Patrice Ferlet metal3d

## train.py
""" Train model and find bad image from a directory """
import argparse
import os
import time
from glob import glob
from typing import Any

import numpy as np
from tensorflow.python.keras.layers.pooling import MaxPool2D

## Info
Based on https://gist.github.com/tadast/8827699:

- remove duplicates
- striped and no unecessary double quotes
- floating point values instead of strings

## make editable vars in html.html
<html>
    <meta charset="utf-8" />
    <body>
        <pre>
        <code>
            # All you have to do is to prefix variables with "_change_"
            # and let the script to manage the editable values
            FOO="_change_thisvariable"

            this is a command with _change_argument here

## sun_set.py
import bpy
import mathutils

# Author: Patrice Ferlet <metal3d@gmail.com>
# License: GPL

# Works with  Blender 2.80-2.90
# To use this script:
# - paste this script in a "text editor" *inside* blender
# - name it "sun_set.py"

## videoframegenerator.py
# DEPRECATED - please  go to my complete package:
# pipy package https://pypi.org/project/keras-video-generators/
# github sources https://github.com/metal3d/keras-video-generators

import keras
import cv2 as cv
import glob
import numpy as np
import os
import random

## videoframegenerator.py
import keras
import cv2 as cv
import glob
import numpy as np
import os
import random
import keras_preprocessing

# author: Patrice Ferlet <patrice.ferlet@smile.fr>
# licence: MIT

## TF - graph exemple.ipynb

      
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                metal3d
                / TF - graph exemple.ipynb
            
            
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              January 20, 2019 16:37
            
              
                Tensorflow basics
              
          
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## tfboardviewer.py
import tensorflow as tf
import numpy as np
from IPython.display import clear_output, Image, display, HTML

def strip_consts(graph_def, max_const_size=32):
    """Strip large constant values from graph_def."""
    strip_def = tf.GraphDef()
    for n0 in graph_def.node:
        n = strip_def.node.add()
        n.MergeFrom(n0)

## generator.py
import cv2 as cv
import os
import glob
import numpy as np
import random
from tensorflow import keras
import keras.preprocessing.image
from tensorflow.python.keras.utils.data_utils import Sequence

class VideoFrameGenerator(Sequence):

## make_prediction.py
from IPython.display import HTML, display
import cv2 as cv
import os
import matplotlib.pyplot as plt

def make_prediction(model, movie, nframe, classes, shape=(224, 224), nchan=3):
    video = cv.VideoCapture(movie)
    frames = []
    while True:
        grabbed, frame = video.read()
	""" Train model and find bad image from a directory """
	import argparse
	import os
	import time
	from glob import glob
	from typing import Any

	import numpy as np
	from tensorflow.python.keras.layers.pooling import MaxPool2D
	Based on https://gist.github.com/tadast/8827699:

	- remove duplicates
	- striped and no unecessary double quotes
	- floating point values instead of strings
	<html>
	<meta charset="utf-8" />
	<body>
	<pre>
	<code>
	# All you have to do is to prefix variables with "_change_"
	# and let the script to manage the editable values
	FOO="_change_thisvariable"

	this is a command with _change_argument here
	import bpy
	import mathutils

	# Author: Patrice Ferlet <metal3d@gmail.com>
	# License: GPL

	# Works with Blender 2.80-2.90
	# To use this script:
	# - paste this script in a "text editor" inside blender
	# - name it "sun_set.py"
	# DEPRECATED - please go to my complete package:
	# pipy package https://pypi.org/project/keras-video-generators/
	# github sources https://github.com/metal3d/keras-video-generators

	import keras
	import cv2 as cv
	import glob
	import numpy as np
	import os
	import random
	import tensorflow as tf
	import numpy as np
	from IPython.display import clear_output, Image, display, HTML

	def strip_consts(graph_def, max_const_size=32):
	"""Strip large constant values from graph_def."""
	strip_def = tf.GraphDef()
	for n0 in graph_def.node:
	n = strip_def.node.add()
	n.MergeFrom(n0)
	from IPython.display import HTML, display
	import cv2 as cv
	import os
	import matplotlib.pyplot as plt

	def make_prediction(model, movie, nframe, classes, shape=(224, 224), nchan=3):
	video = cv.VideoCapture(movie)
	frames = []
	while True:
	grabbed, frame = video.read()