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CARIA.2.1

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Update for the final presentation
huge change with previous version
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ccunatbrule
2024-09-03 12:12:47 +02:00
parent 241121a7d1
commit 75e144ace5
72 changed files with 355347 additions and 1009 deletions
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.gitignore vendored Normal file
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Thumbs.db
.DS_Store

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server-ia/Camera_Identification_RoadSign/NOTWORKING.txt
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server-ia/Camera_Identification_RoadSign/modele.py
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import cv2
import glob
import numpy as np
import json
import requests
# Charger le modèle TensorFlow
import tensorflow as tf
model = tf.keras.models.load_model('server-ia/data/modeles/traffic_signs_model/')
# Chemin vers les images de test
# path = 'server-ia/data/videos/autoroute.mp4'
path = 'server-ia/data/images/panneaux/France_road_sign_B14_(5).svg.png'
images = glob.glob(path)
# Initialiser les listes pour les données et les étiquettes
data = []
# Récupérer les images et leurs étiquettes
for im in images[:5]: # Charger seulement les 5 premières images pour l'exemple
try:
# Lire l'image et redimensionner
image = cv2.imread(im)
image = cv2.resize(image, (30, 30))
# Ajouter l'image à la liste des données
data.append(image)
except Exception as e:
print(f"Erreur lors du chargement de l'image {im}: {e}")
# Convertir la liste des données en un tableau numpy
test_imgs = np.array(data)
# Effectuer les prédictions sur les images
predictions = model.predict(test_imgs)
# Afficher les prédictions
print(predictions)
#dictionary to label all traffic signs class.
classes = { 0:'Speed limit (20km/h)',
1:'Speed limit (30km/h)',
2:'Speed limit (50km/h)',
3:'Speed limit (60km/h)',
4:'Speed limit (70km/h)',
5:'Speed limit (80km/h)',
6:'End of speed limit (80km/h)',
7:'Speed limit (100km/h)',
8:'Speed limit (120km/h)',
9:'No passing',
10:'No passing veh over 3.5 tons',
11:'Right-of-way at intersection',
12:'Priority road',
13:'Yield',
14:'Stop',
15:'No vehicles',
16:'Veh > 3.5 tons prohibited',
17:'No entry',
18:'General caution',
19:'Dangerous curve left',
20:'Dangerous curve right',
21:'Double curve',
22:'Bumpy road',
23:'Slippery road',
24:'Road narrows on the right',
25:'Road work',
26:'Traffic signals',
27:'Pedestrians',
28:'Children crossing',
29:'Bicycles crossing',
30:'Beware of ice/snow',
31:'Wild animals crossing',
32:'End speed + passing limits',
33:'Turn right ahead',
34:'Turn left ahead',
35:'Ahead only',
36:'Go straight or right',
37:'Go straight or left',
38:'Keep right',
39:'Keep left',
40:'Roundabout mandatory',
41:'End of no passing',
42:'End no passing veh > 3.5 tons' }

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server-ia/Camera_Identification_RoadSign/predict.py
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import cv2
import numpy as np
import tensorflow as tf
# Chemin vers le dossier contenant le modèle SavedModel
model_dir = "server-ia/data/modeles/traffic_signs_model/1"
# Chargement du modèle SavedModel
model = tf.keras.models.load_model(model_dir)
# Fonction de prétraitement de l'image
def preprocess_image(image):
# Redimensionner l'image à la taille attendue par le modèle
processed_image = cv2.resize(image, (30, 30))
# Assurez-vous que l'image est dans le format attendu par le modèle
processed_image = processed_image.astype("float32") / 255.0 # Normalisation
processed_image = np.expand_dims(processed_image, axis=0) # Ajouter une dimension pour l'axe du lot
return processed_image
# Fonction de détection des panneaux
def detect_signs(video_path):
cap = cv2.VideoCapture(video_path)
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Prétraitement de l'image
processed_frame = preprocess_image(frame)
# Faire une prédiction avec le modèle
predictions = model.predict(processed_frame)
# Analyser les prédictions et afficher les résultats sur l'image
# Remplacez cette partie par votre code de détection et d'affichage des panneaux
# Affichage de la vidéo avec les détections
cv2.imshow('Video', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
# Appel de la fonction pour détecter les panneaux dans la vidéo
video_path = "server-ia/data/videos/autoroute.mp4"
detect_signs(video_path)

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server-ia/Camera_Identification_User/identifie.py
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#!/usr/bin/env python
import cv2
import pickle
import numpy as np
min_size=50
face_cascade= cv2.CascadeClassifier("server-ia/data/haarcascades/haarcascade_frontalface_alt2.xml")
recognizer=cv2.face.LBPHFaceRecognizer_create()
recognizer.read("server-ia/data/modeles/camera_identification_user/trainner.yml")
id_image=0
color_info=(255, 255, 255)
color_ko=(0, 0, 255)
color_ok=(0, 255, 0)
url = "http://192.168.52.194:8081"
with open("server-ia/data/modeles/camera_identification_user/labels.pickle", "rb") as f:
og_labels=pickle.load(f)
labels={v:k for k, v in og_labels.items()}
cap=cv2.VideoCapture(0)
while True:
ret, frame=cap.read()
tickmark=cv2.getTickCount()
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces=face_cascade.detectMultiScale(gray, scaleFactor=1.2,minNeighbors=4, minSize=(min_size, min_size))
for (x, y, w, h) in faces:
roi_gray=gray[y:y+h, x:x+w]
#id_, conf=recognizer.predict(cv2.resize(roi_gray, (min_size, min_size)))
id_, conf=recognizer.predict(roi_gray)
if conf<=95:
color=color_ok
name=labels[id_]
else:
color=color_ko
name="Inconnu"
label=name+" "+'{:5.2f}'.format(conf)
cv2.putText(frame, label, (x, y-10), cv2.FONT_HERSHEY_DUPLEX, 1, color_info, 1, cv2.LINE_AA)
cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
fps=cv2.getTickFrequency()/(cv2.getTickCount()-tickmark)
cv2.putText(frame, "FPS: {:05.2f}".format(fps), (10, 30), cv2.FONT_HERSHEY_PLAIN, 2, color_info, 2)
cv2.imshow('L42Project', frame)
key=cv2.waitKey(1)&0xFF
if key==ord('q'):
break
if key==ord('a'):
for cpt in range(100):
ret, frame=cap.read()
cv2.destroyAllWindows()
print("Fin")

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server-ia/Camera_Identification_VitesseRoadSign/common.py
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import tensorflow as tf
from tensorflow.keras import layers, models
import os
import cv2
size=42
dir_images_panneaux="server-ia/Camera_Identification_VitesseRoadSign/images_panneaux"
dir_images_autres_panneaux="server-ia/Camera_Identification_VitesseRoadSign/images_autres_panneaux"
dir_images_sans_panneaux="server-ia/Camera_Identification_VitesseRoadSign/images_sans_panneaux"
def panneau_model(nbr_classes):
model=tf.keras.Sequential()
model.add(layers.Input(shape=(size, size, 3), dtype='float32'))
model.add(layers.Conv2D(128, 3, strides=1))
model.add(layers.Dropout(0.2))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Conv2D(128, 3, strides=1))
model.add(layers.Dropout(0.2))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPool2D(pool_size=2, strides=2))
model.add(layers.Conv2D(256, 3, strides=1))
model.add(layers.Dropout(0.3))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Conv2D(256, 3, strides=1))
model.add(layers.Dropout(0.4))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPool2D(pool_size=2, strides=2))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.BatchNormalization())
model.add(layers.Dense(nbr_classes, activation='sigmoid'))
return model
def lire_images_panneaux(dir_images_panneaux, size=None):
tab_panneau=[]
tab_image_panneau=[]
if not os.path.exists(dir_images_panneaux):
quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux))
files=os.listdir(dir_images_panneaux)
if files is None:
quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux))
for file in sorted(files):
if file.endswith("png"):
tab_panneau.append(file.split(".")[0])
image=cv2.imread(dir_images_panneaux+"/"+file)
if size is not None:
image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4)
tab_image_panneau.append(image)
return tab_panneau, tab_image_panneau

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server-ia/Camera_Identification_VitesseRoadSign/extract_panneau.py
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import cv2
import os
import numpy as np
import random
# Tuto25[OpenCV] Lecture des panneaux de vitesse p.1 (houghcircles) 16min
size=42
video_dir = "server-ia/data/videos"
# Liste tous les fichiers dans le répertoire vidéo
l = os.listdir(video_dir)
for video in l:
if not video.endswith("mp4"):
continue
cap = cv2.VideoCapture(video_dir + "/" + video)
print("video:", video)
while True:
# Capture une frame de la vidéo
ret, frame = cap.read()
if ret is False:
break
# Redimensionne la frame pour un affichage correct
f_w, f_h, f_c = frame.shape
frame = cv2.resize(frame, (int(f_h / 1.5), int(f_w / 1.5)))
# Extrait une région d'intérêt (ROI) de la frame
image = frame[200:400, 700:1000]
# represents the top left corner of rectangle
start_point = (600, 50)
# Ending coordinate
# represents t
# he bottom right corner of rectangle
end_point = (800, 450)
# Color in BGR
color = (255, 255, 255)
# Line thickness
thickness = 1
# Dessine un rectangle autour de la ROI (dimensions spécifiées)
cv2.rectangle(frame, start_point, end_point, color, thickness)
# Convertit l'image en niveaux de gris pour la détection des cercles
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Détection des cercles dans l'image
circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=30, param2=60, minRadius=5, maxRadius=45)
if circles is not None:
circles = np.int16(np.around(circles))
for i in circles[0, :]:
if i[2] != 0:
cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 4)
# Extrait le panneau de signalisation à partir de la position et du rayon du cercle
panneau = cv2.resize(
image[max(0, i[1] - i[2]):i[1] + i[2], max(0, i[0] - i[2]):i[0] + i[2]],
(size, size)) / 255
cv2.imshow("panneau", panneau)
# Affiche le nom du fichier vidéo en cours
cv2.putText(frame, "fichier:" + video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
# Affiche la frame avec le rectangle et le panneau détecté
cv2.imshow("Video", frame)
# Attend l'appui d'une touche et traite les actions associées
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
quit()
if key == ord('a'):
for cpt in range(100):
ret, frame = cap.read()
if key == ord('f'):
break
# Ferme toutes les fenêtres OpenCV
cv2.destroyAllWindows()

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server-ia/Camera_Identification_VitesseRoadSign/houghcircles.py
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import cv2
import numpy as np
# Tuto25[OpenCV] Lecture des panneaux de vitesse p.1 (houghcircles) 14min
param1=30
param2=55
dp=1.0
video_path = "server-ia/data/videos/ville.mp4"
cap=cv2.VideoCapture(video_path)
while True:
ret, frame=cap.read()
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, dp, 20, param1=param1, param2=param2, minRadius=10, maxRadius=50)
if circles is not None:
circles=np.around(circles).astype(np.int32)
for i in circles[0, :]:
if i[2]!=0:
cv2.circle(frame, (i[0], i[1]), i[2], (0, 255, 0), 4)
cv2.putText(frame, "[i|k]dp: {:4.2f} [o|l]param1: {:d} [p|m]param2: {:d}".format(dp, param1, param2), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 0), 1)
cv2.imshow("Video", frame)
key=cv2.waitKey(1)&0xFF
if key==ord('q'):
quit()
if key==ord('i'):
dp=min(10, dp+0.1)
if key==ord('k'):
dp=max(0.1, dp-0.1)
if key==ord('o'):
param1=min(255, param1+1)
if key==ord('l'):
param1=max(1, param1-1)
if key==ord('p'):
param2=min(255, param2+1)
if key==ord('m'):
param2=max(1, param2-1)
cv2.destroyAllWindows()

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server-ia/Camera_Identification_VitesseRoadSign/lire_panneau.py
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import tensorflow as tf
from tensorflow.keras import layers, models
import cv2
import os
import numpy as np
import random
th1=30
th2=55
size=42
video_dir="server-ia/data/videos/"
dir_images_panneaux="server-ia/data/images/panneaux"
def panneau_model(nbr_classes):
model=tf.keras.Sequential()
model.add(layers.Input(shape=(size, size, 3), dtype='float32'))
model.add(layers.Conv2D(128, 3, strides=1))
model.add(layers.Dropout(0.2))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Conv2D(128, 3, strides=1))
model.add(layers.Dropout(0.2))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPool2D(pool_size=2, strides=2))
model.add(layers.Conv2D(256, 3, strides=1))
model.add(layers.Dropout(0.3))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.Conv2D(256, 3, strides=1))
model.add(layers.Dropout(0.4))
model.add(layers.BatchNormalization())
model.add(layers.Activation('relu'))
model.add(layers.MaxPool2D(pool_size=2, strides=2))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dropout(0.5))
model.add(layers.BatchNormalization())
model.add(layers.Dense(nbr_classes, activation='sigmoid'))
return model
def lire_images_panneaux(dir_images_panneaux, size=None):
tab_panneau=[]
tab_image_panneau=[]
if not os.path.exists(dir_images_panneaux):
quit("Le repertoire d'image n'existe pas: {}".format(dir_images_panneaux))
files=os.listdir(dir_images_panneaux)
if files is None:
quit("Le repertoire d'image est vide: {}".format(dir_images_panneaux))
for file in sorted(files):
if file.endswith("png"):
tab_panneau.append(file.split(".")[0])
image=cv2.imread(dir_images_panneaux+"/"+file)
if size is not None:
image=cv2.resize(image, (size, size), cv2.INTER_LANCZOS4)
tab_image_panneau.append(image)
return tab_panneau, tab_image_panneau
tab_panneau, tab_image_panneau=lire_images_panneaux(dir_images_panneaux)
model_panneau=panneau_model(len(tab_panneau))
checkpoint=tf.train.Checkpoint(model_panneau=model_panneau)
checkpoint.restore(tf.train.latest_checkpoint("server-ia\data\modeles\road_sign_speed_trainers/"))
l=os.listdir(video_dir)
random.shuffle(l)
for video in l:
if not video.endswith("mp4"):
continue
cap=cv2.VideoCapture(video_dir+"/"+video)
print("video:", video)
id_panneau=-1
while True:
ret, frame=cap.read()
if ret is False:
break
f_w, f_h, f_c=frame.shape
frame=cv2.resize(frame, (int(f_h/1.5), int(f_w/1.5)))
image=frame[200:400, 700:1000]
# represents the top left corner of rectangle
start_point = (600, 50)
# Ending coordinate
# represents t
# he bottom right corner of rectangle
end_point = (800, 450)
# Color in BGR
color = (255, 255, 255)
# Line thickness
thickness = 1
cv2.rectangle(frame, start_point, end_point, color, thickness)
gray=cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
circles=cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, 20, param1=th1, param2=th2, minRadius=5, maxRadius=45)
if circles is not None:
circles=np.int16(np.around(circles))
for i in circles[0,:]:
if i[2]!=0:
panneau=cv2.resize(image[max(0, i[1]-i[2]):i[1]+i[2], max(0, i[0]-i[2]):i[0]+i[2]], (size, size))/255
cv2.imshow("panneau", panneau)
prediction=model_panneau(np.array([panneau]), training=False)
print("Prediction:", prediction)
if np.any(np.greater(prediction[0], 0.6)):
id_panneau=np.argmax(prediction[0])
print(" -> C'est un panneau:", tab_panneau[id_panneau], "KM/H")
w, h, c=tab_image_panneau[id_panneau].shape
else:
print(" -> Ce n'est pas un panneau")
if id_panneau!=-1:
frame[0:h, 0:w, :]=tab_image_panneau[id_panneau]
cv2.putText(frame, "fichier:"+video, (30, 30), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 0), 1, cv2.LINE_AA)
cv2.imshow("Video", frame)
key=cv2.waitKey(1)&0xFF
if key==ord('q'):
quit()
if key==ord('a'):
for cpt in range(100):
ret, frame=cap.read()
if key==ord('f'):
break
cv2.destroyAllWindows()

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server-ia/Camera_Ligne_Detection/canny.py
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import cv2
import numpy as np
th1=75
th2=150
stop=0
k=3
cap=cv2.VideoCapture(0)
while True:
if not stop:
ret, frame=cap.read()
if ret is False:
quit()
image=frame.copy()
cv2.putText(image, "[u|j]th1: {:d} [i|k]th2: {:d} [y|h]blur: {:d}".format(th1, th2, k), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 1)
cv2.putText(image, "[a]>> [s]stop [q]quit", (10, 70), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 1)
cv2.imshow("image", image)
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if k!=1:
gray=cv2.blur(gray, (k, k))
gray_canny=cv2.Canny(gray, th1, th2)
cv2.imshow("blur", gray)
cv2.imshow("canny", gray_canny)
if not stop:
key=cv2.waitKey(10)&0xFF
else:
key=cv2.waitKey()
image=frame.copy()
if key==ord('q'):
break
if key==ord('y'):
k=min(255, k+2)
if key==ord('h'):
k=max(1, k-2)
if key==ord('u'):
th1=min(255, th1+1)
if key==ord('j'):
th1=max(0, th1-1)
if key==ord('i'):
th2=min(255, th2+1)
if key==ord('k'):
th2=max(0, th2-1)
if key==ord('s'):
stop=not stop
if key==ord('a'):
for cpt in range(200):
ret, frame=cap.read()
image=frame.copy()
cap.release()
cv2.destroyAllWindows()

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server-ia/Camera_Ligne_Detection/franchissement_ligne.py
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import cv2
import numpy as np
import time
ymin=400
ymax=401
xmin1=20
xmax1=120
xmin2=550
xmax2=620
def point(capteur):
s1=len(capteur)-1
s2=len(capteur)-1
for i in range(len(capteur)):
if capteur[i]!=0:
s1=i
break
if s1!=len(capteur)-1:
for i in range(len(capteur)-1, s1-1, -1):
if capteur[i]!=0:
s2=i
break
return int((s1+s2)/2)
return -1
s1_old=0
s2_old=0
s1=0
s2=0
s1_time=0
s2_time=0
th1=75
th2=150
stop=0
k=3
url = "http://192.168.74.194:8081"
cap=cv2.VideoCapture(url)
while True:
if not stop:
ret, frame=cap.read()
if ret is False:
quit()
image=frame.copy()
gray1=cv2.cvtColor(frame[ymin:ymax, xmin1:xmax1], cv2.COLOR_BGR2GRAY)
if k!=1:
gray1=cv2.blur(gray1, (k, k))
capteur1=cv2.Canny(gray1, th1, th2)
gray2=cv2.cvtColor(frame[ymin:ymax, xmin1:xmax1], cv2.COLOR_BGR2GRAY)
if k!=1:
gray2=cv2.blur(gray2, (k, k))
capteur2=cv2.Canny(gray2, th1, th2)
cv2.rectangle(image, (xmin1, ymin), (xmax1, ymax), (0, 0, 255), 1)
cv2.rectangle(image, (xmin2, ymin), (xmax2, ymax), (0, 0, 255), 1)
s1=point(capteur1[0])
if s1!=-1:
cv2.circle(image, (s1+xmin1, ymin), 3, (0, 255, 0), 3)
s1_old=s1
s1_time=time.time()
else:
if time.time()-s1_time<1:
cv2.circle(image, (s1_old+xmin1, ymin), 3, (100, 255, 255), 3)
s1=s1_old
else:
s1=-1
s2=point(capteur2[0])
if s2!=-1:
cv2.circle(image, (s2+xmin2, ymin), 3, (0, 255, 0), 3)
s2_old=s2
s2_time=time.time()
else:
if time.time()-s2_time<1:
cv2.circle(image, (s2_old+xmin2, ymin), 3, (100, 255, 255), 3)
s2=s2_old
else:
s2=-1
if s1!=-1 and s2!=-1:
s2_=abs(xmax2-xmin2-s2)
if abs(s2_-s1)>20:
c=(0, max(0, 255-10*int(abs(s1-s2_)/2)), min(255, 10*int(abs(s1-s2_)/2)))
cv2.circle(image, (int((xmax2-xmin1)/2)+xmin1, ymax-25), 5, c, 7)
cv2.arrowedLine(image, (int((xmax2-xmin1)/2)+xmin1, ymax-25), (int((xmax2-xmin1)/2)+xmin1+2*int((s1-s2_)/2), ymax-25), c, 3, tipLength=0.4)
else:
cv2.putText(image, "OK", (int((xmax2-xmin1)/2)+xmin1-15, ymax-16), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
cv2.putText(image, "[u|j]th1: {:d} [i|k]th2: {:d} [y|h]blur: {:d}".format(th1, th2, k), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 1)
cv2.putText(image, "[a]>> [s]stop [q]quit", (10, 70), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 1)
cv2.imshow("image", image)
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if k!=1:
gray=cv2.blur(gray, (k, k))
cv2.imshow("blur", gray)
gray_canny=cv2.Canny(gray, th1, th2)
cv2.imshow("canny", gray_canny)
if not stop:
key=cv2.waitKey(20)&0xFF
else:
key=cv2.waitKey()
image=frame.copy()
if key==ord('q'):
break
if key==ord('m'):
ymin+=1
ymax+=1
if key==ord('p'):
ymin-=1
ymax-=1
if key==ord('o'):
xmin1+=1
xmax1+=1
xmin2+=1
xmax2+=1
if key==ord('l'):
xmin1-=1
xmax1-=1
xmin2-=1
xmax2-=1
if key==ord('y'):
k=min(255, k+2)
if key==ord('h'):
k=max(1, k-2)
if key==ord('u'):
th1=min(255, th1+1)
if key==ord('j'):
th1=max(0, th1-1)
if key==ord('i'):
th2=min(255, th2+1)
if key==ord('k'):
th2=max(0, th2-1)
if key==ord('s'):
stop=not stop
if key==ord('a'):
for cpt in range(200):
ret, frame=cap.read()
image=frame.copy()
cap.release()
cv2.destroyAllWindows()

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server-ia/Camera_Ligne_Detection/gradient.py
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import cv2
import numpy as np
stop=0
cap=cv2.VideoCapture(0)
while True:
if not stop:
ret, frame=cap.read()
if ret is False:
quit()
image=frame.copy()
cv2.imshow("image", image)
gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
grad_x=cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize=5)
cv2.imshow("grad x", grad_x)
grad_y=cv2.Sobel(gray, cv2.CV_8U, 0, 1, ksize=5)
cv2.imshow("grad y", grad_y)
if not stop:
key=cv2.waitKey(10)&0xFF
else:
key=cv2.waitKey()
image=frame.copy()
if key==ord('q'):
break
if key==ord('s'):
stop=not stop
if key==ord('a'):
for cpt in range(200):
ret, frame=cap.read()
image=frame.copy()
cap.release()
cv2.destroyAllWindows()

3
server-ia/Identification_Yolov3/NOTE.txt
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python server-ia/Identification_Yolov3/yolo_opencv.py --image server-ia/data/images/dog.jpg --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt
python server-ia/Identification_Yolov3/yolo_opencv_video.py --video cam --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt
python server-ia/Identification_Yolov3/yolo_opencv_video.py --video server-ia/data/videos/ville.mp4 --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt

90
server-ia/Identify_RoadSignV1/identify-RoadSign-speed.py
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import cv2
from tensorflow.keras.models import load_model
import numpy as np
# Charger le modèle sauvegardé
model = load_model("server-ia/data/modeles/RoadSign/modele_signaux_routiers.h5")
# Fonction pour charger les noms de classe à partir d'un fichier
def load_class_names(file_path):
with open(file_path, 'r') as file:
class_names = [line.strip() for line in file.readlines()]
return class_names
# Définir le chemin du fichier contenant les noms de classe
class_names_file = "server-ia/data/modeles/RoadSign/class_names.txt"
# Charger les noms de classe à partir du fichier
class_names = load_class_names(class_names_file)
# Fonction pour détecter les panneaux de signalisation dans une image
def detect_sign(image):
# Prétraiter l'image
preprocessed_img = preprocess_image(image)
# Faire une prédiction avec le modèle
predictions = model.predict(preprocessed_img)
# Obtenir l'indice de la classe prédite
predicted_class_index = np.argmax(predictions)
# Récupérer le nom de la classe prédite
predicted_class_name = class_names[predicted_class_index]
return predicted_class_name
# Fonction pour prétraiter l'image
def preprocess_image(image):
# Mettre à l'échelle l'image aux dimensions attendues par le modèle
scaled_image = cv2.resize(image, (224, 224))
scaled_image = scaled_image.astype("float") / 255.0
# Ajouter une dimension pour correspondre à la forme d'entrée du modèle
preprocessed_img = np.expand_dims(scaled_image, axis=0)
return preprocessed_img
# Définir la source vidéo ou la caméra
# Pour une vidéo
video_path = "server-ia/data/videos/autoroute.mp4"
cap = cv2.VideoCapture(video_path)
# Pour la caméra
# cap = cv2.VideoCapture(0)
# Nombre de trames à sauter entre chaque trame lue
skip_frames = 5
# Boucle pour lire les images de la vidéo ou de la caméra
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Incrémenter le compteur de trames
frame_count += 1
# Si le compteur de trames est un multiple de skip_frames, traiter la trame et afficher
if frame_count % skip_frames == 0:
# Réinitialiser le compteur de trames
frame_count = 0
# Détecter les panneaux de signalisation dans l'image
predicted_class_name = detect_sign(frame)
# Dessiner un carré autour de chaque objet détecté et afficher le nom de la classe au-dessus
# (ici, je vais dessiner un carré au hasard pour donner un exemple)
x_min, y_min, x_max, y_max = 900, 50, 1200, 650 # Coordonnées de la boîte englobante (à remplacer par les bonnes valeurs)
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
cv2.putText(frame, predicted_class_name, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# Afficher l'image
cv2.imshow('Frame', frame)
# Attendre la touche 'q' pour quitter
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Libérer la capture et fermer les fenêtres
cap.release()
cv2.destroyAllWindows()

97
server-ia/Identify_RoadSignV1/identify-RoadSign-speed2.py
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import cv2
from tensorflow.keras.models import load_model
import numpy as np
# Charger le modèle sauvegardé
model = load_model("server-ia/data/modeles/RoadSign/modele_signaux_routiers.h5")
# Fonction pour charger les noms de classe à partir d'un fichier
def load_class_names(file_path):
with open(file_path, 'r') as file:
class_names = [line.strip() for line in file.readlines()]
return class_names
# Définir le chemin du fichier contenant les noms de classe
class_names_file = "server-ia/data/modeles/RoadSign/class_names.txt"
# Charger les noms de classe à partir du fichier
class_names = load_class_names(class_names_file)
# Fonction pour prétraiter l'image
def preprocess_image(image):
# Mettre à l'échelle l'image aux dimensions attendues par le modèle
scaled_image = cv2.resize(image, (224, 224))
scaled_image = scaled_image.astype("float") / 255.0
# Ajouter une dimension pour correspondre à la forme d'entrée du modèle
preprocessed_img = np.expand_dims(scaled_image, axis=0)
return preprocessed_img
# Fonction pour obtenir les coordonnées de la boîte englobante
def obtenir_coordonnees_boite(predictions):
# A DEFINIR
# Supposons que predictions est une liste de [x_min, y_min, x_max, y_max] pour chaque boîte englobante
return predictions[0] # Retourne les coordonnées de la première boîte englobante
# Fonction pour détecter les panneaux de signalisation dans une image
def detect_sign(image):
# Prétraiter l'image
preprocessed_img = preprocess_image(image)
# Faire une prédiction avec le modèle
predictions = model.predict(preprocessed_img)
# Obtenir l'indice de la classe prédite
predicted_class_index = np.argmax(predictions)
# Récupérer le nom de la classe prédite
predicted_class_name = class_names[predicted_class_index]
return predicted_class_name, predictions
# Définir la source vidéo ou la caméra
# Pour une vidéo
video_path = "server-ia/data/videos/autoroute.mp4"
cap = cv2.VideoCapture(video_path)
# Pour la caméra
# cap = cv2.VideoCapture(0)
# Nombre de trames à sauter entre chaque trame lue
skip_frames = 5
# Boucle pour lire les images de la vidéo ou de la caméra
frame_count = 0
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Incrémenter le compteur de trames
frame_count += 1
# Si le compteur de trames est un multiple de skip_frames, traiter la trame et afficher
if frame_count % skip_frames == 0:
# Réinitialiser le compteur de trames
frame_count = 0
# Détecter les panneaux de signalisation dans l'image
predicted_class_name, predictions = detect_sign(frame)
# Obtenir les coordonnées de la boîte englobante de l'objet détecté
x_min, y_min, x_max, y_max = obtenir_coordonnees_boite(predictions)
# Dessiner un carré autour de chaque objet détecté et afficher le nom de la classe au-dessus
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), (0, 255, 0), 2)
cv2.putText(frame, predicted_class_name, (x_min, y_min - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)
# Afficher l'image
cv2.imshow('Frame', frame)
# Attendre la touche 'q' pour quitter
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Libérer la capture et fermer les fenêtres
cap.release()
cv2.destroyAllWindows()

76
server-ia/Identify_RoadSignV1/identify-RoadSign.py
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import cv2
from tensorflow.keras.models import load_model
import numpy as np
# Charger le modèle sauvegardé
model = load_model("server-ia/data/modeles/RoadSign/modele_signaux_routiers.h5")
# Fonction pour charger les noms de classe à partir d'un fichier
def load_class_names(file_path):
with open(file_path, 'r') as file:
class_names = [line.strip() for line in file.readlines()]
return class_names
# Définir le chemin du fichier contenant les noms de classe
class_names_file = "server-ia/data/modeles/RoadSign/class_names.txt"
# Charger les noms de classe à partir du fichier
class_names = load_class_names(class_names_file)
# Fonction pour détecter les panneaux de signalisation dans une image
def detect_sign(image):
# Prétraiter l'image
preprocessed_img = preprocess_image(image)
# Faire une prédiction avec le modèle
predictions = model.predict(preprocessed_img)
# Obtenir l'indice de la classe prédite
predicted_class_index = np.argmax(predictions)
# Récupérer le nom de la classe prédite
predicted_class_name = class_names[predicted_class_index]
return predicted_class_name
# Fonction pour prétraiter l'image
def preprocess_image(image):
# Mettre à l'échelle l'image aux dimensions attendues par le modèle
scaled_image = cv2.resize(image, (224, 224))
scaled_image = scaled_image.astype("float") / 255.0
# Ajouter une dimension pour correspondre à la forme d'entrée du modèle
preprocessed_img = np.expand_dims(scaled_image, axis=0)
return preprocessed_img
# Définir la source vidéo ou la caméra
# Pour une vidéo
video_path = "server-ia/data/videos/autoroute.mp4"
cap = cv2.VideoCapture(video_path)
# Pour la caméra
# cap = cv2.VideoCapture(0)
# Boucle pour lire les images de la vidéo ou de la caméra
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
# Détecter les panneaux de signalisation dans l'image
predicted_class_name = detect_sign(frame)
# Afficher le résultat sur l'image
cv2.putText(frame, predicted_class_name, (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Afficher l'image
cv2.imshow('Frame', frame)
# Attendre la touche 'q' pour quitter
if cv2.waitKey(1) & 0xFF == ord('q'):
break
# Libérer la capture et fermer les fenêtres
cap.release()
cv2.destroyAllWindows()

3
server-ia/README_Yolov3.txt Normal file
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python server-ia/yolov3_opencv_picture.py --image server-ia/data/images/dog.jpg --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt
python server-ia/yolov3_opencv_video.py --video cam --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt
python server-ia/yolov3_opencv_video.py --video server-ia/data/videos/ville.mp4 --config server-ia/data/modeles/yolov3/yolov3.cfg --weights server-ia/data/modeles/yolov3/yolov3.weights --classes server-ia/data/modeles/yolov3/yolov3.txt

7
server-ia/README_cv2_ligne_detection.txt Normal file
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Largeur route 35cm
Largeur voiture ~19 cm
Analyse des bandes - 50cm
-------------------------
Réel
Largeur route 3,5m
Largeur voiture 1,8 m

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server-ia/TF_Identification_Training_CCB.py Normal file
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import tensorflow as tf
import numpy as np
import cv2
import os
import pandas as pd
# Configuration
size = 60 # Taille des images à laquelle le modèle a été entraîné
model_path = "server-ia/data/modeles/tensorflow/tf_modele_speed_panneau.keras"
labels_csv_path = "server-ia/data/modeles/tensorflow/train_labels.csv"
confidence_threshold = 0.6 # Seuil de confiance pour considérer une prédiction comme valide
# Fonction pour charger le mapping des labels depuis le fichier CSV
def load_labels_mapping(labels_csv_path):
df = pd.read_csv(labels_csv_path)
unique_labels = df['label'].unique()
labels_mapping = {label: idx for idx, label in enumerate(unique_labels)}
print(f"Mapping des labels chargé : {labels_mapping}")
return labels_mapping
# Chargement du mapping des labels
labels_mapping = load_labels_mapping(labels_csv_path)
# Inversion du mapping pour obtenir l'étiquette à partir de l'index
reverse_labels_mapping = {v: k for k, v in labels_mapping.items()}
# Chargement du modèle
model = tf.keras.models.load_model(model_path)
print(f"Modèle chargé depuis {model_path}")
# Fonction pour prédire la catégorie d'une image, encadrer la zone reconnue et afficher les résultats
def predict_and_draw_bounding_box(image_path, model, labels_mapping, size, confidence_threshold):
# Chargement de l'image originale
image = cv2.imread(image_path)
if image is None:
raise ValueError(f"Erreur de chargement de l'image : {image_path}")
# Redimensionnement et normalisation pour la prédiction
image_resized = cv2.resize(image, (size, size), cv2.INTER_LANCZOS4)
image_normalized = image_resized.astype('float32') / 255.0 # Normalisation
image_batch = np.expand_dims(image_normalized, axis=0) # Ajout d'une dimension pour le batch
# Prédiction
predictions = model.predict(image_batch)
predicted_index = np.argmax(predictions, axis=1)[0]
confidence = np.max(predictions)
# Récupération du label prédictif ou "Inconnu" si la confiance est faible
if confidence < confidence_threshold:
predicted_label = "Inconnu"
print(f"Confiance faible ({confidence:.2f}). La prédiction n'est pas fiable.")
else:
predicted_label = reverse_labels_mapping[predicted_index]
# Affichage de la prédiction dans la console
print(f"Image: {image_path}, Prédiction: {predicted_label} (Confiance: {confidence:.2f})")
# Encadrement de la zone reconnue (l'ensemble de l'image redimensionnée)
height, width, _ = image.shape
cv2.rectangle(image, (0, 0), (width, height), (0, 255, 0), 2) # Couleur verte et épaisseur de 2
# Ajout du texte de la prédiction sur l'image
cv2.putText(image, f'Predicted: {predicted_label}', (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
# Affichage de l'image avec le cadre
cv2.imshow("Image avec zone reconnue", image)
cv2.waitKey(0)
cv2.destroyAllWindows()
# Exemple d'utilisation
image_to_classify = "server-ia/data/images/panneaux/France_road_sign_B14_(10).svg.png" # Remplacez par le chemin exact de l'image à classifier
try:
predict_and_draw_bounding_box(image_to_classify, model, labels_mapping, size, confidence_threshold)
except ValueError as e:
print(e)

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server-ia/data/modeles/CV2/README.txt Normal file
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Généré par server-trainer\UsersIdentification-ModelTraining.py
Utilisé pour server-ia\Camera_Identification_User\identifie.py

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server-ia/data/modeles/MobileNetV2/README.txt Normal file
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Generé par CARIA\server-trainer\RoadSigns-ModelTraining.py

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server-ia/data/modeles/MobileNetV2/class_names.txt Normal file
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0
1
10
11
12
13
14
15
16
17
18
19
2
20
21
22
23
24
25
26
27
28
29
3
30
31
32
33
34
35
36
37
38
39
4
40
41
42
5
6
7
8
9

2
server-ia/data/modeles/tensorflow/README.txt Normal file
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Générer par server-trainer\Camera_Identification_VitesseRoadSign-ModelTraining.py
Utilisé pour server-ia\Camera_Identification_VitesseRoadSign\lire_panneau.py

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server-ia/data/modeles/tensorflow/train_labels.csv Normal file
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server-ia/data/modeles/test/MobileNet-SSD/ssd_mobilenet_v1_coco.pbtxt Normal file
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item {
id: 1
name: 'person'
}
item {
id: 2
name: 'bicycle'
}
item {
id: 3
name: 'car'
}
item {
id: 4
name: 'motorcycle'
}

1
server-ia/data/modeles/traffic_signs_model/fingerprint.pb Normal file
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<08><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>_<10><><EFBFBD><EFBFBD><EFBFBD><EFBFBD>âݠ<><DDA0><EFBFBD><EFBFBD>Ø <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>(<28>݃<EFBFBD><DD83>ǚ<EFBFBD><C79A>2

21
server-ia/data/modeles/traffic_signs_model/keras_metadata.pb Normal file
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server-ia/data/modeles/yolov3-tiny/coco.names Normal file
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person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

182
server-ia/data/modeles/yolov3-tiny/yolov3-tiny.cfg Normal file
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[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=1
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
###########
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=80
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 8
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319
classes=80
num=6
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

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server-ia/data/modeles/yolov3/yolov3.cfg Normal file
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[net]
# Testing
# batch=1
# subdivisions=1
# Training
batch=64
subdivisions=16
width=608
height=608
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .7
truth_thresh = 1
random=1

80
server-ia/data/modeles/yolov3/yolov3.txt Normal file
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person
bicycle
car
motorcycle
airplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
couch
potted plant
bed
dining table
toilet
tv
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

108
server-ia/test_cv2_identifier_user.py Normal file
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#!/usr/bin/env python
import cv2
import pickle
import numpy as np
import time
# Configuration des tailles et couleurs
MIN_SIZE = 50
COLOR_INFO = (255, 255, 255)
COLOR_KO = (0, 0, 255)
COLOR_OK = (0, 255, 0)
# Facteur d'agrandissement pour la fenêtre d'affichage
SCALE_FACTOR = 1.3
# Chargement des classificateurs et des modèles
FACE_CASCADE_PATH = "server-ia/data/haarcascades/haarcascade_frontalface_alt2.xml"
MODEL_PATH = "server-ia/data/modeles/CV2/trainner.yml"
LABELS_PATH = "server-ia/data/modeles/CV2/labels.pickle"
face_cascade = cv2.CascadeClassifier(FACE_CASCADE_PATH)
recognizer = cv2.face.LBPHFaceRecognizer_create()
recognizer.read(MODEL_PATH)
# Chargement des labels
with open(LABELS_PATH, "rb") as f:
og_labels = pickle.load(f)
labels = {v: k for k, v in og_labels.items()}
# Initialisation de la capture vidéo
#cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("http://192.168.253.194:8081")
cap.set(cv2.CAP_PROP_FRAME_WIDTH, 640)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)
def calculate_sharpness(image):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
variance = cv2.Laplacian(gray, cv2.CV_64F).var()
return variance
def process_frame(frame):
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, scaleFactor=1.2, minNeighbors=4, minSize=(MIN_SIZE, MIN_SIZE))
for (x, y, w, h) in faces:
roi_gray = gray[y:y+h, x:x+w]
id_, conf = recognizer.predict(roi_gray)
if conf <= 95:
color = COLOR_OK
name = labels.get(id_, "Inconnu")
else:
color = COLOR_KO
name = "Inconnu"
label = f"{name} {conf:.2f}"
cv2.putText(frame, label, (x, y-10), cv2.FONT_HERSHEY_DUPLEX, 1, COLOR_INFO, 1, cv2.LINE_AA)
cv2.rectangle(frame, (x, y), (x+w, y+h), color, 2)
# Afficher la résolution actuelle
resolution_text = f"Resolution: {frame.shape[1]}x{frame.shape[0]}"
cv2.putText(frame, resolution_text, (10, frame.shape[0] - 40), cv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR_INFO, 1, cv2.LINE_AA)
# Calculer et afficher la netteté
sharpness = calculate_sharpness(frame)
sharpness_text = f"Sharpness: {sharpness:.2f}"
cv2.putText(frame, sharpness_text, (10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR_INFO, 1, cv2.LINE_AA)
return frame
def main():
prev_time = time.time()
while True:
ret, frame = cap.read()
if not ret:
print("Erreur lors de la capture de la vidéo.")
break
frame = process_frame(frame)
# Calculer et afficher le FPS
curr_time = time.time()
fps = 1 / (curr_time - prev_time)
prev_time = curr_time
fps_text = f"FPS: {fps:.2f}"
cv2.putText(frame, fps_text, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, COLOR_INFO, 1, cv2.LINE_AA)
# Redimensionner la frame pour l'affichage
frame_resized = cv2.resize(frame, (int(frame.shape[1] * SCALE_FACTOR), int(frame.shape[0] * SCALE_FACTOR)))
cv2.imshow('CARIA Project - Identification du client', frame_resized)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
elif key == ord('a'):
# Capture 50 frames to avoid blocking
for _ in range(50):
ret, _ = cap.read()
if not ret:
break
cap.release()
cv2.destroyAllWindows()
print("Fin")
if __name__ == "__main__":
main()

172
server-ia/test_cv2_ligne_detection.py Normal file
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import cv2
import numpy as np
import time
# Initialisation des variables
th1 = 75
th2 = 150
k = 3
stop = 0
# Coordonnées des zones d'intérêt pour le traitement
ymin = 350
ymax = 351
xmin1 = 30
xmax1 = 130
xmin2 = 520
xmax2 = 620
# Fonction pour déterminer le point central des contours détectés
def point(capteur):
s1 = len(capteur) - 1
s2 = len(capteur) - 1
for i in range(len(capteur)):
if capteur[i] != 0:
s1 = i
break
if s1 != len(capteur) - 1:
for i in range(len(capteur) - 1, s1 - 1, -1):
if capteur[i] != 0:
s2 = i
break
return int((s1 + s2) / 2)
return -1
# Variables pour suivre les positions précédentes et le temps
s1_old = 0
s2_old = 0
s1_time = 0
s2_time = 0
# Capture vidéo
# cap = cv2.VideoCapture(0)
cap = cv2.VideoCapture("http://192.168.253.194:8081")
while True:
if not stop:
ret, frame = cap.read()
if not ret:
break
image = frame.copy()
# Détection de contours dans les zones d'intérêt
gray1 = cv2.cvtColor(frame[ymin:ymax, xmin1:xmax1], cv2.COLOR_BGR2GRAY)
gray2 = cv2.cvtColor(frame[ymin:ymax, xmin2:xmax2], cv2.COLOR_BGR2GRAY)
if k != 1:
gray1 = cv2.blur(gray1, (k, k))
gray2 = cv2.blur(gray2, (k, k))
capteur1 = cv2.Canny(gray1, th1, th2)
capteur2 = cv2.Canny(gray2, th1, th2)
cv2.rectangle(image, (xmin1, ymin), (xmax1, ymax), (0, 0, 255), 1)
cv2.rectangle(image, (xmin2, ymin), (xmax2, ymax), (0, 0, 255), 1)
# Calcul du point central des contours détectés
s1 = point(capteur1[0])
s2 = point(capteur2[0])
if s1 != -1:
cv2.circle(image, (s1 + xmin1, ymin), 3, (0, 255, 0), 3)
s1_old = s1
s1_time = time.time()
else:
if time.time() - s1_time < 1:
cv2.circle(image, (s1_old + xmin1, ymin), 3, (100, 255, 255), 3)
s1 = s1_old
else:
s1 = -1
if s2 != -1:
cv2.circle(image, (s2 + xmin2, ymin), 3, (0, 255, 0), 3)
s2_old = s2
s2_time = time.time()
else:
if time.time() - s2_time < 1:
cv2.circle(image, (s2_old + xmin2, ymin), 3, (100, 255, 255), 3)
s2 = s2_old
else:
s2 = -1
# Affichage de l'état de l'alignement
if s1 != -1 and s2 != -1:
s2_ = abs(xmax2 - xmin2 - s2)
if abs(s2_ - s1) > 20:
c = (0, max(0, 255 - 10 * int(abs(s1 - s2_) / 2)), min(255, 10 * int(abs(s1 - s2_) / 2)))
cv2.circle(image, (int((xmax2 - xmin1) / 2) + xmin1, ymax - 25), 5, c, 7)
cv2.arrowedLine(image, (int((xmax2 - xmin1) / 2) + xmin1, ymax - 25),
(int((xmax2 - xmin1) / 2) + xmin1 + 2 * int((s1 - s2_) / 2), ymax - 25),
c, 3, tipLength=0.4)
else:
cv2.putText(image, "OK", (int((xmax2 - xmin1) / 2) + xmin1 - 15, ymax - 16),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 0), 1)
# Affichage des images dans des fenêtres distinctes
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
if k != 1:
gray = cv2.blur(gray, (k, k))
gray_canny = cv2.Canny(gray, th1, th2)
# Superposition des commandes après tous les traitements
cv2.putText(image, "[u|j]th1: {:d} [i|k]th2: {:d} [y|h]blur: {:d}".format(th1, th2, k),
(10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 2)
cv2.putText(image, "[a]>> [s]stop [q]quit", (10, 70), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 255, 255), 2)
# Affichage des fenêtres d'image
cv2.imshow("image", image)
cv2.imshow("blur", gray)
cv2.imshow("canny", gray_canny)
# Gestion des événements clavier
if not stop:
key = cv2.waitKey(20) & 0xFF
else:
key = cv2.waitKey()
if key == ord('q'):
break
if key == ord('s'):
stop = not stop
if key == ord('a'):
for cpt in range(200):
ret, frame = cap.read()
if not ret:
break
image = frame.copy()
# Contrôle des paramètres de seuil et de flou
if key == ord('y'):
k = min(255, k + 2)
if key == ord('h'):
k = max(1, k - 2)
if key == ord('u'):
th1 = min(255, th1 + 1)
if key == ord('j'):
th1 = max(0, th1 - 1)
if key == ord('i'):
th2 = min(255, th2 + 1)
if key == ord('k'):
th2 = max(0, th2 - 1)
# Déplacement des zones d'intérêt
if key == ord('m'):
ymin += 1
ymax += 1
if key == ord('p'):
ymin -= 1
ymax -= 1
if key == ord('o'):
xmin1 += 1
xmax1 += 1
xmin2 += 1
xmax2 += 1
if key == ord('l'):
xmin1 -= 1
xmax1 -= 1
xmin2 -= 1
xmax2 -= 1
# Libération des ressources
cap.release()
cv2.destroyAllWindows()

0
server-ia/Camera_Identification_Yolov3-Tiny/real_time_yolo.py → server-ia/yolov3-tiny_real_time_yolo.py
View File

2
server-ia/Identification_Yolov3/yolo_opencv.py → server-ia/yolov3_opencv_picture.py
View File

@@ -74,5 +74,5 @@ for i in indices:
cv2.imshow("object detection", image)
cv2.waitKey()
cv2.imwrite("object-detection.jpg", image)
# cv2.imwrite("object-detection.jpg", image)
cv2.destroyAllWindows()

0
server-ia/Identification_Yolov3/yolo_opencv_video.py → server-ia/yolov3_opencv_video.py
View File