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test1img_edited.py
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test1img_edited.py
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from ctypes import *
import math
import random
import os
import cv2
import numpy as np
import time
import darknet
import imutils
# Ham sap xep contour tu trai sang phai
def sort_contours(cnts):
reverse = False
i = 1
boundingBoxes = [cv2.boundingRect(c) for c in cnts]
(cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),
key=lambda b: b[1][i], reverse=reverse))
return cnts
def get_contour_precedence(contour, cols):
tolerance_factor = 10
origin = cv2.boundingRect(contour)
return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]
# Dinh nghia cac ky tu tren bien so
char_list = '0123456789ABCDEFGHKLMNPRSTUVXYZ'
def convertBack(x, y, w, h):
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
# Ham fine tune bien so, loai bo cac ki tu khong hop ly
def fine_tune(lp):
newString = ""
for i in range(len(lp)):
if lp[i] in char_list:
newString += lp[i]
# else:
# newString += '9'
return newString
def cvDrawBoxes(detections, img):
for detection in detections:
x, y, w, h = detection[2][0],\
detection[2][1],\
detection[2][2],\
detection[2][3]
xmin, ymin, xmax, ymax = convertBack(
float(x), float(y), float(w), float(h))
pt1 = (xmin, ymin)
pt2 = (xmax, ymax)
cv2.rectangle(img, pt1, pt2, (0, 255, 0), 1)
image1 = img[ymin:ymax,xmin:xmax]
if image1.size !=0:
digit_w = 30 # Kich thuoc ki tu
digit_h = 60 # Kich thuoc ki tu
image1 = cv2.resize(image1, (300,200), 1)
roi = image1
model_svm = cv2.ml.SVM_load('svm.xml')
#continue
gray = cv2.cvtColor(image1, cv2.COLOR_BGR2GRAY)
#cv2.imshow('Gray', gray)
#binary=cv2.threshold(gray, 127, 255,
#cv2.THRESH_BINARY_INV)[1]
binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 115, 1)
cv2.imshow('Binary', binary)
kernel3 = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))
thre_mor = cv2.morphologyEx(binary, cv2.MORPH_DILATE, kernel3)
thre_mor, cont, hierarchy= cv2.findContours(thre_mor, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cont)
cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
cnts = sort_contours(cnts)
#sorted_cont = sorted(cont, key=lambda ctr: cv2.boundingRect(ctr)[0]* image1.shape[0]+ cv2.boundingRect(ctr)[1] * image1.shape[1],reverse=False )
#sorted_cont = sorted(cont, key=lambda ctr: cv2.contourArea(ctr),reverse=False )
#sorted_cont = sorted(cont, key=lambda ctr:get_contour_precedence(ctr, image1.shape[1]))
plate_info = ""
for i, ctr in enumerate(sorted_cont):
x, y, w, h = cv2.boundingRect(ctr)
ratio = h/w
if 1.5<=ratio<=3.5: # Chon cac contour dam bao ve ratio w/h
if 0.25<=h/roi.shape[0]<=0.8:
# Ve khung chu nhat quanh so
cv2.rectangle(roi, (x, y), (x + w, y + h), (0, 255, 0), 2)
# Tach so va predict
curr_num = thre_mor[y:y+h,x:x+w]
curr_num = cv2.resize(curr_num, dsize=(digit_w, digit_h))
_, curr_num = cv2.threshold(curr_num, 30, 255, cv2.THRESH_BINARY)
curr_num = np.array(curr_num,dtype=np.float32)
curr_num = curr_num.reshape(-1, digit_w * digit_h)
# Dua vao model SVM
result = model_svm.predict(curr_num)[1]
result = int(result[0, 0])
if result<9: # Neu la so thi hien thi luon
result = str(result)
else: #Neu la chu thi chuyen bang ASCII
result = chr(result)
plate_info +=result
cv2.imshow("Cac contour tim duoc", roi)
print("Bien so=", fine_tune(plate_info))
cv2.putText(img,
" [" + plate_info + "]",
(pt1[0], pt1[1] - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
[25, 25, 225], 2)
else:
continue
return img
# Đường dẫn ảnh
img_path = "img1.jpg"
netMain = None
metaMain = None
altNames = None
configPath = "./LP/yolov3-tiny_obj.cfg"
weightPath = "./yolov3-tiny_obj_4000.weights"
metaPath = "./LP/LP.data"
if not os.path.exists(configPath):
raise ValueError("Invalid config path `" +
os.path.abspath(configPath)+"`")
if not os.path.exists(weightPath):
raise ValueError("Invalid weight path `" +
os.path.abspath(weightPath)+"`")
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `" +
os.path.abspath(metaPath)+"`")
if netMain is None:
netMain = darknet.load_net_custom(configPath.encode(
"ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = darknet.load_meta(metaPath.encode("ascii"))
if altNames is None:
try:
with open(metaPath) as metaFH:
metaContents = metaFH.read()
import re
match = re.search("names *= *(.*)$", metaContents,
re.IGNORECASE | re.MULTILINE)
if match:
result = match.group(1)
else:
result = None
try:
if os.path.exists(result):
with open(result) as namesFH:
namesList = namesFH.read().strip().split("\n")
altNames = [x.strip() for x in namesList]
except TypeError:
pass
except Exception:
pass
darknet_image = darknet.make_image(darknet.network_width(netMain),
darknet.network_height(netMain),3)
frame_read = cv2.imread(img_path)
frame_rgb = cv2.cvtColor(frame_read, cv2.COLOR_BGR2RGB)
frame_resized = cv2.resize(frame_rgb,
(darknet.network_width(netMain),
darknet.network_height(netMain)),
interpolation=cv2.INTER_LINEAR)
darknet.copy_image_from_bytes(darknet_image,frame_resized.tobytes())
detections = darknet.detect_image(netMain, metaMain, darknet_image, thresh=0.25)
image = cvDrawBoxes(detections, frame_resized)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
cv2.imshow('Demo', image)
cv2.waitKey()
cv2.destroyAllWindows()