classifiers/object_classifiers/obj_data_io_helper.py

39219523 · tgupta6 · b9351f74 · 39219523
Commit 39219523 authored 9 years ago by tgupta6
--- a/classifiers/object_classifiers/obj_data_io_helper.py
+++ b/classifiers/object_classifiers/obj_data_io_helper.py
+#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()