1st commit

.gitattributes

@@ -32,3 +32,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

Tensorflow/scripts/generate_tfrecord.py

@@ -0,0 +1,168 @@
+""" Sample TensorFlow XML-to-TFRecord converter
+
+usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH]
+
+optional arguments:
+  -h, --help            show this help message and exit
+  -x XML_DIR, --xml_dir XML_DIR
+                        Path to the folder where the input .xml files are stored.
+  -l LABELS_PATH, --labels_path LABELS_PATH
+                        Path to the labels (.pbtxt) file.
+  -o OUTPUT_PATH, --output_path OUTPUT_PATH
+                        Path of output TFRecord (.record) file.
+  -i IMAGE_DIR, --image_dir IMAGE_DIR
+                        Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR.
+  -c CSV_PATH, --csv_path CSV_PATH
+                        Path of output .csv file. If none provided, then no file will be written.
+"""
+
+import os
+import glob
+import pandas as pd
+import io
+import xml.etree.ElementTree as ET
+import argparse
+
+os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'    # Suppress TensorFlow logging (1)
+import tensorflow.compat.v1 as tf
+from PIL import Image
+from object_detection.utils import dataset_util, label_map_util
+from collections import namedtuple
+
+# Initiate argument parser
+parser = argparse.ArgumentParser(
+    description="Sample TensorFlow XML-to-TFRecord converter")
+parser.add_argument("-x",
+                    "--xml_dir",
+                    help="Path to the folder where the input .xml files are stored.",
+                    type=str)
+parser.add_argument("-l",
+                    "--labels_path",
+                    help="Path to the labels (.pbtxt) file.", type=str)
+parser.add_argument("-o",
+                    "--output_path",
+                    help="Path of output TFRecord (.record) file.", type=str)
+parser.add_argument("-i",
+                    "--image_dir",
+                    help="Path to the folder where the input image files are stored. "
+                         "Defaults to the same directory as XML_DIR.",
+                    type=str, default=None)
+parser.add_argument("-c",
+                    "--csv_path",
+                    help="Path of output .csv file. If none provided, then no file will be "
+                         "written.",
+                    type=str, default=None)
+
+args = parser.parse_args()
+
+if args.image_dir is None:
+    args.image_dir = args.xml_dir
+
+label_map = label_map_util.load_labelmap(args.labels_path)
+label_map_dict = label_map_util.get_label_map_dict(label_map)
+
+
+def xml_to_csv(path):
+    """Iterates through all .xml files (generated by labelImg) in a given directory and combines
+    them in a single Pandas dataframe.
+
+    Parameters:
+    ----------
+    path : str
+        The path containing the .xml files
+    Returns
+    -------
+    Pandas DataFrame
+        The produced dataframe
+    """
+
+    xml_list = []
+    for xml_file in glob.glob(path + '/*.xml'):
+        tree = ET.parse(xml_file)
+        root = tree.getroot()
+        for member in root.findall('object'):
+            value = (root.find('filename').text,
+                     int(root.find('size')[0].text),
+                     int(root.find('size')[1].text),
+                     member[0].text,
+                     int(member[4][0].text),
+                     int(member[4][1].text),
+                     int(member[4][2].text),
+                     int(member[4][3].text)
+                     )
+            xml_list.append(value)
+    column_name = ['filename', 'width', 'height',
+                   'class', 'xmin', 'ymin', 'xmax', 'ymax']
+    xml_df = pd.DataFrame(xml_list, columns=column_name)
+    return xml_df
+
+
+def class_text_to_int(row_label):
+    return label_map_dict[row_label]
+
+
+def split(df, group):
+    data = namedtuple('data', ['filename', 'object'])
+    gb = df.groupby(group)
+    return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
+
+
+def create_tf_example(group, path):
+    with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
+        encoded_jpg = fid.read()
+    encoded_jpg_io = io.BytesIO(encoded_jpg)
+    image = Image.open(encoded_jpg_io)
+    width, height = image.size
+
+    filename = group.filename.encode('utf8')
+    image_format = b'jpg'
+    xmins = []
+    xmaxs = []
+    ymins = []
+    ymaxs = []
+    classes_text = []
+    classes = []
+
+    for index, row in group.object.iterrows():
+        xmins.append(row['xmin'] / width)
+        xmaxs.append(row['xmax'] / width)
+        ymins.append(row['ymin'] / height)
+        ymaxs.append(row['ymax'] / height)
+        classes_text.append(row['class'].encode('utf8'))
+        classes.append(class_text_to_int(row['class']))
+
+    tf_example = tf.train.Example(features=tf.train.Features(feature={
+        'image/height': dataset_util.int64_feature(height),
+        'image/width': dataset_util.int64_feature(width),
+        'image/filename': dataset_util.bytes_feature(filename),
+        'image/source_id': dataset_util.bytes_feature(filename),
+        'image/encoded': dataset_util.bytes_feature(encoded_jpg),
+        'image/format': dataset_util.bytes_feature(image_format),
+        'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
+        'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
+        'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
+        'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
+        'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
+        'image/object/class/label': dataset_util.int64_list_feature(classes),
+    }))
+    return tf_example
+
+
+def main(_):
+
+    writer = tf.python_io.TFRecordWriter(args.output_path)
+    path = os.path.join(args.image_dir)
+    examples = xml_to_csv(args.xml_dir)
+    grouped = split(examples, 'filename')
+    for group in grouped:
+        tf_example = create_tf_example(group, path)
+        writer.write(tf_example.SerializeToString())
+    writer.close()
+    print('Successfully created the TFRecord file: {}'.format(args.output_path))
+    if args.csv_path is not None:
+        examples.to_csv(args.csv_path, index=None)
+        print('Successfully created the CSV file: {}'.format(args.csv_path))
+
+
+if __name__ == '__main__':
+    tf.app.run()

Tensorflow/workspace/annotations/label_map.pbtxt

@@ -0,0 +1,212 @@
+item { 
+	name:'1'
+	id:1
+}
+item { 
+	name:'2'
+	id:2
+}
+item { 
+	name:'4'
+	id:3
+}
+item { 
+	name:'7'
+	id:4
+}
+item { 
+	name:'9'
+	id:5
+}
+item { 
+	name:'11'
+	id:6
+}
+item { 
+	name:'12'
+	id:7
+}
+item { 
+	name:'13'
+	id:8
+}
+item { 
+	name:'14'
+	id:9
+}
+item { 
+	name:'16'
+	id:10
+}
+item { 
+	name:'17'
+	id:11
+}
+item { 
+	name:'18'
+	id:12
+}
+item { 
+	name:'19'
+	id:13
+}
+item { 
+	name:'21'
+	id:14
+}
+item { 
+	name:'22'
+	id:15
+}
+item { 
+	name:'23'
+	id:16
+}
+item { 
+	name:'26'
+	id:17
+}
+item { 
+	name:'27'
+	id:18
+}
+item { 
+	name:'28'
+	id:19
+}
+item { 
+	name:'29'
+	id:20
+}
+item { 
+	name:'30'
+	id:21
+}
+item { 
+	name:'31'
+	id:22
+}
+item { 
+	name:'32'
+	id:23
+}
+item { 
+	name:'33'
+	id:24
+}
+item { 
+	name:'34'
+	id:25
+}
+item { 
+	name:'35'
+	id:26
+}
+item { 
+	name:'36'
+	id:27
+}
+item { 
+	name:'37'
+	id:28
+}
+item { 
+	name:'38'
+	id:29
+}
+item { 
+	name:'39'
+	id:30
+}
+item { 
+	name:'40'
+	id:31
+}
+item { 
+	name:'41'
+	id:32
+}
+item { 
+	name:'42'
+	id:33
+}
+item { 
+	name:'43'
+	id:34
+}
+item { 
+	name:'45'
+	id:35
+}
+item { 
+	name:'46'
+	id:36
+}
+item { 
+	name:'47'
+	id:37
+}
+item { 
+	name:'49'
+	id:38
+}
+item { 
+	name:'50'
+	id:39
+}
+item { 
+	name:'51'
+	id:40
+}
+item { 
+	name:'52'
+	id:41
+}
+item { 
+	name:'53'
+	id:42
+}
+item { 
+	name:'54'
+	id:43
+}
+item { 
+	name:'55'
+	id:44
+}
+item { 
+	name:'57'
+	id:45
+}
+item { 
+	name:'58'
+	id:46
+}
+item { 
+	name:'60'
+	id:47
+}
+item { 
+	name:'61'
+	id:48
+}
+item { 
+	name:'62'
+	id:49
+}
+item { 
+	name:'63'
+	id:50
+}
+item { 
+	name:'64'
+	id:51
+}
+item { 
+	name:'66'
+	id:52
+}
+item { 
+	name:'67'
+	id:53
+}

Tensorflow/workspace/images/test/1 (2).xml

@@ -0,0 +1,50 @@
+<annotation>
+	<folder>BigDataset</folder>
+	<filename>1 (8).jpg</filename>
+	<path>F:\Thesis Files\BigDataset\1 (8).jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>159</width>
+		<height>110</height>
+		<depth>1</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>30</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>35</ymin>
+			<xmax>77</xmax>
+			<ymax>96</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>28</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>79</xmin>
+			<ymin>37</ymin>
+			<xmax>128</xmax>
+			<ymax>109</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>62</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>63</xmin>
+			<ymin>2</ymin>
+			<xmax>154</xmax>
+			<ymax>98</ymax>
+		</bndbox>
+	</object>
+</annotation>

Tensorflow/workspace/images/train/1 (1).xml

@@ -0,0 +1,38 @@
+<annotation>
+	<folder>Last</folder>
+	<filename>1 (1).jpg</filename>
+	<path>F:\Thesis Files\Last\1 (1).jpg</path>
+	<source>
+		<database>Unknown</database>
+	</source>
+	<size>
+		<width>159</width>
+		<height>68</height>
+		<depth>1</depth>
+	</size>
+	<segmented>0</segmented>
+	<object>
+		<name>31</name>
+		<pose>Unspecified</pose>
+		<truncated>1</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>1</xmin>
+			<ymin>3</ymin>
+			<xmax>70</xmax>
+			<ymax>68</ymax>
+		</bndbox>
+	</object>
+	<object>
+		<name>27</name>
+		<pose>Unspecified</pose>
+		<truncated>0</truncated>
+		<difficult>0</difficult>
+		<bndbox>
+			<xmin>72</xmin>
+			<ymin>3</ymin>
+			<xmax>153</xmax>
+			<ymax>64</ymax>
+		</bndbox>
+	</object>
+</annotation>

Tensorflow/workspace/models/my_ssd_mobnet/checkpoint

@@ -0,0 +1,16 @@
+model_checkpoint_path: "ckpt-51"
+all_model_checkpoint_paths: "ckpt-45"
+all_model_checkpoint_paths: "ckpt-46"
+all_model_checkpoint_paths: "ckpt-47"
+all_model_checkpoint_paths: "ckpt-48"
+all_model_checkpoint_paths: "ckpt-49"
+all_model_checkpoint_paths: "ckpt-50"
+all_model_checkpoint_paths: "ckpt-51"
+all_model_checkpoint_timestamps: 1647536347.1598632
+all_model_checkpoint_timestamps: 1647537278.138767
+all_model_checkpoint_timestamps: 1647538209.904142
+all_model_checkpoint_timestamps: 1647539145.8142953
+all_model_checkpoint_timestamps: 1647540079.101388
+all_model_checkpoint_timestamps: 1647541013.3102336
+all_model_checkpoint_timestamps: 1647541949.6900263
+last_preserved_timestamp: 1647465050.9765189

Tensorflow/workspace/models/my_ssd_mobnet/ckpt-51.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f522d8067633f24a4948fa5f8b93e421ac681ad1ff01a5a86b1b3130546fcd60
+size 21052821

Tensorflow/workspace/models/my_ssd_mobnet/pipeline.config

@@ -0,0 +1,191 @@
+model {
+  ssd {
+    num_classes: 53
+    image_resizer {
+      fixed_shape_resizer {
+        height: 640
+        width: 640
+      }
+    }
+    feature_extractor {
+      type: "ssd_mobilenet_v2_fpn_keras"
+      depth_multiplier: 1.0
+      min_depth: 16
+      conv_hyperparams {
+        regularizer {
+          l2_regularizer {
+            weight: 4e-05
+          }
+        }
+        initializer {
+          random_normal_initializer {
+            mean: 0.0
+            stddev: 0.01
+          }
+        }
+        activation: RELU_6
+        batch_norm {
+          decay: 0.997
+          scale: true
+          epsilon: 0.001
+        }
+      }
+      use_depthwise: true
+      override_base_feature_extractor_hyperparams: true
+      fpn {
+        min_level: 3
+        max_level: 7
+        additional_layer_depth: 128
+      }
+    }
+    box_coder {
+      faster_rcnn_box_coder {
+        y_scale: 10.0
+        x_scale: 10.0
+        height_scale: 5.0
+        width_scale: 5.0
+      }
+    }
+    matcher {
+      argmax_matcher {
+        matched_threshold: 0.5
+        unmatched_threshold: 0.5
+        ignore_thresholds: false
+        negatives_lower_than_unmatched: true
+        force_match_for_each_row: true
+        use_matmul_gather: true
+      }
+    }
+    similarity_calculator {
+      iou_similarity {
+      }
+    }
+    box_predictor {
+      weight_shared_convolutional_box_predictor {
+        conv_hyperparams {
+          regularizer {
+            l2_regularizer {
+              weight: 4e-05
+            }
+          }
+          initializer {
+            random_normal_initializer {
+              mean: 0.0
+              stddev: 0.01
+            }
+          }
+          activation: RELU_6
+          batch_norm {
+            decay: 0.997
+            scale: true
+            epsilon: 0.001
+          }
+        }
+        depth: 128
+        num_layers_before_predictor: 4
+        kernel_size: 3
+        class_prediction_bias_init: -4.6
+        share_prediction_tower: true
+        use_depthwise: true
+      }
+    }
+    anchor_generator {
+      multiscale_anchor_generator {
+        min_level: 3
+        max_level: 7
+        anchor_scale: 4.0
+        aspect_ratios: 1.0
+        aspect_ratios: 2.0
+        aspect_ratios: 0.5
+        scales_per_octave: 2
+      }
+    }
+    post_processing {
+      batch_non_max_suppression {
+        score_threshold: 1e-08
+        iou_threshold: 0.6
+        max_detections_per_class: 100
+        max_total_detections: 100
+        use_static_shapes: false
+      }
+      score_converter: SIGMOID
+    }
+    normalize_loss_by_num_matches: true
+    loss {
+      localization_loss {
+        weighted_smooth_l1 {
+        }
+      }
+      classification_loss {
+        weighted_sigmoid_focal {
+          gamma: 2.0
+          alpha: 0.25
+        }
+      }
+      classification_weight: 1.0
+      localization_weight: 1.0
+    }
+    encode_background_as_zeros: true
+    normalize_loc_loss_by_codesize: true
+    inplace_batchnorm_update: true
+    freeze_batchnorm: false
+  }
+}
+train_config {
+  batch_size: 8
+  data_augmentation_options {
+    random_horizontal_flip {
+    }
+  }
+  data_augmentation_options {
+    random_crop_image {
+      min_object_covered: 0.0
+      min_aspect_ratio: 0.75
+      max_aspect_ratio: 3.0
+      min_area: 0.75
+      max_area: 1.0
+      overlap_thresh: 0.0
+    }
+  }
+  sync_replicas: true
+  optimizer {
+    momentum_optimizer {
+      learning_rate {
+        cosine_decay_learning_rate {
+          learning_rate_base: 0.08
+          total_steps: 50000
+          warmup_learning_rate: 0.026666
+          warmup_steps: 1000
+        }
+      }
+      momentum_optimizer_value: 0.9
+    }
+    use_moving_average: false
+  }
+  fine_tune_checkpoint: "Tensorflow/workspace/pre-trained-models/ssd_mobilenet_v2_fpnlite_640x640_coco17_tpu-8/checkpoint/ckpt-0"
+  num_steps: 50000
+  startup_delay_steps: 0.0
+  replicas_to_aggregate: 8
+  max_number_of_boxes: 100
+  unpad_groundtruth_tensors: false
+  fine_tune_checkpoint_type: "detection"
+  fine_tune_checkpoint_version: V2
+}
+train_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/train.record"
+  }
+}
+eval_config {
+  metrics_set: "coco_detection_metrics"
+  use_moving_averages: false
+}
+eval_input_reader {
+  label_map_path: "Tensorflow/workspace/annotations/label_map.pbtxt"
+  shuffle: false
+  num_epochs: 1
+  tf_record_input_reader {
+    input_path: "Tensorflow/workspace/annotations/test.record"
+  }
+}

app.py

@@ -0,0 +1,543 @@
+from __future__ import division, print_function
+# coding=utf-8
+import sys
+import os
+import glob
+import re
+import numpy as np
+
+# Keras
+from tensorflow.keras.models import load_model
+#from tensorflow.keras.preprocessing import image
+
+# Flask utils
+from flask import Flask, redirect, url_for, request, render_template
+from werkzeug.utils import secure_filename
+# from gevent.pywsgi import WSGIServer
+
+
+
+
+
+#import everytnimg
+# from skimage.io import imread, imshow
+# from skimage.filters import gaussian, threshold_otsu
+# from skimage.feature import canny
+# from skimage.transform import probabilistic_hough_line, rotate
+# from process_image import process_image
+
+# import glob
+# import math
+
+import cv2 
+# import numpy as np
+# from PIL import Image
+# from matplotlib import pyplot as plt
+# from matplotlib.patches import Rectangle
+#%matplotlib inline 
+
+
+
+# from collections import OrderedDict
+# from PIL import Image
+
+# import pandas as pd
+# import seaborn as sns
+
+# import math
+
+
+#import all from Hough transfrom cell
+# from skimage.transform import hough_line, hough_line_peaks
+# from skimage.transform import rotate
+# from skimage.feature import canny
+# from skimage.io import imread
+# from skimage.color import rgb2gray
+# import matplotlib.pyplot as plt
+# from scipy.stats import mode as md
+# from myhough import deskew, deskew2
+
+# from segment_words import sortit,words,createk,hpf,bps,wps,baw
+
+# from myverify import verify
+#from detect_frame import detect_frame
+import pathlib
+
+
+#import more
+import tensorflow as tf
+from object_detection.utils import config_util
+# from object_detection.protos import pipeline_pb2
+# from google.protobuf import text_format
+
+# import os
+from object_detection.utils import label_map_util
+# from object_detection.utils import visualization_utils as viz_utils
+from object_detection.builders import model_builder
+
+# Load pipeline config and build a detection model
+WORKSPACE_PATH = 'Tensorflow/workspace'
+# SCRIPTS_PATH = 'Tensorflow/scripts'
+#APIMODEL_PATH = 'Tensorflow/models'
+ANNOTATION_PATH = WORKSPACE_PATH+'/annotations'
+# IMAGE_PATH = WORKSPACE_PATH+'/images'
+MODEL_PATH = WORKSPACE_PATH+'/models'
+PRETRAINED_MODEL_PATH = WORKSPACE_PATH+'/pre-trained-models'
+CONFIG_PATH = MODEL_PATH+'/my_ssd_mobnet/pipeline.config'
+CHECKPOINT_PATH = MODEL_PATH+'/my_ssd_mobnet/'
+# INPUT_IMAGE_PATH = 'Tensorflow/myimages'
+# MODEL_PATH = 'E:/RealTimeObjectDetection/model.best.hdf5'
+
+configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
+detection_model = model_builder.build(model_config=configs['model'], is_training=False)
+
+# Restore checkpoint
+ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
+ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-51')).expect_partial()
+
+@tf.function
+def detect_fn(image):
+    image, shapes = detection_model.preprocess(image)
+    prediction_dict = detection_model.predict(image, shapes)
+    detections = detection_model.postprocess(prediction_dict, shapes)
+    return detections
+
+def detect_frame(frame,isRealTime = False):
+    image_np = np.array(frame)
+    cpimg = frame.copy()
+    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
+    detections = detect_fn(input_tensor)
+    print(len(detections))
+    num_detections = int(detections.pop('num_detections'))
+    #print("hello")
+    #print(num_detections)
+    
+    #print(len(detections['detection_scores']))
+    detections = {key: value[0, :num_detections].numpy()
+                  for key, value in detections.items()}
+    detections['num_detections'] = num_detections
+    row,col,dummy = image_np.shape
+    # detection_classes should be ints.
+    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
+    #print(detections['detection_classes'])
+    mark = [0]*15
+    myletters = []
+    for i in range(0,15):
+        curi=detections['detection_classes'][i]
+        classi=classes[curi]
+        print(classes[curi],end='-')
+        cur=detections['detection_scores'][i]
+        if(cur<0.2):
+            continue
+        print(cur,end=' ')
+        print(detections['detection_boxes'][i], end=' ')
+        x0=(detections['detection_boxes'][i][0])
+        y0=(detections['detection_boxes'][i][1])
+        x1=(detections['detection_boxes'][i][2])
+        y1=(detections['detection_boxes'][i][3])
+        curarea=(x1-x0)*(y1-y0)
+        ok=1
+        for j in range(0,i):
+            #print(mark[j])
+            if mark[j]==0:
+                continue
+            curj=detections['detection_classes'][j]
+            classj=classes[curj]
+            
+            if classi=='ি' or classj=='ি':
+                if classi!=classj:
+                    continue
+            if classi=='ী' or classj=='ী':
+                if classi!=classj:
+                    continue
+            
+            x2=(detections['detection_boxes'][j][0])
+            y2=(detections['detection_boxes'][j][1])
+            x3=(detections['detection_boxes'][j][2])
+            y3=(detections['detection_boxes'][j][3])
+            x4=max(x0,x2)
+            y4=max(y0,y2)
+            x5=min(x1,x3)
+            y5=min(y1,y3)
+            if x4>x5 or y4>y5:
+                continue
+            prevarea=(x3-x2)*(y3-y2)
+            commonarea=(x5-x4)*(y5-y4)
+            ins1=curarea/commonarea
+            ins2=prevarea/commonarea
+            ins=commonarea/(curarea+prevarea-commonarea)  
+            print(ins1,end=' ')
+            if(ins>=0.5):
+                ok=0
+                cur=detections['detection_classes'][j]
+                print(classes[cur])
+                break
+        if ok==1:
+            mark[i]=1
+            cur=detections['detection_classes'][i]
+            #myletters.append(classes[cur])
+        print(ok)
+    #verification
+    for i in range(0,15):
+        if mark[i]==0 or avver==0:
+            continue
+        if detections['detection_classes'][i]>38:
+            continue
+        x0=int(detections['detection_boxes'][i][0]*row)
+        y0=int(detections['detection_boxes'][i][1]*col)
+        x1=int(detections['detection_boxes'][i][2]*row)
+        y1=int(detections['detection_boxes'][i][3]*col)
+        #print(y0,y1,x0,x1)
+        currImg = cpimg[x0:x1,y0:y1]
+        
+        curscore = detections['detection_scores'][i]
+        curclass = detections['detection_classes'][i]
+        label,conf = verify(currImg)
+        #print(ulta[label],conf)
+        #print(curclass,curscore)
+        if conf>curscore and ulta[label]!=curclass and ulta[label]!=-1:
+            detections['detection_classes'][i]=ulta[label]
+            detections['detection_scores'][i]=conf
+            
+    for i in range(0,15):
+        if(detections['detection_scores'][i]<0.2):
+            continue
+        if mark[i]==0:
+            continue
+        cur=detections['detection_classes'][i]
+        cur=classes[cur]
+        y0=(detections['detection_boxes'][i][1])
+        y1=(detections['detection_boxes'][i][3])
+        pair = (y0,cur,y1)
+        myletters.append(pair)
+    myletters.sort(key = lambda x: x[0])
+    #print(myletters)
+    for i in range(len(myletters)-1,-1,-1):
+        y0=myletters[i][0]
+        curr=myletters[i][1]
+        y1=myletters[i][2]
+        if curr=='ু' or curr=='্র':
+            mxarea=0
+            mxno=i-1
+            for j in range(0,len(myletters)):
+                if i==j:
+                    continue
+                y2=myletters[j][0]
+                y3=myletters[j][2]
+                curcommon = min(y3,y1)-max(y0,y2)
+                if curcommon>mxarea:
+                    mxarea = curcommon
+                    mxno=j
+            if mxno!=(i-1):
+                myletters[i],myletters[i+1]=myletters[i+1],myletters[i]
+    
+    res_list = [x[1] for x in myletters]
+    print(res_list)
+    
+        
+    for i in range(len(res_list)-2, -1, -1):
+        x=res_list[i]
+        y=res_list[i+1]
+        if x=='ে' or x=='ি':
+            res_list[i],res_list[i+1]=res_list[i+1],res_list[i]
+    for i in range(len(res_list)-2, -1, -1):
+        x=res_list[i]
+        y=res_list[i+1]
+        print(x,y)
+        if x=='অ' and y=='া':
+            print('yo')
+            res_list[i]='আ'
+            res_list.pop(i+1)
+    print(res_list)
+    for i in res_list:
+        print(i,end='')
+        
+    print(' ') 
+    return res_list
+
+
+
+
+# Define a flask app
+# app = Flask(__name__)
+
+# Model saved with Keras model.save()
+
+# Load your trained model
+# model = load_model(MODEL_PATH)
+#model._make_predict_function()          # Necessary
+# print('Model loaded. Start serving...')
+
+# You can also use pretrained model from Keras
+# Check https://keras.io/applications/
+#from keras.applications.resnet50 import ResNet50
+#model = ResNet50(weights='imagenet')
+#model.save('')
+# print('Model loaded. Check http://127.0.0.1:5000/')
+avver=0
+clicked=1
+wp = None; bp = None;
+
+category_index = label_map_util.create_category_index_from_labelmap(ANNOTATION_PATH+'/label_map.pbtxt')
+classes=['অ','ই','উ','এ','ও','ক','খ','গ','ঘ','চ','ছ','জ','ঝ','ট','ঠ','ড','ত','থ','দ','ধ','ন','প','ফ','ব','ভ','ম','য','র','ল','শ','ষ','স','হ','ড়','য়','ৎ','ং','ঁ','০','১','২','৩','৪','৫','৭','৮','া','ি','ী','ে','ু','্র','্য']
+labels=[1,2,4,7,9,11,12,13,14,16,17,18,19,21,22,23,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,45,46,47,49,50,51,52,53,54,55,57,58,60,61,62,63,64,66,67]
+ulta=[0,-1,1,-1,2,-1,-1,3,-1,4,-1,5,6,7,8,-1,9,10,11,12,-1,13,14,15,-1,-1,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,-1,34,35,36,-1,37,38,39,40,41,42,43,-1,44,45,-1,46,47,48,49,50,-1,51,52]
+
+
+def model_predict(word):
+    #img = cv2.imread(img_path,cv2.IMREAD_GRAYSCALE)
+    '''
+    if clicked==1:
+        bp = 66
+        wp = 160
+        mode = "GCMODE"
+        if mode == "GCMODE":
+            img= hpf(img,kSize = 51)
+            wp = 127
+            img = wps(img,wp)
+            img = bps(img)
+        elif mode == "RMODE":
+            bps()
+            wps()
+        elif mode == "SMODE":
+            bps()
+            wps()
+            baw()
+    img = cv2.fastNlMeansDenoising(img, img, 50.0, 7, 21) 
+    print("\ndone.")
+    xs=img.shape
+    if len(xs)==3:
+        img = img[:,:,0]
+
+    img = cv.adaptiveThreshold(img,255,cv.ADAPTIVE_THRESH_GAUSSIAN_C,cv.THRESH_BINARY,11,2)
+    angeel = deskew(img)
+    if angeel!=0:
+        img = deskew2(img,angeel)
+    ho,wo=img.shape
+    area=ho*wo
+    ara=words(img,25,11,7,area/5000)
+    ara.reverse()
+    #cv2.imshow('input image',img)
+    sz=len(ara)
+    for i in range(0,sz):
+        ara[i]=sorted(ara[i], key=lambda entry:entry[0][0])
+    cnt2=0
+    files = glob.glob('Tensorflow/myimages/*')
+    for f in files:
+        os.remove(f)
+    for i in range(0,sz):
+        #print(ara[i].shape)
+        tmp=ara[i]
+        sz2=len(tmp)
+        if i%10==0:
+            cnt2=cnt2+1
+        for j in range(0,sz2):
+            a,b=tmp[j]
+            b = cv2.adaptiveThreshold(b,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
+            if j<10:
+                cnt3=0
+            elif j<20:
+                cnt3=1
+            else:
+                cnt3=2
+            cv2.imwrite('Tensorflow/myimages/ocr %d%d%d%d.jpg' % (cnt2,i,cnt3,j), b)
+            #cv2.imshow('Crop %d%d' % (i,j), b)
+    cv2.waitKey(0)
+    
+    PATH_TO_TEST_IMAGES_DIR = pathlib.Path('Tensorflow/myimages')
+    TEST_IMAGE_PATHS = (list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpg"))+list(PATH_TO_TEST_IMAGES_DIR.glob("*.jpeg"))) #+list(PATH_TO_TEST_IMAGES_DIR.glob("*.png"))
+    print(len(TEST_IMAGE_PATHS))
+    final = []
+    for image_path in TEST_IMAGE_PATHS:
+        print("ovi")
+        print(image_path)
+        frame = cv2.imread(str(image_path))
+        x=str(image_path)
+        print(x[25])
+        # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        final.append((detect_frame(frame),x[25]))
+    '''
+    frame = cv2.fastNlMeansDenoising(word,word, 50.0, 7, 21) 
+    xs = frame.shape
+    if(len(xs)==3):
+        frame = frame[:,:,0]
+    frame= cv2.adaptiveThreshold(frame,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,cv2.THRESH_BINARY,11,2)
+    frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR)
+    # x=str(img_path)
+    #print(x[25])
+        # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    image_np = np.array(frame)
+    cpimg = frame.copy()
+    input_tensor = tf.convert_to_tensor(np.expand_dims(image_np, 0), dtype=tf.float32)
+    image_t, shapes = detection_model.preprocess(input_tensor)
+    prediction_dict = detection_model.predict(image_t, shapes)
+    detections = detection_model.postprocess(prediction_dict, shapes)
+    # print(len(detections))
+    num_detections = int(detections.pop('num_detections'))
+    #print("hello")
+    #print(num_detections)
+    
+    #print(len(detections['detection_scores']))
+    detections = {key: value[0, :num_detections].numpy()
+                  for key, value in detections.items()}
+    detections['num_detections'] = num_detections
+    row,col,dummy = image_np.shape
+    # detection_classes should be ints.
+    detections['detection_classes'] = detections['detection_classes'].astype(np.int64)
+    #print(detections['detection_classes'])
+    mark = [0]*15
+    myletters = []
+    for i in range(0,15):
+        curi=detections['detection_classes'][i]
+        classi=classes[curi]
+        # print(classes[curi],end='-')
+        cur=detections['detection_scores'][i]
+        if(cur<0.2):
+            continue
+        # print(cur,end=' ')
+        # print(detections['detection_boxes'][i], end=' ')
+        x0=(detections['detection_boxes'][i][0])
+        y0=(detections['detection_boxes'][i][1])
+        x1=(detections['detection_boxes'][i][2])
+        y1=(detections['detection_boxes'][i][3])
+        curarea=(x1-x0)*(y1-y0)
+        ok=1
+        for j in range(0,i):
+            #print(mark[j])
+            if mark[j]==0:
+                continue
+            curj=detections['detection_classes'][j]
+            classj=classes[curj]
+            
+            if classi=='ি' or classj=='ি':
+                if classi!=classj:
+                    continue
+            if classi=='ী' or classj=='ী':
+                if classi!=classj:
+                    continue
+            
+            x2=(detections['detection_boxes'][j][0])
+            y2=(detections['detection_boxes'][j][1])
+            x3=(detections['detection_boxes'][j][2])
+            y3=(detections['detection_boxes'][j][3])
+            x4=max(x0,x2)
+            y4=max(y0,y2)
+            x5=min(x1,x3)
+            y5=min(y1,y3)
+            if x4>x5 or y4>y5:
+                continue
+            prevarea=(x3-x2)*(y3-y2)
+            commonarea=(x5-x4)*(y5-y4)
+            ins1=curarea/commonarea
+            ins2=prevarea/commonarea
+            ins=commonarea/(curarea+prevarea-commonarea)  
+            # print(ins1,end=' ')
+            if(ins>=0.5):
+                ok=0
+                cur=detections['detection_classes'][j]
+                # print(classes[cur])
+                break
+        if ok==1:
+            mark[i]=1
+            cur=detections['detection_classes'][i]
+            #myletters.append(classes[cur])
+        # print(ok)
+    #verification
+    for i in range(0,15):
+        if mark[i]==0 or avver==0:
+            continue
+        if detections['detection_classes'][i]>38:
+            continue
+        x0=int(detections['detection_boxes'][i][0]*row)
+        y0=int(detections['detection_boxes'][i][1]*col)
+        x1=int(detections['detection_boxes'][i][2]*row)
+        y1=int(detections['detection_boxes'][i][3]*col)
+        #print(y0,y1,x0,x1)
+        currImg = cpimg[x0:x1,y0:y1]
+        
+        curscore = detections['detection_scores'][i]
+        curclass = detections['detection_classes'][i]
+        label,conf = verify(currImg)
+        #print(ulta[label],conf)
+        #print(curclass,curscore)
+        if conf>curscore and ulta[label]!=curclass and ulta[label]!=-1:
+            detections['detection_classes'][i]=ulta[label]
+            detections['detection_scores'][i]=conf
+            
+    for i in range(0,15):
+        if(detections['detection_scores'][i]<0.2):
+            continue
+        if mark[i]==0:
+            continue
+        cur=detections['detection_classes'][i]
+        cur=classes[cur]
+        y0=(detections['detection_boxes'][i][1])
+        y1=(detections['detection_boxes'][i][3])
+        pair = (y0,cur,y1)
+        myletters.append(pair)
+    myletters.sort(key = lambda x: x[0])
+    #print(myletters)
+    for i in range(len(myletters)-1,-1,-1):
+        y0=myletters[i][0]
+        curr=myletters[i][1]
+        y1=myletters[i][2]
+        if curr=='ু' or curr=='্র':
+            mxarea=0
+            mxno=i-1
+            for j in range(0,len(myletters)):
+                if i==j:
+                    continue
+                y2=myletters[j][0]
+                y3=myletters[j][2]
+                curcommon = min(y3,y1)-max(y0,y2)
+                if curcommon>mxarea:
+                    mxarea = curcommon
+                    mxno=j
+            if mxno!=(i-1):
+                myletters[i],myletters[i+1]=myletters[i+1],myletters[i]
+    
+    res_list = [x[1] for x in myletters]
+    # print(res_list)
+    
+        
+    for i in range(len(res_list)-2, -1, -1):
+        x=res_list[i]
+        y=res_list[i+1]
+        if x=='ে' or x=='ি':
+            res_list[i],res_list[i+1]=res_list[i+1],res_list[i]
+    for i in range(len(res_list)-2, -1, -1):
+        x=res_list[i]
+        y=res_list[i+1]
+        # print(x,y)
+        if x=='অ' and y=='া':
+            # print('yo')
+            res_list[i]='আ'
+            res_list.pop(i+1)
+    # print(res_list)
+    output=''
+    for i in res_list:
+        output=output+i
+        
+    # print(' ') 
+    return output
+    '''
+    output=''
+    for i in range(0,len(final)):
+        ara=final[i][0]
+        numb=final[i][1]
+        if i>0 and numb!=final[i-1][1]:
+            output= output+'\n'
+        word = ''.join(ara)
+        #corrected_word = get_campaign(word)
+        output= output + word
+        #print(corrected_word,end='')
+        output = output + ' '
+    return output
+    '''
+
+import gradio as gr
+
+
+demo = gr.Interface(fn=model_predict, inputs= "paint", outputs="text")
+
+demo.launch()