zakirangwala

# Real Time Prediction -> Video Capture
def real_time_prediction():
    category_index = label_map_util.create_category_index_from_labelmap(
        ANNOTATION_PATH+'/label_map.pbtxt')
    cap = cv2.VideoCapture(0)
    width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # Make detection
    while True:

zakirangwala

def check(image):
    image_np = cv2.imread(image)
    input_tensor = tf.convert_to_tensor(
        np.expand_dims(image_np, 0), dtype=tf.float32)

    detections = detect_fn(input_tensor)
    category_index = label_map_util.create_category_index_from_labelmap(
        ANNOTATION_PATH+'/label_map.pbtxt')

    num_detections = int(detections.pop('num_detections'))

zakirangwala

def detect_fn(image):
    detection_model = load_model()
    image, shapes = detection_model.preprocess(image)
    prediction_dict = detection_model.predict(image, shapes)
    detections = detection_model.postprocess(prediction_dict, shapes)
    return detections

zakirangwala

# Load Model from checkpoints
def load_model():
    configs = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
    detection_model = model_builder.build(
        model_config=configs['model'], is_training=False)
    ckpt = tf.compat.v2.train.Checkpoint(model=detection_model)
    ckpt.restore(os.path.join(CHECKPOINT_PATH, 'ckpt-9')).expect_partial()
    return detection_model

zakirangwala

# Configuration
def config():
    CONFIG_PATH = MODEL_PATH+'/'+CUSTOM_MODEL_NAME+'/pipeline.config'
    config = config_util.get_configs_from_pipeline_file(CONFIG_PATH)
    pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
    with tf.io.gfile.GFile(CONFIG_PATH, "r") as f:
        proto_str = f.read()
        text_format.Merge(proto_str, pipeline_config)
    # Modify pipeline
    pipeline_config.model.ssd.num_classes = 2

zakirangwala

# XML TO CSV
def xml_to_csv(path):
    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+".jpg",
                     int(root.find('size')[0].text),
                     int(root.find('size')[1].text),

zakirangwala

# Label Map
def construct_label_map():
    labels = [{'name': 'Mask', 'id': 1}, {'name': 'NoMask', 'id': 2}]

    with open(ANNOTATION_PATH + '\label_map.pbtxt', 'w') as f:
        for label in labels:
            f.write('item { \n')
            f.write('\tname:\'{}\'\n'.format(label['name']))
            f.write('\tid:{}\n'.format(label['id']))
            f.write('}\n')

zakirangwala

# Setup Paths
WORKSPACE_PATH = 'Tensorflow/workspace'
SCRIPTS_PATH = 'Tensorflow/scripts'
APIMODEL_PATH = 'Tensorflow/tensorflow-models/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/'

zakirangwala

# Import Libraries
import os
import cv2
import glob
import pandas as pd
import xml.etree.ElementTree as ET
import numpy as np
import tensorflow as tf
from object_detection.utils import config_util
from object_detection.protos import pipeline_pb2

zakirangwala

if __name__ == "__main__":
  greet()
  city, country, latitude, longitude = get_location()
  while True:
    query = listen().lower()
    if 'stock' in query:
      pass
    elif 'weather' in query or 'temperature' in query:
      pass
    elif 'movie' in query or 'documentary' in query: