salman-ghauri

from keras import applications
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential, Model 
from keras.layers import Dropout, Flatten, Dense, GlobalAveragePooling2D
from keras import backend as k 
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard, EarlyStopping

img_width, img_height = 256, 256

salman-ghauri

import tensorflow as tf

# Assuming object detection API is available for use
from object_detection.utils.config_util import create_pipeline_proto_from_configs
from object_detection.utils.config_util import get_configs_from_pipeline_file
import object_detection.tf_server_export_inference_graph

# Configuration for model to be exported, the pipeline_config_path
config_pathname = "custom_detection/configs/101resnet_frcnn_coco.config"

salman-ghauri

"""Functions to export object detection inference graph."""
import logging
import os
import tempfile
import tensorflow as tf
from tensorflow.core.protobuf import rewriter_config_pb2
from tensorflow.python import pywrap_tensorflow
from tensorflow.python.client import session
from tensorflow.python.framework import graph_util
from tensorflow.python.platform import gfile

salman-ghauri

"""A simple GRPC client to communicate with tf-server on the given port. 
It has been developed by some help from another GIST which I forgot to reference."""
import time
from argparse import ArgumentParser
from grpc.beta import implementations
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from tensorflow.contrib.util import make_tensor_proto
import grpc
import scipy

salman-ghauri

model_config_list: {

  config: {
    name: "resnet_101",
    base_path: "/models/res2",
    model_platform: "tensorflow",
    model_version_policy: {
      specific: {
        versions: 1
      }

salman-ghauri

s = class_descriptions[class_descriptions['class']\
                       .isin(classes)]\
                       .set_index('name').T.to_dict()
train_df = pd.DataFrame(columns=['FileName', 'XMin', 'XMax',
                                 'YMin', 'YMax', 
                                 'ClassName'])

# Find boxes in each image and put them in a dataframe
train_imgs = os.listdir(train_path)
train_imgs = [name[0:16] for name in train_imgs\

salman-ghauri

"""
Use this code to download High resolution Birds Eye view which is similar to Google map's 45° imagery. There is no straight
forward way to download either of these.
"""
import urllib
import json, sys
from PIL import Image
from io import BytesIO

latlon = sys.argv[1]

salman-ghauri

License Key PhpStorm 8 
 
User Name : EMBRACE
===== LICENSE BEGIN =====
43136-12042010
00002UsvSON704l"dILe1PVx3y4"B3
49AU6oSDJrsjE8nMOQh"8HTDJHIUUh
gd1BebYc5U"6OxDbVsALB4Eb10PW8"
===== LICENSE END =====