code to get a batch of shape images as 4D ndarray

object_classifiers/obj_data_io_helper.py

@@ -6,19 +6,19 @@ import matplotlib.image as mpimg
 import numpy as np
 
 
-def obj_mini_batch_loader(json_filename, start_index, batch_size, im):
+def obj_mini_batch_loader(json_filename, start_index, batch_size, img_height=100, img_width=100, channels=3):
 
     with open(json_filename, 'r') as json_file: 
         json_data = json.load(json_file)
 
-    obj_images = np.empty(shape=[9*batch_size, ])
-    obj_labels = []
+    obj_images = np.empty(shape=[9*batch_size, img_height, img_width, channels])
+    obj_labels = np.empty(shape=[9*batch_size])
 
     for i in range(start_index, start_index + batch_size):
         image_name = os.path.join(image_dir, str(i+1) + '.jpg')
         image = mpimg.imread(image_name)
-        crop_shape = [image.shape[0]/3, image.shape[1]/3]
-        selected_anno = [q for q in json_data if q['image_id']==1]
+        crop_shape = np.array([image.shape[0], image.shape[1]])/3
+        selected_anno = [q for q in json_data if q['image_id']==i+1]
         grid_config = selected_anno[0]['config']
         
         counter = 0;
@@ -27,14 +27,15 @@ def obj_mini_batch_loader(json_filename, start_index, batch_size, im):
                 start_row = grid_row*crop_shape[0]
                 start_col = grid_col*crop_shape[1]
                 print([start_row, start_col])
-                obj_images.append(image[start_row:start_row+crop_shape[0]-1, start_col:start_col+crop_shape[1]-1, :])
-                obj_labels.append(grid_config[6*grid_row+2*grid_col])
-                # imgplot = plt.imshow(obj_images[counter])
-                # plt.show()
+                cropped_image = image[start_row:start_row+crop_shape[0], start_col:start_col+crop_shape[1], :]
+                obj_images[9*(i-start_index)+counter,:,:,:] = cropped_image
+                obj_labels[9*(i-start_index)+counter] = grid_config[6*grid_row+2*grid_col]
                 counter = counter + 1
 
-
-
+    # imgplot = plt.imshow(obj_images[0,:,:,:].astype(np.uint8))
+    # plt.show()
+    
+        
     
 
 if __name__=="__main__":