Tony607

######## WebCam Object Detection Using Tensorflow Classifier #########
# Description: 
# This program uses a TensorFlow classifier to perform object detection.
# It loads the classifier uses it to perform object detection on a WebCam feed.
# It draws boxes and scores around the objects of interest in each frame from
# the WebCam. It also can be used with picamera by adding "--picamera"
# when executing this script from the terminal.

# Import packages
import os

Tony607

import numpy as np
import tensorflow as tf
from tensorflow_model_optimization.sparsity import keras as sparsity

# Backend agnostic way to save/restore models
# _, keras_file = tempfile.mkstemp('.h5')
# print('Saving model to: ', keras_file)
# tf.keras.models.save_model(model, keras_file, include_optimizer=False)

# Load the serialized model

Tony607

import gizeh as gz
from matplotlib import pyplot as plt
from PIL import Image
%matplotlib inline

from drawing_dataset import DrawingDataset
dataset = DrawingDataset('./data/quick_draw_pickles/', './data/label_mapping.jsonl')
dataset.setup()

strokes = dataset.get_drawing('cat', 0)

Tony607

from keras import backend as K
import tensorflow as tf

def freeze_session(session, keep_var_names=None, output_names=None, clear_devices=True):
    """
    Freezes the state of a session into a pruned computation graph.

    Creates a new computation graph where variable nodes are replaced by
    constants taking their current value in the session. The new graph will be
    pruned so subgraphs that are not necessary to compute the requested

Tony607

import numpy as np
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras import backend as K


def cosine_decay_with_warmup(global_step,
                             learning_rate_base,
                             total_steps,

Tony607

def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5):
    """
    Applies Non-max suppression (NMS) to set of boxes
    
    Arguments:
    scores -- tensor of shape (None,), output of yolo_filter_boxes()
    boxes -- tensor of shape (None, 4), output of yolo_filter_boxes() that have been scaled to the image size (see later)
    classes -- tensor of shape (None,), output of yolo_filter_boxes()
    max_boxes -- integer, maximum number of predicted boxes you'd like
    iou_threshold -- real value, "intersection over union" threshold used for NMS filtering

Tony607

import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.keras.models import load_model


# Clear any previous session.
tf.keras.backend.clear_session()

save_pb_dir = './model'
model_fname = './model/model.h5'

Tony607

def get_mfccs(audio_file=None ,signals=None, sample_rate = 44100, num_mfccs = 13, frame_length=1024, frame_step=512, fft_length=1024, fmax=8000, fmin=80):
    """Compute the MFCCs for audio file
    
    Keyword Arguments:
        audio_file {str} -- audio wav file path (default: {None})
        signals {tensor} -- input signals as tensor or np.array in float32 type (default: {None})
        sample_rate {int} -- sampling rate (default: {44100})
        num_mfccs {int} -- number of mfccs to keep (default: {13})
        frame_length {int} -- frame length to compute STFT (default: {1024})
        frame_step {int} -- frame step to compute STFT (default: {512})

Tony607

import tensorflow as tf

def get_frozen_graph(graph_file):
    """Read Frozen Graph file from disk."""
    with tf.gfile.FastGFile(graph_file, "rb") as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
    return graph_def

# The TensorRT inference graph file downloaded from Colab or your local machine.

Tony607