recreate_aligned_images(img, lm_68, output_size=1024, transform_size=4096, enable_padding=True):
pil_img = PIL.Image.fromarray(img)
lm_chin = lm_68[0: 17] # left-right
lm_eyebrow_left = lm_68[17: 22] # left-right
lm_eyebrow_right = lm_68[22: 27] # left-right
lm_nose = lm_68[27: 31] # top-down
lm_nostrils = lm_68[31: 36] # top-down
lm_eye_left = lm_68[36: 42] # left-clockwise
lm_eye_right = lm_68[42: 48] # left-clockwise
lm_mouth_outer = lm_68[48: 60] # left-clockwise
lm_mouth_inner = lm_68[60: 68] # left-clockwise
# Calculate auxiliary vectors.
eye_left = np.mean(lm_eye_left, axis=0)
eye_right = np.mean(lm_eye_right, axis=0)
eye_avg = (eye_left + eye_right) * 0.5
eye_to_eye = eye_right - eye_left
mouth_left = lm_mouth_outer[0]
mouth_right = lm_mouth_outer[6]
mouth_avg = (mouth_left + mouth_right) * 0.5
eye_to_mouth = mouth_avg - eye_avg
# Choose oriented crop rectangle.
x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]
x /= np.hypot(*x)
x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)
y = np.flipud(x) * [-1, 1]
c = eye_avg + eye_to_mouth * 0.1
quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])
qsize = np.hypot(*x) * 2
# Load in-the-wild image.
img = pil_img
trans_points = lm_68
# Shrink.
shrink = int(np.floor(qsize / output_size * 0.5))
if shrink > 1:
rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))
img = img.resize(rsize, PIL.Image.ANTIALIAS)
quad /= shrink
qsize /= shrink
trans_points = trans_points / shrink
# Crop.
border = max(int(np.rint(qsize * 0.1)), 3)
crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),
min(crop[3] + border, img.size[1]))
if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:
img = img.crop(crop)
quad -= crop[0:2]
trans_points = trans_points - np.array([crop[0], crop[1]])
# Pad.
pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),
int(np.ceil(max(quad[:, 1]))))
pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),
max(pad[3] - img.size[1] + border, 0))
if enable_padding and max(pad) > border - 4:
pad = np.maximum(pad, int(np.rint(qsize * 0.3)))
img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')
trans_points = trans_points + np.array([pad[0], pad[1]])
h, w, _ = img.shape
y, x, _ = np.ogrid[:h, :w, :1]
mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),
1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))
blur = qsize * 0.02
img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)
img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)
img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
quad += pad[:2]
# Transform.
trans_data = (quad + 0.5)
img = img.transform((transform_size, transform_size), PIL.Image.QUAD, trans_data.flatten(), PIL.Image.BILINEAR)
if output_size < transform_size:
img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)
projective_matrix = cv2.getPerspectiveTransform(trans_data.astype('float32'),
np.array([[0, 0], [0, 1], [1, 1], [1, 0]], dtype='float32'))
augmented_lm = projective_matrix @ np.concatenate([trans_points, np.ones([trans_points.shape[0], 1])], axis=1).T
trans_points = augmented_lm[:2, :] / augmented_lm[2] * output_size
trans_points = trans_points.T
trans_points = (trans_points + 0.5).astype('int32')
return img, trans_points[:68]
def draw_landmarks(landmarks, img_np, font_size=1.0):
font = cv2.FONT_HERSHEY_SIMPLEX
for idx, point in enumerate(landmarks):
pos = (point[0], point[1])
cv2.circle(img_np, pos, 2, color=(139, 0, 0))
cv2.putText(img_np, str(idx + 1), pos, font, font_size, (0, 0, 255), 1, cv2.LINE_AA)
return img_np
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