95 lines
3.6 KiB
Python
95 lines
3.6 KiB
Python
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import random
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import torch
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import numpy as np
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def _get_random_crop_indices(crop_region, crop_size):
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'''
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crop_region: (strat_y, end_y, start_x, end_x)
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crop_size: (y, x)
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'''
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region_size = (crop_region[1] - crop_region[0], crop_region[3] - crop_region[2])
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if region_size[0] < crop_size[0] or region_size[1] < crop_size[1]:
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print(region_size, crop_size)
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assert region_size[0] >= crop_size[0] and region_size[1] >= crop_size[1]
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if region_size[0] == crop_size[0]:
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start_y = crop_region[0]
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else:
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start_y = random.choice(range(crop_region[0], crop_region[1] - crop_size[0]))
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if region_size[1] == crop_size[1]:
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start_x = crop_region[2]
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else:
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start_x = random.choice(range(crop_region[2], crop_region[3] - crop_size[1]))
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return start_y, start_y + crop_size[0], start_x, start_x + crop_size[1]
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def _get_adaptive_crop_indices(crop_region, crop_size, num_candidate, dist_map, min_diff=False):
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candidates = [_get_random_crop_indices(crop_region, crop_size) for _ in range(num_candidate)]
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max_choice = candidates[0]
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min_choice = candidates[0]
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max_dist = 0
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min_dist = np.infty
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with torch.no_grad():
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for c in candidates:
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start_y, end_y, start_x, end_x = c
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dist = torch.sum(dist_map[start_y: end_y, start_x: end_x])
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if dist > max_dist:
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max_dist = dist
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max_choice = c
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if dist < min_dist:
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min_dist = dist
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min_choice = c
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if min_diff:
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return min_choice
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else:
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return max_choice
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def get_split_list(divisor, dividend):
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split_list = [dividend // divisor for _ in range(divisor - 1)]
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split_list.append(dividend - (dividend // divisor) * (divisor - 1))
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return split_list
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def random_sampler(pic_size, crop_dict):
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crop_region = (0, pic_size[0], 0, pic_size[1])
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crop_res_dict = {}
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for k, v in crop_dict.items():
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crop_size = (int(k), int(k))
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crop_res_dict[k] = [_get_random_crop_indices(crop_region, crop_size) for _ in range(v)]
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return crop_res_dict
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def region_sampler(crop_region, crop_dict):
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crop_res_dict = {}
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for k, v in crop_dict.items():
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crop_size = (int(k), int(k))
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crop_res_dict[k] = [_get_random_crop_indices(crop_region, crop_size) for _ in range(v)]
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return crop_res_dict
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def adaptive_sampler(pic_size, crop_dict, num_candidate_dict, dist_map, min_diff=False):
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crop_region = (0, pic_size[0], 0, pic_size[1])
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crop_res_dict = {}
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for k, v in crop_dict.items():
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crop_size = (int(k), int(k))
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crop_res_dict[k] = [_get_adaptive_crop_indices(crop_region, crop_size, num_candidate_dict[k], dist_map, min_diff) for _ in range(v)]
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return crop_res_dict
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# TODO more flexible
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def pyramid_sampler(pic_size, crop_dict):
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crop_res_dict = {}
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sorted_key = list(crop_dict.keys())
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sorted_key.sort(key=lambda x: int(x), reverse=True)
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k = sorted_key[0]
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crop_size = (int(k), int(k))
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crop_region = (0, pic_size[0], 0, pic_size[1])
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crop_res_dict[k] = [_get_random_crop_indices(crop_region, crop_size) for _ in range(crop_dict[k])]
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for i in range(1, len(sorted_key)):
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crop_res_dict[sorted_key[i]] = []
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afore_num = crop_dict[sorted_key[i-1]]
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new_num = crop_dict[sorted_key[i]]
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split_list = get_split_list(afore_num, new_num)
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crop_size = (int(sorted_key[i]), int(sorted_key[i]))
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for j in range(len(split_list)):
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crop_region = crop_res_dict[sorted_key[i-1]][j]
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crop_res_dict[sorted_key[i]].extend([_get_random_crop_indices(crop_region, crop_size) for _ in range(split_list[j])])
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return crop_res_dict
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