techzizou

import glob, os

# Current directory
current_dir = os.path.dirname(os.path.abspath(__file__))

print(current_dir)

current_dir = 'data/obj'

# Percentage of images to be used for the test set

techzizou

classes = 2
train = data/train.txt
valid = data/test.txt
names = data/obj.names
backup = /mydrive/yolov4/training

techzizou

# RUNNING INFERENCE
import numpy as np
import os
import cv2
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from google.colab.patches import cv2_imshow

techzizou

from __future__ import division
from __future__ import print_function
from __future__ import absolute_import

import os
import io
import pandas as pd
import tensorflow as tf
import argparse

techzizou

#Loading the saved_model
import tensorflow as tf
import time
import numpy as np
import warnings
warnings.filterwarnings('ignore')
from PIL import Image
from google.colab.patches import cv2_imshow
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as viz_utils

techzizou

from tflite_support.metadata_writers import object_detector
from tflite_support.metadata_writers import writer_utils
from tflite_support import metadata
import flatbuffers
import os
from tensorflow_lite_support.metadata import metadata_schema_py_generated as _metadata_fb
from tensorflow_lite_support.metadata.python import metadata as _metadata
from tensorflow_lite_support.metadata.python.metadata_writers import metadata_info
from tensorflow_lite_support.metadata.python.metadata_writers import metadata_writer
from tensorflow_lite_support.metadata.python.metadata_writers import writer_utils

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%cd /mydrive/customTF2/data/

'''**********************************
   FOR FLOATING-POINT INFERENCE
**********************************'''

import tensorflow as tf

saved_model_dir = '/mydrive/customTF2/data/tflite/saved_model'