| import os |
| import numpy as np |
| |
| from PIL import Image |
| from utils import paramUtil |
| import math |
| import time |
| import matplotlib.pyplot as plt |
| from scipy.ndimage import gaussian_filter |
|
|
|
|
| def mkdir(path): |
| if not os.path.exists(path): |
| os.makedirs(path) |
|
|
| COLORS = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], |
| [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], |
| [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]] |
|
|
| MISSING_VALUE = -1 |
|
|
| def save_image(image_numpy, image_path): |
| img_pil = Image.fromarray(image_numpy) |
| img_pil.save(image_path) |
|
|
|
|
| def save_logfile(log_loss, save_path): |
| with open(save_path, 'wt') as f: |
| for k, v in log_loss.items(): |
| w_line = k |
| for digit in v: |
| w_line += ' %.3f' % digit |
| f.write(w_line + '\n') |
|
|
| def as_minutes(s): |
| m = math.floor(s / 60) |
| s -= m * 60 |
| return '%dm %ds' % (m, s) |
| |
| def print_current_loss(accelerator,start_time, niter_state, losses, epoch=None, inner_iter=None): |
| def as_minutes(s): |
| m = math.floor(s / 60) |
| s -= m * 60 |
| return '%dm %ds' % (m, s) |
|
|
| def time_since(since, percent): |
| now = time.time() |
| s = now - since |
| es = s / percent |
| rs = es - s |
| return '%s (- %s)' % (as_minutes(s), as_minutes(rs)) |
|
|
| if epoch is not None: |
| |
| accelerator.print('epoch: %3d niter: %6d inner_iter: %4d' % (epoch, niter_state, inner_iter), end=" ") |
|
|
| now = time.time() |
| message = '%s'%(as_minutes(now - start_time)) |
|
|
| for k, v in losses.items(): |
| message += ' %s: %.4f ' % (k, v) |
| |
| accelerator.print(message) |
|
|
|
|
| def compose_gif_img_list(img_list, fp_out, duration): |
| img, *imgs = [Image.fromarray(np.array(image)) for image in img_list] |
| img.save(fp=fp_out, format='GIF', append_images=imgs, optimize=False, |
| save_all=True, loop=0, duration=duration) |
|
|
|
|
| def save_images(visuals, image_path): |
| if not os.path.exists(image_path): |
| os.makedirs(image_path) |
|
|
| for i, (label, img_numpy) in enumerate(visuals.items()): |
| img_name = '%d_%s.jpg' % (i, label) |
| save_path = os.path.join(image_path, img_name) |
| save_image(img_numpy, save_path) |
|
|
|
|
| def save_images_test(visuals, image_path, from_name, to_name): |
| if not os.path.exists(image_path): |
| os.makedirs(image_path) |
|
|
| for i, (label, img_numpy) in enumerate(visuals.items()): |
| img_name = "%s_%s_%s" % (from_name, to_name, label) |
| save_path = os.path.join(image_path, img_name) |
| save_image(img_numpy, save_path) |
|
|
|
|
| def compose_and_save_img(img_list, save_dir, img_name, col=4, row=1, img_size=(256, 200)): |
| |
| compose_img = compose_image(img_list, col, row, img_size) |
| if not os.path.exists(save_dir): |
| os.makedirs(save_dir) |
| img_path = os.path.join(save_dir, img_name) |
| |
| compose_img.save(img_path) |
|
|
|
|
| def compose_image(img_list, col, row, img_size): |
| to_image = Image.new('RGB', (col * img_size[0], row * img_size[1])) |
| for y in range(0, row): |
| for x in range(0, col): |
| from_img = Image.fromarray(img_list[y * col + x]) |
| |
| |
| paste_area = (x * img_size[0], y*img_size[1], |
| (x + 1) * img_size[0], (y + 1) * img_size[1]) |
| to_image.paste(from_img, paste_area) |
| |
| return to_image |
|
|
|
|
| def list_cut_average(ll, intervals): |
| if intervals == 1: |
| return ll |
|
|
| bins = math.ceil(len(ll) * 1.0 / intervals) |
| ll_new = [] |
| for i in range(bins): |
| l_low = intervals * i |
| l_high = l_low + intervals |
| l_high = l_high if l_high < len(ll) else len(ll) |
| ll_new.append(np.mean(ll[l_low:l_high])) |
| return ll_new |
|
|
|
|
| def motion_temporal_filter(motion, sigma=1): |
| motion = motion.reshape(motion.shape[0], -1) |
| |
| for i in range(motion.shape[1]): |
| motion[:, i] = gaussian_filter(motion[:, i], sigma=sigma, mode="nearest") |
| return motion.reshape(motion.shape[0], -1, 3) |
|
|
|
|
