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datasets/save_selective_search.py

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+import cv2
+import sys
+# from drawBoxes import draw_boxes
+import time
+import torch
+import matplotlib.pyplot as plt
+from selective_search import selective_search
+import numpy as np
+
+
+t1_train_file_path = "/home/selective_search/all_task_test.txt"
+
+with open(t1_train_file_path, 'r') as t1_train_file:
+    t1_train_list = t1_train_file.read().splitlines()
+    
+for image_num,image_name in enumerate(t1_train_list):
+    image_name = t1_train_list[image_num]
+    image_path = "/home//datasets/VOC2007/JPEGImages/" + image_name + ".jpg"
+    img = cv2.imread(image_path)[:, :, ::-1]
+    img_rgb = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
+    try:
+        boxes = selective_search(img_rgb, mode='single', random_sort=False)
+        new_flag = True
+        for boxes_i in boxes:
+            if new_flag:
+                boxes_sum = np.array(boxes_i).reshape(1,4)
+                new_flag = False
+            else:
+                boxes_sum = np.r_[boxes_sum,np.array(boxes_i).reshape(1,4)]
+        
+        boxes_draw = boxes_sum[:50,:]
+        img_save = {'image_size':img.shape[0:2],'obj_boxes':boxes_draw}
+
+    except:
+        print("cannot compute score:",image_name)
+        boxes_draw = []
+        img_save = {'image_size':img.shape[0:2],'obj_boxes':boxes_draw}
+        error_save_path = "/home/selective_search_save/selective_search_test_error/" + image_name + ".pickle"
+        torch.save(img_save, error_save_path)
+    img_save_path = "/home/selective_search_save/selective_search_test/" + image_name + ".pickle"
+    torch.save(img_save, img_save_path)
+    print("image_num:",image_num)