diff --git a/classifiers/object_classifiers/obj_data_io_helper.py b/classifiers/object_classifiers/obj_data_io_helper.py
new file mode 100644
index 0000000000000000000000000000000000000000..5a683295430fdb4438a4127c41b1fa70e2c67e16
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+++ b/classifiers/object_classifiers/obj_data_io_helper.py
@@ -0,0 +1,85 @@
+#Embedded file name: /home/tanmay/Code/GenVQA/GenVQA/classifiers/object_classifiers/obj_data_io_helper.py
+import json
+import sys
+import os
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import numpy as np
+import tensorflow as tf
+from scipy import misc
+
+def obj_mini_batch_loader(json_filename, image_dir, mean_image, 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,
+ img_height / 3,
+ img_width / 3,
+ channels])
+ obj_labels = np.zeros(shape=[9 * batch_size, 4])
+ for i in range(start_index, start_index + batch_size):
+ image_name = os.path.join(image_dir, str(i) + '.jpg')
+ image = misc.imresize(mpimg.imread(image_name), (img_height, img_width), interp='nearest')
+ crop_shape = np.array([image.shape[0], image.shape[1]]) / 3
+ selected_anno = [ q for q in json_data if q['image_id'] == i ]
+ grid_config = selected_anno[0]['config']
+ counter = 0
+ for grid_row in range(0, 3):
+ for grid_col in range(0, 3):
+ start_row = grid_row * crop_shape[0]
+ start_col = grid_col * crop_shape[1]
+ cropped_image = image[start_row:start_row + crop_shape[0], start_col:start_col + crop_shape[1], :]
+ if np.ndim(mean_image) == 0:
+ obj_images[9 * (i - start_index) + counter, :, :, :] = cropped_image / 254.0
+ else:
+ obj_images[9 * (i - start_index) + counter, :, :, :] = (cropped_image - mean_image) / 254
+ obj_labels[9 * (i - start_index) + counter, grid_config[6 * grid_row + 2 * grid_col]] = 1
+ counter = counter + 1
+
+ return (obj_images, obj_labels)
+
+
+def mean_image_batch(json_filename, image_dir, start_index, batch_size, img_height = 100, img_width = 100, channels = 3):
+ batch = obj_mini_batch_loader(json_filename, image_dir, np.empty([]), start_index, batch_size, img_height, img_width, channels)
+ mean_image = np.mean(batch[0], 0)
+ return mean_image
+
+
+def mean_image(json_filename, image_dir, num_images, batch_size, img_height = 100, img_width = 100, channels = 3):
+ max_iter = np.floor(num_images / batch_size)
+ mean_image = np.zeros([img_height / 3, img_width / 3, channels])
+ for i in range(max_iter.astype(np.int16)):
+ mean_image = mean_image + mean_image_batch(json_filename, image_dir, 1 + i * batch_size, batch_size, img_height, img_width, channels)
+
+ mean_image = mean_image / max_iter
+ tmp_mean_image = mean_image * 254
+ return mean_image
+
+
+class html_obj_table_writer:
+
+ def __init__(self, filename):
+ self.filename = filename
+ self.html_file = open(self.filename, 'w')
+ self.html_file.write('<!DOCTYPE html>\n<html>\n<body>\n<table border="1" style="width:100%"> \n')
+
+ def add_element(self, col_dict):
+ self.html_file.write(' <tr>\n')
+ for key in range(len(col_dict)):
+ self.html_file.write(' <td>{}</td>\n'.format(col_dict[key]))
+
+ self.html_file.write(' </tr>\n')
+
+ def image_tag(self, image_path, height, width):
+ return '<img src="{}" alt="IMAGE NOT FOUND!" height={} width={}>'.format(image_path, height, width)
+
+ def close_file(self):
+ self.html_file.write('</table>\n</body>\n</html>')
+ self.html_file.close()
+
+
+if __name__ == '__main__':
+ html_writer = html_obj_table_writer('/home/tanmay/Code/GenVQA/Exp_Results/Shape_Classifier_v_1/trial.html')
+ col_dict = {0: 'sam',
+ 1: html_writer.image_tag('something.png', 25, 25)}
+ html_writer.add_element(col_dict)
+ html_writer.close_file()