pra-dan

//Tencent is pleased to support the open source community by making FeatherCNN available.

//Copyright (C) 2018 THL A29 Limited, a Tencent company. All rights reserved.

//Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
//in compliance with the License. You may obtain a copy of the License at
//
//https://opensource.org/licenses/BSD-3-Clause
//
//Unless required by applicable law or agreed to in writing, software distributed

pra-dan

# Obtain frozen graph of mobilenet v1 
!wget "http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz"
!tar -xf mobilenet_v1_1.0_224.tgz

# Get TensorFlow source code repo
!git clone "https://github.com/tensorflow/tensorflow.git"
# Optimize graph using TensorFlow tool
!python "/content/tensorflow/tensorflow/python/tools/optimize_for_inference.py"  --input=mobilenet_v1_1.0_224_frozen.pb  --output=mobilenet_v1_1.0_224_final.pb --input_names=input  --output_names="MobilenetV1/Predictions/Softmax"

pra-dan

# This script will help print the inputs and output layer names of Tensorflow graph files (.pb)

import tensorflow as tf
gf = tf.GraphDef()   
m_file = open('/content/frozen_darknet_yolov3_model.pb','rb')
gf.ParseFromString(m_file.read())

with open('somefile.txt', 'a') as the_file:
    for n in gf.node:
        the_file.write(n.name+'\n')

pra-dan

!pip install tensorflow-gpu==1.15.0

# Convert
!toco --graph_def_file /content/yolo-v2-tiny-coco.pb \
    --output_file yolo-v2-tiny-coco.tflite \
    --output_format TFLITE \
    --inference_type FLOAT \
    --inference_input_type FLOAT \
    --input_arrays input \
    --output_arrays output

pra-dan

# Install TensorFlow 1.0
!pip install tensorflow==1.0

# Install dependencies of the DarkFlow
!apt-get update
!pip3 install numpy
!apt-get install python-opencv -y
!pip install cython

# Clean up the directory

pra-dan

# Install TensorFlow 1.13.1
!pip install tensorflow==1.13.1

# Get the repo
!git clone "https://github.com/jinyu121/DW2TF.git"
%cd DW2TF
!python3 main.py \
    --cfg "PATH_to_config/yolo-v2-tiny-coco.cfg" \
    --weights "PATH_to_weights/yolo-v2-tiny-coco.weights" \
    --output "content/" \

pra-dan

# Note: The author has kept the weights and cfg files strictly in their respective directories.
# If you add yours, do place them in the same folders

!pip install onnx==1.6.0 onnx-tf==1.5.0
!pip install tensorflow-gpu==1.15.0

%cd repo
!wget "https://pjreddie.com/media/files/yolov3-tiny.weights" -O "/weights/" 
# The cfg file is already present in the repo.

pra-dan

# Install TensorFlow
!pip install tensorflow==1.11.0

# Get repository
!git clone https://github.com/mystic123/tensorflow-yolo-v3.git
%cd tensorflow-yolo-v3
!git checkout ed60b90

# Get coco.names
!wget https://raw.githubusercontent.com/pjreddie/darknet/master/data/coco.names

pra-dan

1. Remove all entries with empty strings OR empty cells

for col in rawData.columns: # Iterate over all cols
  #print(f'col: {col}')  
  
  # Replace empty strings (if any), with NaN
  df[col].replace('', np.nan, inplace=True)
  
  # Remove all rows with any entry = NaN
 df.dropna(subset=[col], inplace=True)

pra-dan

import bpy
import os
from math import *
from mathutils import *

#set your own target here
target = bpy.data.objects['Cube']
cam = bpy.data.objects['Camera']
t_loc_x = target.location.x
t_loc_y = target.location.y