-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathvideo_detection.py
184 lines (154 loc) · 7.65 KB
/
video_detection.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
import torch
import cv2
import matplotlib.pyplot as plt
import pytesseract
import os
from datetime import datetime
import numpy as np
import time
from Db_Handler import Db_Handler
import pprint
from PlateInfo import PlateInfo
import pymongo
from pymongo import MongoClient
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt') # or yolov5m, yolov5x, custom
def draw(boxes,plates,frame):
for i in range(len(boxes)):
box=boxes[i]
text=plates[i]
box = [int(x) for x in box]
x1, y1, x2, y2 = box
color = (255, 255, 0)
cv2.rectangle(frame, (x1, y1), (x2, y2), color, 2)
font = cv2.FONT_HERSHEY_SIMPLEX
cv2.putText(frame, text, (x1, y1 - 20), font,
1, color, 2, cv2.LINE_AA)
return frame
if __name__ == '__main__':
cap = cv2.VideoCapture('1.mp4') # Dosyadan video okumak için
prev_frame_time = 0 # son kareyi işlediğimiz zamanı kaydetmek için kullanılır
new_frame_time = 0 # geçerli kareyi işlediğimiz zamanı kaydetmek için kullanılır
count = 0
while(cap.isOpened()):
ret, frame = cap.read()
if not ret: # video bittiyse veya Video Girişi yoksa
break
# fps = 1/(new_frame_time-prev_frame_time)# fps, belirli bir zaman aralığında işlenen kare sayısı olacaktır. çünkü çoğu zaman 0.001 saniyelik bir hata olacak
# prev_frame_time = new_frame_time
# fps = int(fps) # fps'yi tam sayıya dönüştürme
# fps = str(fps) # kare üzerinde görüntüleyebilmemiz için fps'yi dizeye dönüştürme
# cv2.putText(frame, fps, (7, 35), font, 1, (100, 255, 0), 3, cv2.LINE_AA) # putText işlevini kullanarak
#model.conf = 0.60
if count%30==0:
results = model(frame)
roi_list = []
boxes = []
plates = []
# Loop over all the bounding boxes
for _, det in enumerate(results.xyxy[0]):
# convert from tensor to numpy
box=det.detach().cpu().numpy()[:4]
# convert from float to integer
box=[int(x) for x in box]
x1,y1,x2,y2=box # crop the license plate image part
cropped = frame[y1:y2,x1:x2].copy()
label = "plate"
color=(0,255,255)
# draw a box on original image
cv2.rectangle(frame, (x1, y1), (x2, y2), (0,255,255), 2)
t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
plate_num=""
plate_num0=""
plate_num1=""
plate_num2=""
img2 = cropped.copy()
kernell = np.ones((5, 5), np.uint8)
img2 = cv2.resize(cropped, (0, 0),fx=5,fy=5,interpolation = cv2.INTER_CUBIC)
gray = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY) # togray
dst = cv2.GaussianBlur(gray, (5, 5), cv2.BORDER_DEFAULT) # gaussianblur
ret, thresh = cv2.threshold(dst, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) # Threshold
# img_dilation2 = cv2.erode(thresh2, kernell, iterations=1) # Dilate
# img_dilation = cv2.dilate(thresh, None, iterations=1) # Dilate
img_dilation=thresh
try:
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
except:
ret_img, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
sorted_contours = sorted(contours, key=lambda ctr: cv2.boundingRect(ctr)[0])
for cnt in sorted_contours:
x,y,w,h = cv2.boundingRect(cnt)
height, width = gray.shape
if height / float(h) > 5:
continue
ratio = h / float(w) #oran
crop_area = h * w
total_area=height*width
# Too small
if crop_area<100:
continue
if crop_area/total_area >0.20 or crop_area/total_area<0.01:
continue
# if width is not more than 25 pixels skip
if width / float(w) > 15: continue
pad=5
try:
if ((width / height) >= 2.5):
roi = thresh[y-pad:y+h+pad, x-pad:x+w+pad]
if roi is None:
continue
roi = cv2.bitwise_not(roi)
roi = cv2.medianBlur(roi, 3)
roi = cv2.bitwise_not(roi)
roi=cv2.dilate(roi,None,iterations=1)
roi_list.append(roi)
text = pytesseract.image_to_string(roi, config='-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ --psm 8 --oem 3')
plate_num0 += text
plate_num=plate_num0
plate_num=plate_num0.replace(" ", "").replace("\n","")
else:
if ((y >= 0) & (y <= height/2)):
roi = thresh[y - pad:y + h + pad, x - pad:x + w + pad]
roi = cv2.medianBlur(roi, 3)
roi_list.append(roi)
text1 = pytesseract.image_to_string(roi,
config='-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ --psm 8 --oem 3')
plate_num1 += text1
elif (y>=height/2):
roi = thresh[y - pad:y + h + pad, x - pad:x + w + pad]
roi = cv2.bitwise_not(roi)
roi = cv2.medianBlur(roi, 3)
roi = cv2.bitwise_not(roi)
roi = cv2.dilate(roi, None, iterations=1)
roi_list.append(roi)
text2 = pytesseract.image_to_string(roi,
config='-c tessedit_char_whitelist=0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ --psm 8 --oem 3')
plate_num2 += text2
plate_num = plate_num1 + plate_num2
#print(plate_num)
roi_list.append(roi)
except:
pass
clean_text2 = ""
for char in plate_num:
if char in "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ ":
clean_text2 += char
plate_num = clean_text2
plates.append(plate_num)
boxes.append(box)
if len(plate_num)>=7 :
db=Db_Handler()
p=PlateInfo()
p.plate_num=plate_num
db.insert(p)
print(plate_num)
else:
pass
#time.sleep(2.0)
count=count+1
drawn_frame=draw(boxes,plates,frame)
cv2.imshow("a",drawn_frame)
if cv2.waitKey(30) & 0xFF == ord('q'):
break
db.plate_list(p)
cap.release()
cv2.destroyAllWindows()