def Predict(self, ic, option=0):
self.LoadData()
self.CreateKNN()
Target = ic
original = Target.img.copy()
process = Target.BinaryProcess()
final, contours = Target.Draw()
NewContours = []
for cnt in contours:
[x, y, w, h] = cv2.boundingRect(cnt)
cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2)
TargetPxs = process[y:y + h, x:x + w]
TargetPxsSmall = cv2.resize(TargetPxs, (10, 10))
sample = TargetPxsSmall.reshape((1, 100))
sample = np.float32(sample)
retval, results, neigh_resp, dists = self.model.findNearest(sample,
k=3)
if (results == 1):
NewContours.append(cnt)
pic = np.zeros((450, 600, 3)).astype(np.uint8)
cv2.drawContours(pic, NewContours, -1, (255, 255, 255), 2)
if (option == 1):
ip.CvShow('Classification', pic)
return pic, NewContours
def Predict(self, ic: ImgClass, contours):
self.LoadData()
self.CreateKNN()
Target = ic
original = Target.img.copy()
process = Target.BinaryProcess()
out = np.zeros(Target.img.shape, np.uint8)
for cnt in contours:
if cv2.contourArea(cnt) > 50:
[x, y, w, h] = cv2.boundingRect(cnt)
cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2)
TargetPxs = process[y:y + h, x:x + w]
TargetPxsSmall = cv2.resize(TargetPxs, (10, 10))
sample = TargetPxsSmall.reshape((1, 100))
sample = np.float32(sample)
retval, results, neigh_resp, dists = self.model.findNearest(
sample, k=1)
string = self.Code.deCode(results[0][0])
cv2.putText(out, string, (x, y + h), 0, 1, (0, 255, 0))
ip.CvShow('Out', out)
def __init__(self,ic:ImgClass):
self.original=ic.img.copy()
img = ic.img.copy()
self.resize=cv2.resize(img,(800,600))
bp = ip.Binarization(self.resize,80)
p,contours, heirs = cv2.findContours(bp,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
pic = np.zeros(self.resize.shape).astype(np.uint8)
cv2.drawContours(pic,contours,-1,(0,0,255),2)
ip.CvShow(pic)
def deBug(self, x):
im = Image.open('0.jpg')
img = ImageTk.PhotoImage(image=im)
cvim = cv2.imread('5799.jpg')
ip.CvShow('5799', cvim)
return img
print(self.CurrentFile)
def Url2ic(self):
if self.url_src.get():
url = self.url_src.get()
cap = cv2.VideoCapture(url)
if (cap.isOpened()):
ret, img = cap.read()
if ret:
self.ic = ImgClass('')
self.ic.FromUrlSrc(img)
self.classfy = Classification(self.ic, self)
self.program = Mainsys(self.ic, self)
ip.CvShow('N', img)
def __init__(self):
r = requests.get(self.url)
self.r = r
if r.status_code == requests.codes.ok:
# 以 BeautifulSoup 解析 HTML 程式碼
soup = BeautifulSoup(r.text, 'html.parser')
# 以 CSS 的 class 抓出各類
#print(soup)
imgs = soup.find_all('img')
print(len(imgs))
for img in imgs:
c = img.get('alt')
print(c)
urlsrc = img.get('src')
print(urlsrc)
cap = cv2.VideoCapture(urlsrc)
if (cap.isOpened()):
ret, img = cap.read()
if ret:
ip.CvShow('N', img)
else:
print('Wrong Request')
def Trainning(self):
Main = self.Main
Main.SetConsole('Train!')
Target = self.ic
original = Target.img.copy()
process = Target.BinaryProcess()
final, contours = Target.Draw()
#area = ip.MappingCnt(contours)
if os.path.isfile('CarLicenseSample.data') and os.path.isfile(
'CarLicenseRes.data'):
print('LoadData')
Main.SetConsole('LoadData!')
samples = np.loadtxt('CarLicenseSample.data', np.float32)
responses = np.loadtxt('CarLicenseRes.data', np.float32)
else:
print('NewData')
Main.SetConsole('NewData!')
samples = np.empty((0, 100))
responses = []
keys = [i for i in range(48, 50)]
close = 0
for cnt in contours:
original = Target.img.copy()
[x, y, w, h] = cv2.boundingRect(cnt)
cv2.rectangle(original, (x, y), (x + w, y + h), (0, 0, 255), 2)
TargetPxs = process[y:y + h, x:x + w]
TargetPxsSmall = cv2.resize(TargetPxs, (10, 10))
cv2.imshow('norm', original)
while close == 0:
key = cv2.waitKey(100)
if key != -1:
print(key)
if cv2.getWindowProperty('norm', 0) == -1:
print('Close')
Main.SetConsole('Close!')
close = 1
if key == 27: # (escape to quit)
cv2.destroyAllWindows()
close = 1
print('Esc')
Main.SetConsole('Esc!')
elif key == 32:
print('Skip')
Main.SetConsole('Skip!')
elif key == ord('s'):
ip.CvShow('ic.img', Target.img)
Main.SetConsole('Show!')
elif key in keys:
responses = np.append(responses, int(chr(key)))
sample = TargetPxsSmall.reshape((1, 100))
samples = np.append(samples, sample, 0)
Main.SetConsole('Add Sample!')
break
if close == 1:
return 0
cv2.destroyAllWindows()
responses = np.array(responses, np.float32)
responses = responses.reshape((responses.size, 1))
print('training complete')
Main.SetConsole('訓練完成，進行存檔!')
cv2.destroyAllWindows()
np.savetxt('CarLicenseSample.data', samples)
np.savetxt('CarLicenseRes.data', responses)
return 1
def GetContours(self):
if self.ic:
pic, contours = self.ic.Draw()
ip.CvShow(self.CurrentFile, pic)
def BinaryProcess(self):
if self.ic:
ip.CvShow(self.CurrentFile, self.ic.BinaryProcess())
