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Commit 34470249 authored by Victor Demessance's avatar Victor Demessance
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[*] Change knn best parameter computation output display

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%% Cell type:markdown id: tags:
## Entrainement d'un modèle avec la méthode Knn
%% Cell type:code id: tags:
``` python
import os
import numpy as np
import random
from PIL import Image
import matplotlib.pyplot as plt
```
%% Cell type:markdown id: tags:
### 1) Fonctions de Preprocessing des datasets
%% Cell type:code id: tags:
``` python
AVERAGE_SIZE_IMAGE = (127, 145) # Thanks to the stats, we know that size of bbox will be (127, 145) -> Average size of labels
def generate_empty_bbox(image_width, image_height):
"""
Generate an empty box for images without label
"""
# Generating random coords for the bbox
x_min = random.randint(0, image_width - AVERAGE_SIZE_IMAGE[0])
y_min = random.randint(0, image_height - AVERAGE_SIZE_IMAGE[1])
# Compute complete coords of the bbox
x_max = x_min + AVERAGE_SIZE_IMAGE[0]
y_max = y_min + AVERAGE_SIZE_IMAGE[1]
return (x_min, y_min, x_max, y_max)
def load_data(image_dir, label_dir):
"""
Create a dict with all the usefull datas of the dataset
datas = {
"XXXX" (name of the file) : {
"img" : image as an array,
"labels" (data of the labels): {
"X" index of the label (0,1,...,n) : {
"name" : name of the label,
"coord" : coord of the label like xmin, ymin, xmax, ymax,
"img" : crooped img of the label,
}
}
}
}
"""
datas = {}
for image_file in os.listdir(image_dir):
# Computing name and files paths
image_path = image_dir + '/' + image_file
name = image_file.split('.')[0]
label_path = label_dir + '/' + name + '.csv'
# Import image as array
image = np.array(Image.open(image_path))
# Import labels as array
with open(label_path, 'r') as file:
rows = file.readlines()
label_data = {}
if rows == ['\n']: # Create a random empty label to balance model
# Create random coords for empty label
xmin, ymin, xmax, ymax = generate_empty_bbox(image.shape[1], image.shape[0])
# Get the cropped image (as array) of the label
cropped_image = np.array(Image.fromarray(image[ymin:ymax, xmin:xmax]).resize(AVERAGE_SIZE_IMAGE))
label_data[0] = {
"name":"empty",
"coord": (xmin, ymin, xmax, ymax),
"img":cropped_image
}
else:
for i, row in enumerate(rows): # One image can contain several labels
row = row.strip().split(",")
# Compute coords of the label
xmin, ymin, xmax, ymax = map(int, row[0:4])
# Get the label name
class_name = row[4]
# Get the cropped image (as array) of the label
cropped_image = np.array(Image.fromarray(image[ymin:ymax, xmin:xmax]).resize(AVERAGE_SIZE_IMAGE))
# Adding to the json
label_data[i] = {
"name":class_name,
"coord": (xmin, ymin, xmax, ymax),
"img":cropped_image
}
datas[name] = {
"img" : image,
"labels" : label_data,
}
return datas
# Dict to convert str class name to int
name_to_int = {
"danger": 0,
"interdiction": 1,
"obligation": 2,
"stop": 3,
"ceder": 4,
"frouge": 5,
"forange": 6,
"fvert": 7,
"ff": 8,
"empty": 9
}
```
%% Cell type:markdown id: tags:
### 2) Fonction de création des datasets
%% Cell type:code id: tags:
``` python
def create_xy(datas):
# Creating arrays with all labels datas & classes
X = []
Y = []
for name, data in datas.items():
for row in data["labels"].values():
image_as_array = np.array(row["img"]).flatten()
X.append(image_as_array)
Y.append(name_to_int[row["name"]])
X = np.array(X)
Y = np.array(Y)
return X, Y
```
%% Cell type:markdown id: tags:
### 3) Création des datasets
%% Cell type:code id: tags:
``` python
# Training dataset
datas_train = load_data("../../data/train/images", "../../data/train/labels")
X_train, Y_train = create_xy(datas=datas_train)
# Validation dataset
datas_val = load_data("../../data/val/images", "../../data/val/labels")
X_val, Y_val = create_xy(datas=datas_val)
```
%% Cell type:markdown id: tags:
### 4) Application de la méthode Knn
%% Cell type:code id: tags:
``` python
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier(n_neighbors=5) # To change
clf.fit(X_train, Y_train)
y = clf.predict(X_val)
print(f"Taux d'erreur : {np.mean(y != Y_val)}")
```
%% Output
Taux d'erreur : 0.31092436974789917
%% Cell type:markdown id: tags:
### 5) Test de la méthode Knn avec application des caractéristiques HOG
%% Cell type:code id: tags:
``` python
from skimage.feature import hog
from skimage.color import rgb2gray
def extract_hog(datas):
# Creating X array with all HOG information of images
X = []
for name, data in datas.items():
for row in data["labels"].values():
image_as_array = np.array(hog(rgb2gray(row["img"]))).flatten()
X.append(image_as_array)
return np.array(X)
# Update training dataset
X_train_HOG = extract_hog(datas=datas_train)
# Update validation dataset
X_val_HOG = extract_hog(datas=datas_val)
```
%% Cell type:code id: tags:
``` python
clf = KNeighborsClassifier(n_neighbors=8)
clf.fit(X_train_HOG, Y_train)
y_HOG = clf.predict(X_val_HOG)
print(f"Taux d'erreur : {np.mean(y_HOG != Y_val)}")
```
%% Output
Taux d'erreur : 0.17647058823529413
%% Cell type:markdown id: tags:
### 6) Test avec concaténation des informations de couleur
%% Cell type:code id: tags:
``` python
from skimage.color import rgb2hsv
def extract_color_features(datas):
# Creating X array with all HOG information of images
X = []
for name, data in datas.items():
for row in data["labels"].values():
# Convertir l'image en espace colorimétrique HSV
hsv_image = rgb2hsv(row["img"])
# Calculer l'histogramme de couleur pour chaque canal
hue_hist = np.histogram(hsv_image[:,:,0], bins=10, range=(0, 1), density=True)[0]
saturation_hist = np.histogram(hsv_image[:,:,1], bins=10, range=(0, 1), density=True)[0]
value_hist = np.histogram(hsv_image[:,:,2], bins=10, range=(0, 1), density=True)[0]
# Concaténer les histogrammes de couleur
color_features = np.concatenate((hue_hist, saturation_hist, value_hist))
X.append(color_features)
return np.array(X)
# Update training dataset
X_train_COLORS = extract_color_features(datas=datas_train)
# Update validation dataset
X_val_COLORS = extract_color_features(datas=datas_val)
```
%% Cell type:code id: tags:
``` python
X_train_combined = np.concatenate((X_train_HOG, X_train_COLORS), axis=1)
X_val_combined = np.concatenate((X_val_HOG, X_val_COLORS), axis=1)
clf = KNeighborsClassifier(n_neighbors=7)
clf.fit(X_train_combined, Y_train)
y_combined = clf.predict(X_val_combined)
print(f"Taux d'erreur : {np.mean(y_combined != Y_val)}")
```
%% Output
Taux d'erreur : 0.11764705882352941
%% Cell type:markdown id: tags:
### 7) Test des meilleurs paramètres à utiliser :
%% Cell type:code id: tags:
``` python
tab = []
for i in range(1, 20, 1):
clf = KNeighborsClassifier(n_neighbors=i)
clf.fit(X_train_combined, Y_train)
y_HOG = clf.predict(X_val_combined)
tab.append(np.mean(y_HOG != Y_val))
y_combined = clf.predict(X_val_combined)
tab.append(np.mean(y_combined != Y_val))
plt.bar(range(len(tab)), tab, color='skyblue')
plt.show()
print(tab)
print(f"Taux d'erreur : {np.mean(y != Y_val)}")
print(f"Taux d'erreur minimum : {min(tab)}")
```
%% Output
[0.18487394957983194, 0.16806722689075632, 0.11764705882352941, 0.13445378151260504, 0.12605042016806722, 0.12605042016806722, 0.11764705882352941, 0.13445378151260504, 0.14285714285714285, 0.14285714285714285, 0.13445378151260504, 0.14285714285714285, 0.13445378151260504, 0.13445378151260504, 0.15966386554621848, 0.14285714285714285, 0.15966386554621848, 0.15126050420168066, 0.15966386554621848]
Taux d'erreur : 0.31092436974789917
Taux d'erreur minimum : 0.11764705882352941
......
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