object_detection.py

# -*- coding: utf-8 -*-
"""
Created on Thu Jan 11 16:55:43 2018

@author: Xiang Guo
"""
#Imports
import time

import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import cv2

from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
import pandas as pd

if tf.__version__ < '1.4.0':
  raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')
  
os.chdir('E:\\tensorflow_models\\models\\research\\object_detection\\')
  
  
#Env setup 
# This is needed to display the images.
#%matplotlib inline

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")




#Object detection imports
from utils import label_map_util

from utils import visualization_utils as vis_util
 



#Model preparation
# What model to download.
#MODEL_NAME = 'tv_vehicle_inference_graph'
#MODEL_NAME = 'tv_vehicle_inference_graph_fasterCNN'
MODEL_NAME = 'tv_vehicle_inference_graph'
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'    #[30,21]  best
#MODEL_NAME = 'ssd_inception_v2_coco_2017_11_17'            #[42,24]
#MODEL_NAME = 'faster_rcnn_inception_v2_coco_2017_11_08'         #[58,28]
#MODEL_NAME = 'faster_rcnn_resnet50_coco_2017_11_08'     #[89,30]
#MODEL_NAME = 'faster_rcnn_resnet50_lowproposals_coco_2017_11_08'   #[64, ]
#MODEL_NAME = 'rfcn_resnet101_coco_2017_11_08'    #[106,32]

'''
MODEL_FILE = MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
'''

# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'

# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('training', 'tv_vehicle_detection.pbtxt')

NUM_CLASSES = 3



'''
#Download Model

opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
  file_name = os.path.basename(file.name)
  if 'frozen_inference_graph.pb' in file_name:
    tar_file.extract(file, os.getcwd())
'''   
    
    
#Load a (frozen) Tensorflow model into memory.    
detection_graph = tf.Graph()
with detection_graph.as_default():
  od_graph_def = tf.GraphDef()
  with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:
    serialized_graph = fid.read()
    od_graph_def.ParseFromString(serialized_graph)
    tf.import_graph_def(od_graph_def, name='')    
    
    
#Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)
category_index = label_map_util.create_category_index(categories)


#Helper code
def load_image_into_numpy_array(image):
  (im_width, im_height) = image.size
  return np.array(image.getdata()).reshape(
      (im_height, im_width, 3)).astype(np.uint8)


#Detection
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
PATH_TO_TEST_IMAGES_DIR = 'G:\\Resource for Xiang\\Lian Cui experiment\\eyetracking data\\2.New Experiment\\4.analysis\\framepics_base\\'
os.chdir(PATH_TO_TEST_IMAGES_DIR)
TEST_IMAGE_DIRS = os.listdir(PATH_TO_TEST_IMAGES_DIR)

# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)

output_image_path = ('G:\\Resource for Xiang\\Lian Cui experiment\\eyetracking data\\2.New Experiment\\4.analysis\\output\\pics_base\\')
output_csv_path = ('G:\\Resource for Xiang\\Lian Cui experiment\\eyetracking data\\2.New Experiment\\4.analysis\\output\\csv_base\\')

for image_folder in TEST_IMAGE_DIRS:
    start = time.time()
    with detection_graph.as_default():
      with tf.Session(graph=detection_graph) as sess:
        # Definite input and output Tensors for detection_graph
        image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
        # Each box represents a part of the image where a particular object was detected.
        detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
        # Each score represent how level of confidence for each of the objects.
        # Score is shown on the result image, together with the class label.
        detection_scores = detection_graph.get_tensor_by_name('detection_scores:0')
        detection_classes = detection_graph.get_tensor_by_name('detection_classes:0')
        num_detections = detection_graph.get_tensor_by_name('num_detections:0')
        TEST_IMAGE_PATHS = os.listdir(os.path.join(image_folder))
        os.makedirs(output_image_path+image_folder)
        data = pd.DataFrame()
        for image_path in TEST_IMAGE_PATHS:

          image = Image.open(image_folder + '//'+image_path)
          width, height = image.size
          # the array based representation of the image will be used later in order to prepare the
          # result image with boxes and labels on it.
          image_np = load_image_into_numpy_array(image)
          # Expand dimensions since the model expects images to have shape: [1, None, None, 3]
          image_np_expanded = np.expand_dims(image_np, axis=0)
          # Actual detection.
          (boxes, scores, classes, num) = sess.run(
              [detection_boxes, detection_scores, detection_classes, num_detections],
              feed_dict={image_tensor: image_np_expanded})
          # Visualization of the results of a detection.
          vis_util.visualize_boxes_and_labels_on_image_array(
              image_np,
              np.squeeze(boxes),
              np.squeeze(classes).astype(np.int32),
              np.squeeze(scores),
              category_index,
              use_normalized_coordinates=True,
              line_thickness=8)
          #write images
          cv2.imwrite(output_image_path+image_folder+'\\'+image_path.split('\\')[-1],image_np)
          
          s_boxes = boxes[scores > 0.5]
          s_classes = classes[scores > 0.5]
          s_scores=scores[scores>0.5]
          for i in range(len(s_classes)):

              newdata= pd.DataFrame(0, index=range(1), columns=range(7))
              newdata.iloc[0,0] = image_path.split("\\")[-1].split('.')[0]
              newdata.iloc[0,1] = s_boxes[i][0]*height  #ymin
              newdata.iloc[0,2] = s_boxes[i][1]*width     #xmin
              newdata.iloc[0,3] = s_boxes[i][2]*height    #ymax
              newdata.iloc[0,4] = s_boxes[i][3]*width     #xmax
              newdata.iloc[0,5] = s_scores[i]
              newdata.iloc[0,6] = s_classes[i]
    
              data = data.append(newdata)
        data.columns = ['framename', 'ymin', 'xmin', 'ymax', 'xmax', 'scores', 'class']
        data.to_csv(output_csv_path+image_folder+'.csv',index = False)
      
    end =  time.time()
    print("Execution Time: ", end - start)