object_detection_Images_Only_Resnet.py~

#Object detection CNN

#Modules imported
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
import time
import sys
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from gtts import gTTS

#to check if the system runs the required tensorflow version
if tf.__version__ < '1.4.0':
    raise ImportError('Please upgrade your tensorflow installation to v1.4.* or later!')


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

# ## Object detection imports
# Here are the imports from the object detection module.
from utils import label_map_util

from utils import visualization_utils as vis_util

# # Model preparation

# ## Variables
#
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_CKPT` to point to a new .pb file.

# What model to download.
MODEL_NAME = 'faster_rcnn_resnet50_coco_2017_11_08'
MODEL_FILE = MODEL_NAME + '.tar.gz'

# 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('data', 'mscoco_label_map.pbtxt')

NUM_CLASSES = 90

# ## 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 maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`.  Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine

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)


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

PATH_TO_TEST_IMAGES_DIR = 'pi_images'

TEST_IMAGE_PATHS = os.path.join(PATH_TO_TEST_IMAGES_DIR, "example.jpg")
# Size, in inches, of the output images.
IMAGE_SIZE = (30, 20)

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')
        #for image_path in TEST_IMAGE_PATHS:
        while TEST_IMAGE_PATHS:
            # ret, image_np = cap.read()
            image = Image.open(TEST_IMAGE_PATHS)
            # 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)
            matching_classes = set(np.squeeze(classes).astype(np.int32)[:10])

            #remove the earlier record file
            os.remove("record.txt")

            #open the record.txt file for wrtiting results
            recordFile = open("record.txt", mode="a")

            #write the current results into record.txt
            print("The objects in the image:", file=recordFile)

            for i in matching_classes:
                if (np.squeeze(scores)[i] >= 0.10):
                    print(category_index[i]['name'], file=recordFile)

            #close the record.txt file previously opened
            recordFile.close()

            #exit the processing for now
            break