app.py

import streamlit as st
from main import Main
import numpy as np
import os
import sys
import tarfile
import tensorflow as tf
import zipfile
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
from distutils.version import StrictVersion
import datetime
from io import StringIO
import matplotlib.pyplot as plt
from PIL import Image
 
 
 

st.title("Face Mask Detection 😊")
st.write('Upload an jpg or png image < 1MB file and after upload finish click on submit and wait for magic 😀')

uploaded_file = st.file_uploader("Choose an image...", type=['jpg','jpeg','png'])

PATH_TO_LABELS = os.getcwd() + '/training/labelmap.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(
    PATH_TO_LABELS, use_display_name=True)





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)


def Graph():
    print("> ====== Loading frozen graph into memory")
    PATH_TO_FROZEN_GRAPH = os.getcwd() + '/inference_graph/frozen_inference_graph.pb'
    # List of the strings that is used to add correct label for each box.
    PATH_TO_LABELS = os.getcwd() + '/training/labelmap.pbtxt'
    detection_graph = tf.Graph()
    with detection_graph.as_default():
        od_graph_def = tf.GraphDef()
        with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
            serialized_graph = fid.read()
            od_graph_def.ParseFromString(serialized_graph)
            tf.import_graph_def(od_graph_def, name='')
        return detection_graph


def run_inference_for_single_image(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[1], image.shape[2])
                detection_masks_reframed = tf.cast(
                    tf.greater(detection_masks_reframed, 0.5), 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: image})

            # 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



def main():
    if uploaded_file is not None:
        image = Image.open(uploaded_file)
        print("size of",sys.getsizeof(image.tobytes()))
        if sys.getsizeof(image.tobytes()) > 8570722:
            st.title('Image Size is greater than 1mb')
            return 
        if st.button('submit'):
            graph = Graph()
            image_np = load_image_into_numpy_array(image)
            image_np_expanded = np.expand_dims(image_np, axis=0)
            output_dict = run_inference_for_single_image(image_np_expanded, graph)
            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=8)
            PIL_image = Image.fromarray(np.uint8(image_np)).convert('RGB')
            st.image(PIL_image, caption='Detected image.')
             
         
main()