object_detection_images.py

# RUNNING INFERENCE
import numpy as np
import os
import cv2
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from google.colab.patches import cv2_imshow
from collections import defaultdict
from io import StringIO
import matplotlib
import pandas as pd
import matplotlib.pyplot as plt

from PIL import Image

# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops

# This is needed to display the images.
%matplotlib inline

from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

# Change these values for the model used
num_classes = 2 # Change this value to the number of classes of the model
IMAGE_SIZE = (12, 8) # Output display size as you want

# Use images in test dir
IMAGE_DIR = "/mydrive/images"
IMAGE_PATHS = []
for file in os.listdir(IMAGE_DIR):
    if file.endswith(".jpg") or file.endswith(".png"):
        IMAGE_PATHS.append(os.path.join(IMAGE_DIR, file))

# Set paths to the trained model
PATH_TO_LABELS = '/content/gdrive/MyDrive/customTF1/data/label_map.pbtxt'
PATH_TO_CKPT = os.path.join(os.path.abspath("/content/gdrive/MyDrive/customTF1/data/inference_graph"), "frozen_inference_graph.pb")


# Set tensorflow graph
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='')

# Set categories
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)

# Convert input image to a numpy array
def load_image_to_numpy(image):
    (im_width, im_height) = image.size
    return np.array(image.getdata()).reshape(
        (im_height, im_width, 3)).astype(np.uint8)

# Inference pipeline
def run_inference(image, graph):
    with graph.as_default():
        with tf.Session() as sess:
            # Get handles to input and output tensors
            ops = tf.get_default_graph().get_operations()
            all_tensor_names = {
                output.name for op in ops for output in op.outputs}
            tensor_dict = {}
            for key in [
                'num_detections', 'detection_boxes', 'detection_scores',
                'detection_classes', 'detection_masks'
            ]:
                tensor_name = key + ':0'
                if tensor_name in all_tensor_names:
                    tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
                        tensor_name)
            if 'detection_masks' in tensor_dict:
                # The following processing is only for single image
                detection_boxes = tf.squeeze(
                    tensor_dict['detection_boxes'], [0])
                detection_masks = tf.squeeze(
                    tensor_dict['detection_masks'], [0])
                # Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
                real_num_detection = tf.cast(
                    tensor_dict['num_detections'][0], tf.int32)
                detection_boxes = tf.slice(detection_boxes, [0, 0], [
                                           real_num_detection, -1])
                detection_masks = tf.slice(detection_masks, [0, 0, 0], [
                                           real_num_detection, -1, -1])
                detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
                    detection_masks, detection_boxes, image.shape[0], image.shape[1])
                detection_masks_reframed = tf.cast(
                    tf.greater(detection_masks_reframed, .5), tf.uint8)
                # Follow the convention by adding back the batch dimension
                tensor_dict['detection_masks'] = tf.expand_dims(
                    detection_masks_reframed, 0)
            image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')

            # Run inference
            output_dict = sess.run(tensor_dict,
                                   feed_dict={image_tensor: np.expand_dims(image, 0)})

            # all outputs are float32 numpy arrays, so convert types as appropriate
            output_dict['num_detections'] = int(
                output_dict['num_detections'][0])
            output_dict['detection_classes'] = output_dict[
                'detection_classes'][0].astype(np.uint8)
            output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
            output_dict['detection_scores'] = output_dict['detection_scores'][0]
            if 'detection_masks' in output_dict:
                output_dict['detection_masks'] = output_dict['detection_masks'][0]
    return output_dict

# Run the inference for each image
for image_path in IMAGE_PATHS:
    image = Image.open(image_path)
    # Conver the image to numpy array
    image_np = load_image_to_numpy(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)
    # Perform the interence
    output_dict = run_inference(image_np, detection_graph)
    # Visualize
    vis_util.visualize_boxes_and_labels_on_image_array(
        image_np,
        output_dict['detection_boxes'],
        output_dict['detection_classes'],
        output_dict['detection_scores'],
        category_index,
        instance_masks=output_dict.get('detection_masks'),
        use_normalized_coordinates=True,
        line_thickness=20,
        min_score_thresh=0.1)
    plt.figure(figsize=IMAGE_SIZE, dpi=200)
    plt.axis("off")
    plt.imshow(image_np)