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Video_test2.py
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Video_test2.py
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import math
import cv2
import numpy as np
import Preprocess
ADAPTIVE_THRESH_BLOCK_SIZE = 19
ADAPTIVE_THRESH_WEIGHT = 9
Min_char_area = 0.015
Max_char_area = 0.06
Min_char = 0.01
Max_char = 0.09
Min_ratio_char = 0.25
Max_ratio_char = 0.7
max_size_plate = 18000
min_size_plate = 5000
RESIZED_IMAGE_WIDTH = 20
RESIZED_IMAGE_HEIGHT = 30
tongframe = 0
biensotimthay = 0
# Load KNN model
npaClassifications = np.loadtxt("classifications.txt", np.float32)
npaFlattenedImages = np.loadtxt("flattened_images.txt", np.float32)
npaClassifications = npaClassifications.reshape(
(npaClassifications.size, 1)) # reshape numpy array to 1d, necessary to pass to call to train
kNearest = cv2.ml.KNearest_create() # instantiate KNN object
kNearest.train(npaFlattenedImages, cv2.ml.ROW_SAMPLE, npaClassifications)
# Read video
cap = cv2.VideoCapture('data/video/haihang.mp4')
while (cap.isOpened()):
# Image preprocessing
ret, img = cap.read()
tongframe = tongframe + 1
# img = cv2.resize(img, None, fx=0.5, fy=0.5)
imgGrayscaleplate, imgThreshplate = Preprocess.preprocess(img)
canny_image = cv2.Canny(imgThreshplate, 250, 255) # Canny Edge
kernel = np.ones((3, 3), np.uint8)
dilated_image = cv2.dilate(canny_image, kernel,iterations=1) # Dilation
# Filter out license plates
contours, hierarchy = cv2.findContours(dilated_image, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
contours = sorted(contours, key=cv2.contourArea, reverse=True)[:10] # Pick out 10 biggest contours
screenCnt = []
for c in contours:
peri = cv2.arcLength(c, True) # Tính chu vi
approx = cv2.approxPolyDP(c, 0.06 * peri, True) # Approximate the edges of contours
[x, y, w, h] = cv2.boundingRect(approx.copy())
ratio = w / h
if (len(approx) == 4) and (0.8 <= ratio <= 1.5 or 4.5 <= ratio <= 6.5):
screenCnt.append(approx)
if screenCnt is None:
detected = 0
print("No plate detected")
else:
detected = 1
if detected == 1:
n = 1
for screenCnt in screenCnt:
################## Find the angle of the license plate ###############
(x1, y1) = screenCnt[0, 0]
(x2, y2) = screenCnt[1, 0]
(x3, y3) = screenCnt[2, 0]
(x4, y4) = screenCnt[3, 0]
array = [[x1, y1], [x2, y2], [x3, y3], [x4, y4]]
sorted_array = array.sort(reverse=True, key=lambda x: x[1])
(x1, y1) = array[0]
(x2, y2) = array[1]
doi = abs(y1 - y2)
ke = abs(x1 - x2)
angle = math.atan(doi / ke) * (180.0 / math.pi)
#################################################
# Masking the part other than the number plate
mask = np.zeros(imgGrayscaleplate.shape, np.uint8)
new_image = cv2.drawContours(mask, [screenCnt], 0, 255, -1, )
# Now crop
(x, y) = np.where(mask == 255)
(topx, topy) = (np.min(x), np.min(y))
(bottomx, bottomy) = (np.max(x), np.max(y))
roi = img[topx:bottomx + 1, topy:bottomy + 1]
imgThresh = imgThreshplate[topx:bottomx + 1, topy:bottomy + 1]
ptPlateCenter = (bottomx - topx) / 2, (bottomy - topy) / 2
if x1 < x2:
rotationMatrix = cv2.getRotationMatrix2D(ptPlateCenter, -angle, 1.0)
else:
rotationMatrix = cv2.getRotationMatrix2D(ptPlateCenter, angle, 1.0)
roi = cv2.warpAffine(roi, rotationMatrix, (bottomy - topy, bottomx - topx))
imgThresh = cv2.warpAffine(imgThresh, rotationMatrix, (bottomy - topy, bottomx - topx))
roi = cv2.resize(roi, (0, 0), fx=3, fy=3)
imgThresh = cv2.resize(imgThresh, (0, 0), fx=3, fy=3)
# License Plate preprocessing
kerel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
thre_mor = cv2.morphologyEx(imgThresh, cv2.MORPH_DILATE, kerel3)
cont, hier = cv2.findContours(thre_mor, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# Character segmentation
char_x_ind = {}
char_x = []
height, width, _ = roi.shape
roiarea = height * width
# print ("roiarea",roiarea)
for ind, cnt in enumerate(cont):
area = cv2.contourArea(cnt)
(x, y, w, h) = cv2.boundingRect(cont[ind])
ratiochar = w / h
if (Min_char * roiarea < area < Max_char * roiarea) and (0.25 < ratiochar < 0.7):
if x in char_x: # Sử dụng để dù cho trùng x vẫn vẽ được
x = x + 1
char_x.append(x)
char_x_ind[x] = ind
# Character recognition
if len(char_x) in range(7, 10):
cv2.drawContours(img, [screenCnt], -1, (0, 255, 0), 3)
char_x = sorted(char_x)
strFinalString = ""
first_line = ""
second_line = ""
for i in char_x:
(x, y, w, h) = cv2.boundingRect(cont[char_x_ind[i]])
cv2.rectangle(roi, (x, y), (x + w, y + h), (0, 255, 0), 2)
imgROI = thre_mor[y:y + h, x:x + w] # crop characters
imgROIResized = cv2.resize(imgROI,
(RESIZED_IMAGE_WIDTH, RESIZED_IMAGE_HEIGHT)) # resize
npaROIResized = imgROIResized.reshape(
(1, RESIZED_IMAGE_WIDTH * RESIZED_IMAGE_HEIGHT)) # đưa hình ảnh về mảng 1 chiều
# cHUYỂN ảnh thành ma trận có 1 hàng và số cột là tổng số điểm ảnh trong đó
npaROIResized = np.float32(npaROIResized) # chuyển mảng về dạng float
_, npaResults, neigh_resp, dists = kNearest.findNearest(npaROIResized,k=3) # call KNN function find_nearest; neigh_resp là hàng xóm
strCurrentChar = str(chr(int(npaResults[0][0]))) # ASCII of the character
cv2.putText(roi, strCurrentChar, (x, y + 50), cv2.FONT_HERSHEY_DUPLEX, 2, (0, 255, 255), 3)
if (y < height / 3): # decide 1 or 2-line license plate
first_line = first_line + strCurrentChar
else:
second_line = second_line + strCurrentChar
strFinalString = first_line + second_line
print("\n License Plate " + str(n) + " is: " + first_line + " - " + second_line + "\n")
cv2.putText(img, strFinalString, (topy, topx), cv2.FONT_HERSHEY_DUPLEX, 1, (0, 255, 255), 1)
n = n + 1
biensotimthay = biensotimthay + 1
cv2.imshow("a", cv2.cvtColor(roi, cv2.COLOR_BGR2RGB))
imgcopy = cv2.resize(img, None, fx=0.5, fy=0.5)
cv2.imshow('License plate', imgcopy)
print("number of plates found", biensotimthay)
print("total frame", tongframe)
print("plate found rate:", 100 * biensotimthay / (368), "%")
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()