Update GTSRB

f08fcc4c · Jean-Luc Parouty Jean-Luc.Parouty@simap.grenoble-inp.fr · 72704d4b · f08fcc4c · f08fcc4c · f08fcc4c
Commit f08fcc4c authored 5 years ago by Jean-Luc Parouty Jean-Luc.Parouty@simap.grenoble-inp.fr
--- a/GTSRB/01-Preparation-of-data.ipynb
+++ b/GTSRB/01-Preparation-of-data.ipynb
@@ -7,13 +7,14 @@
    "German Traffic Sign Recognition Benchmark (GTSRB)\n",
    "=================================================\n",
    "---\n",
-    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020\n",
+    "Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020  \n",
+    "Version: 1.12\n",
    "\n",
    "## Episode 1 : Preparation of data\n",
    "\n",
    " - Understanding the dataset\n",
    " - Preparing and formatting enhanced data\n",
-    " - Save enhanced datasets\n"
+    " - Save enhanced datasets in h5 file format\n"
   ]
  },
  {
@@ -43,7 +44,7 @@
    "\n",
    "import idle.pwk as ooo\n",
    "from importlib import reload\n",
-    "reload(ooo)\n",
+    "\n",
    "ooo.init()"
   ]
  },
@@ -109,7 +110,8 @@
    " - **x_test, y_test** : Validation or test data\n",
    " - x_meta, y_meta : Illustration data\n",
    " \n",
-    "The learning data will be randomly mixted and the illustration data sorted."
+    "The learning data will be randomly mixted and the illustration data sorted.  \n",
+    "Will take about 2-3'"
   ]
  },
  {
@@ -440,7 +442,8 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
-    "Create enhanced datasets, and save them..."
+    "Create enhanced datasets, and save them...  \n",
+    "Will take about 7-8'"
   ]
  },
  {

 %% Cell type:markdown id: tags:
 German Traffic Sign Recognition Benchmark (GTSRB)
 =================================================
 ---
 Introduction au Deep Learning  (IDLE) - S. Arias, E. Maldonado, JL. Parouty - CNRS/SARI/DEVLOG - 2020
+Version: 1.12
 ## Episode 1 : Preparation of data
 - Understanding the dataset
 - Preparing and formatting enhanced data
- - Save enhanced datasets
+ - Save enhanced datasets in h5 file format
 %% Cell type:markdown id: tags:
 ## 1/ Import and init
 %% Cell type:code id: tags:
 ``` python
 import os, time, sys
 import csv
 import math, random
 import numpy as np
 import matplotlib.pyplot as plt
 import h5py
 from skimage.morphology import disk
 from skimage.filters import rank
 from skimage import io, color, exposure, transform
 import idle.pwk as ooo
 from importlib import reload
-reload(ooo)
 ooo.init()
 ```
 %% Cell type:markdown id: tags:
 ## 2/ Read the dataset
 Description is available there : http://benchmark.ini.rub.de/?section=gtsrb&subsection=dataset
 - Each directory contains one CSV file with annotations ("GT-<ClassID>.csv") and the training images
 - First line is fieldnames: Filename;Width;Height;Roi.X1;Roi.Y1;Roi.X2;Roi.Y2;ClassId
 ### 2.1/ Usefull functions
 %% Cell type:code id: tags:
 ``` python
 def read_dataset_dir(csv_filename):
    '''Reads traffic sign data from German Traffic Sign Recognition Benchmark dataset.
    Arguments:  csv filename
                Example /data/GTSRB/Train.csv
    Returns:   np array of images, np array of corresponding labels'''
    # ---- csv filename and path
    #
    name=os.path.basename(csv_filename)
    path=os.path.dirname(csv_filename)
    # ---- Read csv file
    #
    f,x,y = [],[],[]
    with open(csv_filename) as csv_file:
        reader = csv.DictReader(csv_file, delimiter=',')
        for row in reader:
            f.append( path+'/'+row['Path'] )
            y.append( int(row['ClassId'])  )
        csv_file.close()
    nb_images = len(f)
    # ---- Read images
    #
    for filename in f:
        image=io.imread(filename)
        x.append(image)
        ooo.update_progress(name,len(x),nb_images)
    # ---- Return
    #
    return np.array(x),np.array(y)
 ```
 %% Cell type:markdown id: tags:
 ### 2.2/ Read the data
 We will read the following datasets:
 - **x_train, y_train** : Learning data
 - **x_test, y_test** : Validation or test data
 - x_meta, y_meta : Illustration data
 The learning data will be randomly mixted and the illustration data sorted.
+Will take about 2-3'
 %% Cell type:code id: tags:
 ``` python
 %%time
 # ---- Read datasets
 (x_train,y_train) = read_dataset_dir('./data/origine/Train.csv')
 (x_test ,y_test)  = read_dataset_dir('./data/origine/Test.csv')
 (x_meta ,y_meta)  = read_dataset_dir('./data/origine/Meta.csv')
 # ---- Shuffle train set
 combined = list(zip(x_train,y_train))
 random.shuffle(combined)
 x_train,y_train = zip(*combined)
 # ---- Sort Meta
 combined = list(zip(x_meta,y_meta))
 combined.sort(key=lambda x: x[1])
 x_meta,y_meta = zip(*combined)
 ```
 %% Cell type:markdown id: tags:
 ## 3/ Few statistics about train dataset
 We want to know if our images are homogeneous in terms of size, ratio, width or height.
 ### 3.1/ Do statistics
 %% Cell type:code id: tags:
 ``` python
 train_size  = []
 train_ratio = []
 train_lx    = []
 train_ly    = []
 test_size   = []
 test_ratio  = []
 test_lx     = []
 test_ly     = []
 for image in x_train:
    (lx,ly,lz) = image.shape
    train_size.append(lx*ly/1024)
    train_ratio.append(lx/ly)
    train_lx.append(lx)
    train_ly.append(ly)
 for image in x_test:
    (lx,ly,lz) = image.shape
    test_size.append(lx*ly/1024)
    test_ratio.append(lx/ly)
    test_lx.append(lx)
    test_ly.append(ly)
 ```
 %% Cell type:markdown id: tags:
 ### 3.2/ Show statistics
 %% Cell type:code id: tags:
 ``` python
 # ------ Global stuff
 print("x_train size : ",len(x_train))
 print("y_train size : ",len(y_train))
 print("x_test size  : ",len(x_test))
 print("y_test size  : ",len(y_test))
 # ------ Statistics / sizes
 plt.figure(figsize=(16,6))
 plt.hist([train_size,test_size], bins=100)
 plt.gca().set(title='Sizes in Kpixels - Train=[{:5.2f}, {:5.2f}]'.format(min(train_size),max(train_size)),
              ylabel='Population',
              xlim=[0,30])
 plt.legend(['Train','Test'])
 plt.show()
 # ------ Statistics / ratio lx/ly
 plt.figure(figsize=(16,6))
 plt.hist([train_ratio,test_ratio], bins=100)
 plt.gca().set(title='Ratio lx/ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ratio),max(train_ratio)),
              ylabel='Population',
              xlim=[0.8,1.2])
 plt.legend(['Train','Test'])
 plt.show()
 # ------ Statistics / lx
 plt.figure(figsize=(16,6))
 plt.hist([train_lx,test_lx], bins=100)
 plt.gca().set(title='Images lx - Train=[{:5.2f}, {:5.2f}]'.format(min(train_lx),max(train_lx)),
              ylabel='Population',
              xlim=[20,150])
 plt.legend(['Train','Test'])
 plt.show()
 # ------ Statistics / ly
 plt.figure(figsize=(16,6))
 plt.hist([train_ly,test_ly], bins=100)
 plt.gca().set(title='Images ly - Train=[{:5.2f}, {:5.2f}]'.format(min(train_ly),max(train_ly)),
              ylabel='Population',
              xlim=[20,150])
 plt.legend(['Train','Test'])
 plt.show()
 # ------ Statistics / classId
 plt.figure(figsize=(16,6))
 plt.hist([y_train,y_test], bins=43)
 plt.gca().set(title='ClassesId',
              ylabel='Population',
              xlim=[0,43])
 plt.legend(['Train','Test'])
 plt.show()
 ```
 %% Cell type:markdown id: tags:
 ## 4/ List of classes
 What are the 43 classes of our images...
 %% Cell type:code id: tags:
 ``` python
 ooo.plot_images(x_meta,y_meta, range(43), columns=8, x_size=2, y_size=2,
                                colorbar=False, y_pred=None, cm='binary')
 ```
 %% Cell type:markdown id: tags:
 ## 5/ What does it really look like
 %% Cell type:code id: tags:
 ``` python
 # ---- Get and show few images
 samples = [ random.randint(0,len(x_train)-1) for i in range(32)]
 ooo.plot_images(x_train,y_train, samples, columns=8, x_size=2, y_size=2, colorbar=False, y_pred=None, cm='binary')
 ```
 %% Cell type:markdown id: tags:
 ## 6/ dataset cooking...
 Images must have the **same size** to match the size of the network.
 It is possible to work on **rgb** or **monochrome** images and **equalize** the histograms.
 The data must be **normalized**.
 See : [Exposure with scikit-image](https://scikit-image.org/docs/dev/api/skimage.exposure.html)
 See : [Local histogram equalization](https://scikit-image.org/docs/dev/api/skimage.filters.rank.html#skimage.filters.rank.equalize)
 See : [Histogram equalization](https://scikit-image.org/docs/dev/api/skimage.exposure.html#skimage.exposure.equalize_hist)
 ### 6.1/ Enhancement cook
 %% Cell type:code id: tags:
 ``` python
 def images_enhancement(images, width=25, height=25, mode='RGB'):
    '''
    Resize and convert images - doesn't change originals.
    input images must be RGBA or RGB.
    args:
        images :         images list
        width,height :   new images size (25,25)
        mode :           RGB | RGB-HE | L | L-HE | L-LHE | L-CLAHE
    return:
        numpy array of enhanced images
    '''
    modes = { 'RGB':3, 'RGB-HE':3, 'L':1, 'L-HE':1, 'L-LHE':1, 'L-CLAHE':1}
    lz=modes[mode]
    out=[]
    for img in images:
        # ---- if RGBA, convert to RGB
        if img.shape[2]==4:
            img=color.rgba2rgb(img)
        # ---- Resize
        img = transform.resize(img, (width,height))
        # ---- RGB / Histogram Equalization
        if mode=='RGB-HE':
            hsv = color.rgb2hsv(img.reshape(width,height,3))
            hsv[:, :, 2] = exposure.equalize_hist(hsv[:, :, 2])
            img = color.hsv2rgb(hsv)
        # ---- Grayscale
        if mode=='L':
            img=color.rgb2gray(img)
        # ---- Grayscale / Histogram Equalization
        if mode=='L-HE':
            img=color.rgb2gray(img)
            img=exposure.equalize_hist(img)
        # ---- Grayscale / Local Histogram Equalization
        if mode=='L-LHE':
            img=color.rgb2gray(img)
            img=rank.equalize(img, disk(10))/255.
        # ---- Grayscale / Contrast Limited Adaptive Histogram Equalization (CLAHE)
        if mode=='L-CLAHE':
            img=color.rgb2gray(img)
            img=exposure.equalize_adapthist(img)
        # ---- Add image in list of list
        out.append(img)
        ooo.update_progress('Enhancement: ',len(out),len(images))
    # ---- Reshape images
    #     (-1, width,height,1) for L
    #     (-1, width,height,3) for RGB
    #
    out = np.array(out,dtype='float64')
    out = out.reshape(-1,width,height,lz)
    return out
 ```
 %% Cell type:markdown id: tags:
 ### 6.2/ To get an idea of the different recipes
 %% Cell type:code id: tags:
 ``` python
 i=random.randint(0,len(x_train)-16)
 x_samples = x_train[i:i+16]
 y_samples = y_train[i:i+16]
 datasets  = {}
 datasets['RGB']      = images_enhancement( x_samples, width=25, height=25, mode='RGB'  )
 datasets['RGB-HE']   = images_enhancement( x_samples, width=25, height=25, mode='RGB-HE'  )
 datasets['L']        = images_enhancement( x_samples, width=25, height=25, mode='L'  )
 datasets['L-HE']     = images_enhancement( x_samples, width=25, height=25, mode='L-HE'  )
 datasets['L-LHE']    = images_enhancement( x_samples, width=25, height=25, mode='L-LHE'  )
 datasets['L-CLAHE']  = images_enhancement( x_samples, width=25, height=25, mode='L-CLAHE'  )
 print('\nEXPECTED (Meta) :\n')
 x_expected=[ x_meta[i] for i in y_samples]
 ooo.plot_images(x_expected, y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
 print('\nORIGINAL IMAGES :\n')
 ooo.plot_images(x_samples,  y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
 print('\nENHANCED :\n')
 for k,d in datasets.items():
    print("dataset : {}  min,max=[{:.3f},{:.3f}]  shape={}".format(k,d.min(),d.max(), d.shape))
    ooo.plot_images(d, y_samples, range(16), columns=16, x_size=1, y_size=1, colorbar=False, y_pred=None, cm='binary')
 ```
 %% Cell type:markdown id: tags:
 ### 6.3/ Cook and save
 A function to save a dataset
 %% Cell type:code id: tags:
 ``` python
 def save_h5_dataset(x_train, y_train, x_test, y_test, x_meta,y_meta, h5name):
    # ---- Filename
    filename='./data/'+h5name
    # ---- Create h5 file
    with h5py.File(filename, "w") as f:
        f.create_dataset("x_train", data=x_train)
        f.create_dataset("y_train", data=y_train)
        f.create_dataset("x_test",  data=x_test)
        f.create_dataset("y_test",  data=y_test)
        f.create_dataset("x_meta",  data=x_meta)
        f.create_dataset("y_meta",  data=y_meta)
    # ---- done
    size=os.path.getsize(filename)/(1024*1024)
    print('Dataset : {:24s}  shape : {:22s} size : {:6.1f} Mo   (saved)\n'.format(filename, str(x_train.shape),size))
 ```
 %% Cell type:markdown id: tags:
 Create enhanced datasets, and save them...
+Will take about 7-8'
 %% Cell type:code id: tags:
 ``` python
 %%time
 for s in [24, 48]:
    for m in ['RGB', 'RGB-HE', 'L', 'L-LHE']:
        # ---- A nice dataset name
        name='set-{}x{}-{}.h5'.format(s,s,m)
        print("\nDataset : ",name)
        # ---- Enhancement
        x_train_new = images_enhancement( x_train, width=s, height=s, mode=m )
        x_test_new  = images_enhancement( x_test,  width=s, height=s, mode=m )
        x_meta_new  = images_enhancement( x_meta,  width=s, height=s, mode='RGB' )
        # ---- Save
        save_h5_dataset( x_train_new, y_train, x_test_new, y_test, x_meta_new,y_meta, name)
 x_train_new,x_test_new=0,0
 ```
 %% Cell type:markdown id: tags:
 ## 7/ Reload data to be sure ;-)
 %% Cell type:code id: tags:
 ``` python
 %%time
 dataset='set-48x48-L'
 samples=range(24)
 with  h5py.File('./data/'+dataset+'.h5') as f:
    x_tmp = f['x_train'][:]
    y_tmp = f['y_train'][:]
    print("dataset loaded from h5 file.")
 ooo.plot_images(x_tmp,y_tmp, samples, columns=8, x_size=2, y_size=2, colorbar=False, y_pred=None, cm='binary')
 x_tmp,y_tmp=0,0
 ```
 %% Cell type:markdown id: tags:
 ----
 That's all folks !

--- a/GTSRB/02-First-convolutions.ipynb
+++ b/GTSRB/02-First-convolutions.ipynb
--- a/GTSRB/README.ipynb
+++ b/GTSRB/README.ipynb
@@ -24,14 +24,17 @@
    "Environment, with the following packages :\n",
    " - Python 3.6\n",
    " - numpy\n",
-    " - Matplotlib\n",
    " - Tensorflow 2.0\n",
    " - scikit-image\n",
+    " - scikit-learn\n",
+    " - Matplotlib\n",
+    " - seaborn\n",
    " \n",
    "You can create it from the `environment.yml` file :\n",
    "```\n",
    "# conda env create -f environment.yml\n",
    "```\n",
+    "To manage conda environment see [there](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)  \n",
    "\n",
    "About the dataset\n",
    "-----------------\n",
@@ -88,7 +91,7 @@
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.5"
  }
 },
 "nbformat": 4,

 %% Cell type:markdown id: tags:
 German Traffic Sign Recognition Benchmark (GTSRB)
 =================================================
 ---
 Introduction au Deep Learning  (IDLE)
 S. Aria, E. Maldonado, JL. Parouty
 CNRS/SARI/DEVLOG - 2020
 Objectives of this practical work
 ---------------------------------
 Traffic sign classification with **CNN**, using Tensorflow and **Keras**
 Prerequisite
 ------------
 Environment, with the following packages :
 - Python 3.6
 - numpy
- - Matplotlib
 - Tensorflow 2.0
 - scikit-image
+ - scikit-learn
+ - Matplotlib
+ - seaborn
 You can create it from the `environment.yml` file :
 ```
 # conda env create -f environment.yml
 ```
+To manage conda environment see [there](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#)
 About the dataset
 -----------------
 Name : [German Traffic Sign Recognition Benchmark (GTSRB)](http://benchmark.ini.rub.de/?section=gtsrb)
 Available [here](https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html)
 or on **[kaggle](https://www.kaggle.com/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)**
 A nice example from : [Alex Staravoitau](https://navoshta.com/traffic-signs-classification/)
 In few words :
 - Images : Variable dimensions, rgb
 - Train set : 39209 images
 - Test set : 12630 images
 - Classes : 0 to 42
 Episodes
 --------
 **[01 - Preparation of data](01-Preparation-of-data.ipynb)**
 - Understanding the dataset
 - Preparing and formatting data
 - Organize and backup data
 **[02 - First convolutions](02-First-convolutions.ipynb)**
 - Read dataset
 - Build a model
 - Train the model
 - Model evaluation
 %% Cell type:code id: tags:
 ``` python
 ```