-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathCarsColor_YoloV8n_Min_Distance.py
249 lines (186 loc) · 8.18 KB
/
CarsColor_YoloV8n_Min_Distance.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
# -*- coding: utf-8 -*-
"""
Created on augost 2023
@author: Alfonso Blanco
"""
#######################################################################
# PARAMETERS
######################################################################
dir=""
dirname= "Test1"
import time
import cv2
import joblib
import cvzone
import math
import os
import re
Ini=time.time()
from ultralytics import YOLO
# from # https://medium.com/@shaw801796/your-first-object-detection-model-using-yolo-2e841547cc20
#modified because yolov8x.pt is more precise than yolov8n, but need more resources
modelv8n = YOLO("yolov8x.pt")
class_names = [
"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"
]
import numpy as np
from CarColor_Min_Distance import CarColorImg_Min_Distance
########################################################################
def loadimagesRoboflow (dirname):
#########################################################################
# adapted from:
# https://www.aprendemachinelearning.com/clasificacion-de-imagenes-en-python/
# by Alfonso Blanco García
########################################################################
imgpath = dirname + "\\"
images = []
Licenses=[]
print("Reading imagenes from ",imgpath)
NumImage=-2
Cont=0
for root, dirnames, filenames in os.walk(imgpath):
NumImage=NumImage+1
for filename in filenames:
if re.search("\.(jpg|jpeg|png|bmp|tiff)$", filename):
filepath = os.path.join(root, filename)
License=filename[:len(filename)-4]
#if License != "PGMN112": continue
image = cv2.imread(filepath)
# Roboflow images are (416,416)
#image=cv2.resize(image,(416,416))
# kaggle images
#image=cv2.resize(image, (640,640))
images.append(image)
Licenses.append(License)
Cont+=1
return images, Licenses
# https://medium.com/@shaw801796/your-first-object-detection-model-using-yolo-2e841547cc20
def DetectCarWithYolov8n (img):
TabcropLicense=[]
y=[]
yMax=[]
x=[]
xMax=[]
results = modelv8n(img, stream=True)
SalvaImg=img.copy()
for r in results:
boxes = r.boxes
for box in boxes:
x1,y1,x2,y2 = box.xyxy[0]
x1, y1, x2, y2 = int(x1),int(y1),int(x2),int(y2)
#print(x1,y1,x2,y2)
cv2.rectangle(img,(x1,y1),(x2,y2),(255,0,255),3)
# crop image to center it
xoff= int((x2 - x1)*0.1)
yoff= int((y2 - y1)*0.1)
#if int(box[0]) < xoff or int(box[1]) < xoff or int(box[2]) < xoff or int(box[3]) < xoff:
# continue
x1=x1+xoff
x2=x2-xoff
y1=y1+ yoff
y2 = y2 -yoff
if (x2- x1) < 120: continue
#print(x1,y1,x2,y2)
# # https://medium.com/@shaw801796/your-first-object-detection-model-using-yolo-2e841547cc20
conf = math.ceil((box.conf[0]*100))/100
#if conf < 0.85: continue
cls = int(box.cls[0]) #converting the float to int so that the class name can be called
#if class_names[cls] != "car" and class_names[cls] != "license plate": continue
#if class_names[cls] != "car" and class_names[cls] != "truck" and class_names[cls] != "bus": continue
cvzone.putTextRect(img,f'{class_names[cls]} {conf} ',(max(0,x1),max(35,y1)),scale=1,thickness=1)
cv2.imshow("img", img)
cv2.waitKey()
cropLicense=SalvaImg[y1:y2,x1:x2]
#cv2.imshow("Crop", cropLicense)
#cv2.waitKey(0)
TabcropLicense.append(cropLicense)
y.append(y1)
yMax.append(y2)
x.append(x1)
xMax.append(x2)
return TabcropLicense, y,yMax,x,xMax
###########################################################
# MAIN
##########################################################
arr=[]
arry=[]
arrname=[]
f=open("colors.csv","r")
Conta=0;
for linea in f:
lineadelTrain =linea.split(",")
linea_x =[]
z=2
for x in lineadelTrain:
z=z+1
if z==6: break
linea_x.append(int(lineadelTrain[z]))
arr.append(linea_x)
arry.append(int(Conta))
Conta=Conta+1
arrname.append(lineadelTrain[1])
X_train=np.array(arr)
# print(x)
Y_train=np.array(arry)
Name=np.array(arrname)
imagesComplete, Licenses=loadimagesRoboflow(dirname)
from sklearn.neighbors import KNeighborsClassifier
modelKNN = KNeighborsClassifier(n_neighbors=1)
modelKNN.fit(X_train,Y_train)
print("Number of imagenes : " + str(len(imagesComplete)))
ContDetected=0
ContNoDetected=0
TotHits=0
TotFailures=0
with open( "CarColorResults.txt" ,"w") as w:
for i in range (len(imagesComplete)):
# solo imagenes .jpg pueden ser referenciadas en formato [:, :, 0]
cv2.imwrite('pp.jpg',imagesComplete[i])
img=cv2.imread("pp.jpg")
#cv2.imshow("img", img)
#cv2.waitKey()
TabImgSelect, y, yMax, x, xMax =DetectCarWithYolov8n(img)
#gray=imagesComplete[i]
License=Licenses[i]
if TabImgSelect==[] :
print(License + " NON DETECTED")
continue
for j in range( len(TabImgSelect)):
height = TabImgSelect[j].shape[0]
width = TabImgSelect[j].shape[1]
R, G, B, Y_elected=CarColorImg_Min_Distance(TabImgSelect[j], modelKNN, Name, License)
NameColor=str(Name[int(Y_elected)])
#print(" Is elected by min distance " + NameColor)
X_elected=X_train[Y_elected][0]
#print(X_train[Y_elected])
rgb= "("+str(R)+","+str(G)+","+ str(B)+")"
print(License + " Color code rgb " + rgb)
R_elected=X_elected[0]
G_elected=X_elected[1]
B_elected=X_elected[2]
rgb_elected= "("+str(R_elected)+","+str(G_elected)+","+ str(B_elected)+")"
print(License + " Color code rgb elected " + rgb_elected + " " + NameColor)
print ("")
lineaw=[]
lineaw.append(License)
lineaw.append(str(R))
lineaw.append(str(G))
lineaw.append(str(B))
lineaw.append(str(R_elected))
lineaw.append(str(G_elected))
lineaw.append(str(B_elected))
lineaw.append(NameColor)
lineaWrite =','.join(lineaw)
lineaWrite=lineaWrite + "\n"
w.write(lineaWrite)
print ("seconds "+ str(round((time.time()-Ini),2)))