def string2txt(image, string):
roi = String.extractROI(image, string, False)
bgr = roi.cropped
gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)
laplacian = Laplacian(gray)
lthr = cv2.adaptiveThreshold(laplacian, 255.0,
cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
cv2.THRESH_BINARY_INV,
ADAPTIVE_THRESH_BLOCK_SIZE,
ADAPTIVE_THRESH_WEIGHT)
dog = DoG(gray)
compose = maskize(lthr, dog)
###
img_contour, contours, npaHierarchy = cv2.findContours(
compose, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(img_contour, contours, -1, SCALAR_WHITE, -1)
chars = find_possible_chars(contours, False)
chars = innerfiltering(chars)
chars.sort(key=lambda char: char.brX)
img_char = drawChars(gray.shape, "size-filtering", chars)
compose = maskize(img_contour, 255 - img_char)
#showResult("compose",compose)
### OCR
ocrresult, count = string.ocr2(compose, chars)
string.meaningfulcharcount = count
return ocrresult
def makeCompose(self):
self.gray = cv2.cvtColor(self.resultimg, cv2.COLOR_BGR2GRAY)
if self.mode == "Yellow":
self.gray = 255 - self.gray
# DOG
equalized_image = cv2.equalizeHist(self.gray)
self.DoG = DoG(equalized_image) #Difference of Gaussians
self.compose = maskize(self.thr, self.DoG)
def preprocess(self, mode="Blue"):
gray = cv2.cvtColor(self.bgr, cv2.COLOR_BGR2GRAY)
if mode == "Blue":
self.mode = "Blue"
self.gray = gray #iicontrast(gray)#contrast(gray)#equalized_image = cv2.equalizeHist(self.gray)
else:
self.mode = "Yellow"
self.gray = 255 - gray
self.DoG = DoG(self.gray) #Difference of Gaussians
self.laplacian = Laplacian(self.gray, needcontrast=False)
self.thr = AdaptiveThreshold(self.laplacian)
self.thr = maskize(self.thr, self.DoG)
def preprocess(self, image):
self.bgr = image
gray = cv2.cvtColor(self.bgr, cv2.COLOR_BGR2GRAY)
self.gray = icontrast(255 - gray)
self.laplacian = Laplacian(gray)
#self.sobel = Sobel(gray)
#self.entropy = Entropy(gray)
#self.garbor = Garbor(gray)
self.DoG = DoG(gray)
self.lthr = AdaptiveThreshold(self.laplacian)
self.tophat = tophatmask(gray)
#tophatblackhat(gray)
masks = []
#masks.append(self.tophat)
masks.append(self.DoG)
self.compose = maskize(self.lthr, masks)
self.contour = np.zeros((self.compose.shape[:2]), np.uint8)