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RCNN/preprocessing_RCNN.py /
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| from __future__ import division, print_function, absolute_import | |
| import numpy as np | |
| import selectivesearch | |
| import tools | |
| import cv2 | |
| import config | |
| import os | |
| import random | |
| def resize_image(in_image, new_width, new_height, out_image=None, resize_mode=cv2.INTER_CUBIC): | |
| img = cv2.resize(in_image, (new_width, new_height), resize_mode) | |
| if out_image: | |
| cv2.imwrite(out_image, img) | |
| return img | |
| # IOU Part 1 | |
| def if_intersection(xmin_a, xmax_a, ymin_a, ymax_a, xmin_b, xmax_b, ymin_b, ymax_b): | |
| if_intersect = False | |
| if xmin_a < xmax_b <= xmax_a and (ymin_a < ymax_b <= ymax_a or ymin_a <= ymin_b < ymax_a): | |
| if_intersect = True | |
| elif xmin_a <= xmin_b < xmax_a and (ymin_a < ymax_b <= ymax_a or ymin_a <= ymin_b < ymax_a): | |
| if_intersect = True | |
| elif xmin_b < xmax_a <= xmax_b and (ymin_b < ymax_a <= ymax_b or ymin_b <= ymin_a < ymax_b): | |
| if_intersect = True | |
| elif xmin_b <= xmin_a < xmax_b and (ymin_b < ymax_a <= ymax_b or ymin_b <= ymin_a < ymax_b): | |
| if_intersect = True | |
| else: | |
| return if_intersect | |
| if if_intersect: | |
| x_sorted_list = sorted([xmin_a, xmax_a, xmin_b, xmax_b]) | |
| y_sorted_list = sorted([ymin_a, ymax_a, ymin_b, ymax_b]) | |
| x_intersect_w = x_sorted_list[2] - x_sorted_list[1] | |
| y_intersect_h = y_sorted_list[2] - y_sorted_list[1] | |
| area_inter = x_intersect_w * y_intersect_h | |
| return area_inter | |
| # IOU Part 2 | |
| def IOU(ver1, vertice2): | |
| # vertices in four points | |
| vertice1 = [ver1[0], ver1[1], ver1[0]+ver1[2], ver1[1]+ver1[3]] | |
| area_inter = if_intersection(vertice1[0], vertice1[2], vertice1[1], vertice1[3], vertice2[0], vertice2[2], vertice2[1], vertice2[3]) | |
| if area_inter: | |
| area_1 = ver1[2] * ver1[3] | |
| area_2 = vertice2[4] * vertice2[5] | |
| iou = float(area_inter) / (area_1 + area_2 - area_inter) | |
| return iou | |
| return False | |
| # Clip Image | |
| def clip_pic(img, rect): | |
| x = rect[0] | |
| y = rect[1] | |
| w = rect[2] | |
| h = rect[3] | |
| x_1 = x + w | |
| y_1 = y + h | |
| # return img[x:x_1, y:y_1, :], [x, y, x_1, y_1, w, h] | |
| return img[y:y_1, x:x_1, :], [x, y, x_1, y_1, w, h] | |
| # Read in data and save data for Alexnet | |
| def load_train_proposals(datafile, num_clss, save_path, threshold=0.5, is_svm=False, save=False): | |
| fr = open(datafile, 'r') | |
| train_list = fr.readlines() | |
| # random.shuffle(train_list) | |
| for num, line in enumerate(train_list): | |
| labels = [] | |
| images = [] | |
| tmp = line.strip().split(' ') | |
| # tmp0 = image address | |
| # tmp1 = label | |
| # tmp2 = rectangle vertices | |
| img = cv2.imread(tmp[0]) | |
| img_lbl, regions = selectivesearch.selective_search( | |
| img, scale=500, sigma=0.9, min_size=10) | |
| candidates = set() | |
| for r in regions: | |
| # excluding same rectangle (with different segments) | |
| if r['rect'] in candidates: | |
| continue | |
| # excluding small regions | |
| if r['size'] < 220: | |
| continue | |
| if (r['rect'][2] * r['rect'][3]) < 500: | |
| continue | |
| # resize to 227 * 227 for input | |
| proposal_img, proposal_vertice = clip_pic(img, r['rect']) | |
| # Delete Empty array | |
| if len(proposal_img) == 0: | |
| continue | |
| # Ignore things contain 0 or not C contiguous array | |
| x, y, w, h = r['rect'] | |
| if w == 0 or h == 0: | |
| continue | |
| # Check if any 0-dimension exist | |
| [a, b, c] = np.shape(proposal_img) | |
| if a == 0 or b == 0 or c == 0: | |
| continue | |
| resized_proposal_img = resize_image(proposal_img, config.IMAGE_SIZE, config.IMAGE_SIZE) | |
| candidates.add(r['rect']) | |
| img_float = np.asarray(resized_proposal_img, dtype="float32") | |
| images.append(img_float) | |
| # IOU | |
| ref_rect = tmp[2].split(',') | |
| ref_rect_int = [int(i) for i in ref_rect] | |
| iou_val = IOU(ref_rect_int, proposal_vertice) | |
| # labels, let 0 represent default class, which is background | |
| index = int(tmp[1]) | |
| if is_svm: | |
| if iou_val < threshold: | |
| labels.append(0) | |
| else: | |
| labels.append(index) | |
| else: | |
| label = np.zeros(num_clss + 1) | |
| if iou_val < threshold: | |
| label[0] = 1 | |
| else: | |
| label[index] = 1 | |
| labels.append(label) | |
| tools.view_bar("processing image of %s" % datafile.split('\\')[-1].strip(), num + 1, len(train_list)) | |
| if save: | |
| np.save((os.path.join(save_path, tmp[0].split('/')[-1].split('.')[0].strip()) + '_data.npy'), [images, labels]) | |
| print(' ') | |
| fr.close() | |
| # load data | |
| def load_from_npy(data_set): | |
| images, labels = [], [] | |
| data_list = os.listdir(data_set) | |
| # random.shuffle(data_list) | |
| for ind, d in enumerate(data_list): | |
| i, l = np.load(os.path.join(data_set, d)) | |
| images.extend(i) | |
| labels.extend(l) | |
| tools.view_bar("load data of %s" % d, ind + 1, len(data_list)) | |
| print(' ') | |
| return images, labels |