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create_imdb_clean_1024.py
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create_imdb_clean_1024.py
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from typing import List, Union
import cv2
import tqdm
import os
import sys
import os.path as osp
import numpy as np
from PIL import Image, ImageFile, ImageDraw
from multiprocessing import Pool
import pandas as pd
ImageFile.LOAD_TRUNCATED_IMAGES = True
Image.MAX_IMAGE_PIXELS = None
IMDB_DIR = './data/imdb'
MAX_SIDE = 1024
OUT_DIR = f'./data/imdb-clean-{MAX_SIDE}'
VIS_DIR = f'./data/imdb-clean-{MAX_SIDE}-visualisation'
def pil_loader(path):
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(osp.join(path), 'rb') as f:
with Image.open(f) as img:
return img.convert('RGB')
def crop_face_with_margin(img, box, crop_margin: Union[float, List[float]] = [0.4, 0.4, 0.4, 0.4]):
'''
img: H,W,3 array
box: x1,y1,x2,y2 list
adapted from https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/extractSubImage.m
'''
if isinstance(crop_margin, (float, int)):
crop_margin = [float(crop_margin)] * 4
elif isinstance(crop_margin, (list, tuple)):
assert len(
crop_margin) == 4, f'crop_margin has to be a float value or a list of four margins, got{crop_margin}'
else:
raise ValueError
h, w = img.shape[:2]
is_color = len(img.shape) > 2
# size of face
orig_size = [0, 0]
orig_size[0] = box[3]-box[1]+1
orig_size[1] = box[2]-box[0]+1
# add margin
full_crop = [0, 0, 0, 0]
full_crop[0] = round(box[0]-crop_margin[0]*orig_size[1])
full_crop[1] = round(box[1]-crop_margin[1]*orig_size[0])
full_crop[2] = round(box[2]+crop_margin[2]*orig_size[1])
full_crop[3] = round(box[3]+crop_margin[3]*orig_size[0])
# size of face with margin
new_size = [0, 0]
new_size[0] = full_crop[3]-full_crop[1]+1
new_size[1] = full_crop[2]-full_crop[0]+1
# ensure that the region cropped from the original image with margin doesn't go beyond the image size
crop = [0, 0, 0, 0]
crop[0] = max(full_crop[0], 0)
crop[1] = max(full_crop[1], 0)
crop[2] = min(full_crop[2], w-1)
crop[3] = min(full_crop[3], h-1)
# size of the actual region being cropped from the original image
crop_size = [0, 0]
crop_size[0] = crop[3]-crop[1]+1
crop_size[1] = crop[2]-crop[0]+1
if is_color:
new_img = np.zeros(new_size+[3], dtype=np.uint8)
else:
new_img = np.zeros(new_size, dtype=np.uint8)
# coordinates of region taken out of the original image in the new image
new_location = [0, 0, 0, 0]
new_location[0] = crop[0]-full_crop[0]
new_location[1] = crop[1]-full_crop[1]
new_location[2] = crop[0]-full_crop[0]+crop_size[1]-1
new_location[3] = crop[1]-full_crop[1]+crop_size[0]-1
# # coordinates of the face in the new image
new_box = [0, 0, 0, 0]
new_box[0] = new_location[0]+box[0]-crop[0]
new_box[1] = new_location[1]+box[1]-crop[1]
new_box[2] = new_location[2]+box[2]-crop[2]
new_box[3] = new_location[3]+box[3]-crop[3]
new_box = np.array(new_box, int)
# do the crop
new_img[new_location[1]:new_location[3], new_location[0]:new_location[2], ...] = \
img[crop[1]:crop[3], crop[0]:crop[2], ...]
return new_img, new_box
def resize_max_side(im: np.array, max_side: int,
bbox: Union[np.array, list] = None,
square: bool = False,
inter=cv2.INTER_LINEAR,
pad_mode: str = 'constant'):
h, w = im.shape[:2]
if h > w:
scale = max_side / float(h)
sz = (int(w * scale), max_side)
else:
scale = max_side / float(w)
sz = (max_side, int(h * scale))
im = cv2.resize(im, sz, inter)
if bbox is not None:
x1, y1, x2, y2 = bbox[:4]
x1, x2 = x1 * scale, x2 * scale
y1, y2 = y1 * scale, y2 * scale
bbox = np.array([x1, y1, x2, y2], int)
if square:
w_diff, h_diff = max_side-sz[0], max_side-sz[1]
h_pad = (h_diff//2, h_diff-h_diff//2)
w_pad = (w_diff//2, w_diff-w_diff//2)
pad_sz = [h_pad, w_pad]
if len(im.shape) > 2:
pad_sz += [(0, 0)]
im = np.pad(im, pad_sz, pad_mode)
if bbox is not None:
bbox[0] += w_pad[0]
bbox[2] += w_pad[0]
bbox[1] += h_pad[0]
bbox[3] += h_pad[0]
return im, bbox
def process(ind):
row = imdb_csv_split.iloc[ind].copy()
fn = osp.join(IMDB_DIR, row.filename)
img = np.array(pil_loader(fn))
x_min, y_min, x_max, y_max = map(
int, [row.x_min, row.y_min, row.x_max, row.y_max])
bbox = [x_min, y_min, x_max, y_max]
img, bbox = crop_face_with_margin(img, bbox, crop_margin=1.)
h, w = img.shape[:2]
if h > MAX_SIDE or w > MAX_SIDE:
img, bbox = resize_max_side(img, MAX_SIDE, bbox, square=True)
row.x_min, row.y_min, row.x_max, row.y_max = bbox
out_fn = fn.replace(IMDB_DIR, OUT_DIR)
os.makedirs(osp.dirname(out_fn), exist_ok=1)
img = Image.fromarray(img)
img.save(out_fn)
# visualisating some random images
if ind % 100 == 0:
draw = ImageDraw.Draw(img)
draw.text((row.x_min, row.y_min), f'Age: {row.age}')
draw.rectangle((row.x_min, row.y_min, row.x_max, row.y_max), outline=(255, 255, 255))
vis_fn = out_fn.replace(OUT_DIR, VIS_DIR)
os.makedirs(osp.dirname(vis_fn), exist_ok=1)
img.save(vis_fn)
return row
if __name__ == '__main__':
output = []
split = sys.argv[1]
imdb_csv_split = pd.read_csv(f'./csvs/imdb_{split}_new.csv')
with Pool(processes=16) as pool:
for row in tqdm.tqdm(pool.imap_unordered(process, imdb_csv_split.index), total=len(imdb_csv_split.index)):
output.append(row)
output = pd.concat(output, axis=1).transpose()
output.to_csv(f'{OUT_DIR}/imdb_{split}_new_{MAX_SIDE}.csv', index=False)