| import h5py |
| import scipy.io as io |
| import numpy as np |
| import os |
| import glob |
| from matplotlib import pyplot as plt |
| from multiprocessing import Pool |
| from functools import partial |
|
|
| def process(idx, img_paths): |
| img_path = img_paths[idx] |
| mat = io.loadmat(img_path.replace('.jpg','.mat').replace('images','ground_truth').replace('img','GT_img')) |
| img = plt.imread(img_path) |
| base_range = 5 |
| off1 = np.zeros((int(img.shape[0]/2),int(img.shape[1]/2),2)) |
| off2 = np.zeros((int(img.shape[0]/4),int(img.shape[1]/4),2)) |
| off3 = np.zeros((int(img.shape[0]/8),int(img.shape[1]/8),2)) |
| ids = np.zeros((int(img.shape[0]/2),int(img.shape[1]/2)), np.int16) |
| gt = mat["image_info"][0,0][0,0][0] |
|
|
| for i in range(len(gt)): |
| for j in range(3): |
| h, w = int(img.shape[0]/2**(j+1)), int(img.shape[1]/2**(j+1)) |
| y, x = int(gt[i][1]/2**(j+1)), int(gt[i][0]/2**(j+1)) |
| if x < w and y < h: |
| if j == 0: |
| ids[y,x] = gt[i][2] |
| trk_range = int(base_range/(j+1.)) |
| left = max(0, x - trk_range) |
| right = min(w-1, x + trk_range) |
| top = max(0, y - trk_range) |
| down = min(h-1, y + trk_range) |
| for n in range(left,right+1,1): |
| for m in range(top,down+1,1): |
| if j == 0: |
| off1[m,n,0], off1[m,n,1] = y-m, x-n |
| elif j == 1: |
| off2[m,n,0], off2[m,n,1] = y-m, x-n |
| elif j == 2: |
| off3[m,n,0], off3[m,n,1] = y-m, x-n |
|
|
| with h5py.File(img_path.replace('.jpg','_max.h5').replace('images','ground_truth'), 'w') as hf: |
| hf['identity'] = ids |
| hf['offset1'] = off1 |
| hf['offset2'] = off2 |
| hf['offset3'] = off3 |
|
|
| print(idx, len(img_paths)) |
|
|
|
|
| part_train = os.path.join('train_data','images') |
| part_test = os.path.join('val_data','images') |
|
|
| img_paths = [] |
| for img_path in glob.glob(os.path.join(part_train, '*.jpg')): |
| img_paths.append(img_path) |
| for img_path in glob.glob(os.path.join(part_test, '*.jpg')): |
| img_paths.append(img_path) |
| img_paths.sort() |
|
|
| pool = Pool(10) |
| partial = partial(process, img_paths=img_paths) |
| _ = pool.map(partial, range(len(img_paths))) |
