app.py

from flask import Flask, render_template, request, redirect, url_for, send_from_directory
from werkzeug.utils import secure_filename
import numpy as np
import os
import sys
import tensorflow as tf
from PIL import Image

sys.path.append("..")
from FlaskObjectDetection.utils import label_map_util

from FlaskObjectDetection.utils import visualization_utils as vis_util

# from utils import visualization_utils as vis_util
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
PATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90

detection_graph = tf.Graph()
with detection_graph.as_default():
    od_graph_def = tf.compat.v1.GraphDef()
    with tf.compat.v2.io.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='')
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)


app = Flask(__name__)

app.config['UPLOAD_FOLDER'] = 'uploads/'
app.config['ALLOWED_EXTENSIONS'] = set(['png', 'jpg', 'jpeg'])


def allowed_file(filename):
    return '.' in filename and \
           filename.rsplit('.', 1)[1] in app.config['ALLOWED_EXTENSIONS']


@app.route('/')
def index():
    return render_template('index.html')


@app.route('/upload', methods=['POST'])
def upload():
    file = request.files['file']
    if file and allowed_file(file.filename):
        filename = secure_filename(file.filename)
        file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
        return redirect(url_for('uploaded_file',
                                filename=filename))


@app.route('/uploads/<filename>')
def uploaded_file(filename):
    PATH_TO_TEST_IMAGES_DIR = app.config['UPLOAD_FOLDER']
    TEST_IMAGE_PATHS = [os.path.join(PATH_TO_TEST_IMAGES_DIR, filename.format(i)) for i in range(1, 2)]
    IMAGE_SIZE = (12, 8)

    with detection_graph.as_default():
        with tf.compat.v1.Session(graph=detection_graph) as sess:
            for image_path in TEST_IMAGE_PATHS:
                image = Image.open(image_path)
                image_np = load_image_into_numpy_array(image)
                image_np_expanded = np.expand_dims(image_np, axis=0)
                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
                boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
                scores = detection_graph.get_tensor_by_name('detection_scores:0')
                classes = detection_graph.get_tensor_by_name('detection_classes:0')
                num_detections = detection_graph.get_tensor_by_name('num_detections:0')
                (boxes, scores, classes, num_detections) = sess.run(
                    [boxes, scores, classes, num_detections],
                    feed_dict={image_tensor: image_np_expanded})
                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)
                im = Image.fromarray(image_np)
                im.save('uploads/' + filename)

    return send_from_directory(app.config['UPLOAD_FOLDER'],
                               filename)


if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)