RAU6 Dokuwiki

gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY) 
blur = cv2.GaussianBlur(gray, (5, 5), 0) 
thresh = cv2.adaptiveThreshold(blur, 255, 1, 1, 11, 2)

In result we get:

image, contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for cnt in contours:
    if cv2.contourArea(cnt) > 20:
        [x, y, w, h] = cv2.boundingRect(cnt)
        if h > 10:
            im = copy.copy(im_copy)
            cv2.rectangle(im, (x, y), (x + w, y + h), (0, 0, 255), 2)
            roi = thresh[y:y + h, x:x + w]
            roismall = cv2.resize(roi, (20, 20))

In result we get 20×20 picture:

np.savetxt('generalsamples.data', samples)
np.savetxt('generalresponses.data', responses)

self.samples = np.loadtxt("generalsamples.data", np.float32)
self.responses = np.loadtxt("generalresponses.data", np.float32)
self.model = cv2.ml.KNearest_create()
self.model.train(self.samples, cv2.ml.ROW_SAMPLE, self.responses)

ef extract_lines(self, image):
    line_list = {}
    # dilation
    cv2.imshow('input extract_line', image)
    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (10, 1))
    img_dilation = cv2.dilate(image, kernel, iterations=3)
    # find contours
    image, contours, _ = cv2.findContours(img_dilation.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    for cnt in contours:
        [x, y, w, h] = cv2.boundingRect(cnt)
        # ic contour is big enough
        #print w * h
        if w * h > 5000: # wszystko co jest poniżej 5000 ignorujemy
            line_list[y] = [x, y, w, h]
    # sort contours
    print "Liczba Linii %s" % len(line_list)
    if len(line_list) == 3:
        return sorted(line_list.items())
    else:
        print "Read Error, Detected Lines Count Error ! -> %s" % len(line_list)
        return None

def decode(self, image):
    if len(image) > 0 and image is not None:
        contours = self.extract_lines(image)
        MZR_lines = []
        if contours is not None:
            for key, value in contours:
                # Get bounding box
                x, y, w, h = value
                line_image = image[y:y + h, x:x + w]
                MZR_lines.append(self.ocr(line_image))
                cv2.imshow("linie", line_image)
            return MZR_lines
    return None

def extract_chars_from_line(self, image):
    image, contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    char_list = []
    i = 0
    lista_rect = {}
    for cnt in contours:
        [x, y, w, h] = cv2.boundingRect(cnt)
        if cv2.contourArea(cnt) > 10:
            char_list.append([x, y, w, h])
            lista_rect[x] = [x, y, w, h]
    return sorted(lista_rect.items())

Następnie na podstawie zwróconych parametrów znaku funkcja OCR wycina obrazek, skaluje do rozmiaru 20×20 i przekazuje do Sieci KNN jako dane wejściowe. Jako informacje zwrotną sieć KNN zwraca kod rozpoznanego znaku.

def validate(self, read_data):
    print "Read Data: %s" % read_data
    print "Test Data: %s" % self.test_lines
    read_line_count = len(read_data)
    test_line_count = len(self.test_lines)
    PASS_COUNT = 0;
    FAIL_COUNT = 0;
    if (test_line_count == read_line_count):
        for i in xrange(read_line_count):
            read_line_data = read_data[i]
            test_line_data = self.test_lines[i]
            if (len(read_line_data) == len(test_line_data)):
                for x in xrange(len(read_line_data)):
                    if read_line_data[x] == test_line_data[x]:
                        PASS_COUNT += 1
                    else:
                        FAIL_COUNT += 1
            else:
                return None
    else:
        return None
    print "Validation Summary: PASS: %s, FAIL: %s" % (PASS_COUNT, FAIL_COUNT)
    Accuracy = float(float(PASS_COUNT) / float(PASS_COUNT + FAIL_COUNT)) * 100
    return Accuracy

def validate(self, read_data):
    print "Read Data: %s" % read_data
    print "Test Data: %s" % self.test_lines
    read_line_count = len(read_data)
    test_line_count = len(self.test_lines)
    PASS_COUNT = 0;
    FAIL_COUNT = 0;
    if (test_line_count == read_line_count):
        for i in xrange(read_line_count):
            read_line_data = read_data[i]
            test_line_data = self.test_lines[i]
            if (len(read_line_data) == len(test_line_data)):
                for x in xrange(len(read_line_data)):
                    if read_line_data[x] == test_line_data[x]:
                        PASS_COUNT += 1
                    else:
                        FAIL_COUNT += 1
            else:
                return None
    else:
        return None
    print "Validation Summary: PASS: %s, FAIL: %s" % (PASS_COUNT, FAIL_COUNT)
    Accuracy = float(float(PASS_COUNT) / float(PASS_COUNT + FAIL_COUNT)) * 100
    return Accuracy

def capture_process(self, cam):
    cap = cv2.VideoCapture(cam)
    while (True):
        ret, frame = cap.read()
        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        cv2.imshow('Camera data input', gray)
        key = cv2.waitKey(1)
        if chr(key & 255) == ' ':
            thresh = self.filtr(gray)
            text = self.locate_MZR(thresh, gray)
            print text
 
        if chr(key & 255) == 'q':
            break

def filtr(self, image):
    rectKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (13, 5))
    sqKernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 21))
    # load the image, resize it, and convert it to grayscal
    # smooth the image using a 3x3 Gaussian, then apply the blackhat
    # morphological operator to find dark regions on a light background
    gray = cv2.GaussianBlur(image, (5, 5), 0)
    blackhat = cv2.morphologyEx(gray, cv2.MORPH_BLACKHAT, rectKernel)
    # compute the Scharr gradient of the blackhat image and scale the
    # result into the range [0, 255]
    gradX = cv2.Sobel(blackhat, ddepth=cv2.CV_32F, dx=1, dy=0, ksize=-1)
    gradX = np.absolute(gradX)
    (minVal, maxVal) = (np.min(gradX), np.max(gradX))
    gradX = (255 * ((gradX - minVal) / (maxVal - minVal))).astype("uint8")
    # apply a closing operation using the rectangular kernel to close
    # gaps in between letters -- then apply Otsu's thresholding method
    gradX = cv2.morphologyEx(gradX, cv2.MORPH_CLOSE, rectKernel)
    thresh = cv2.threshold(gradX, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
    # perform another closing operation, this time using the square
    # kernel to close gaps between lines of the MRZ, then perform a
    # series of erosions to break apart connected components
    thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, sqKernel)
    thresh = cv2.erode(thresh, None, iterations=4)
    # during thresholding, it's possible that border pixels were
    # included in the thresholding, so let's set 5% of the left and
    # right borders to zero
    p = int(image.shape[1] * 0.05)
    thresh[:, 0:p] = 0
    thresh[:, image.shape[1] - p:] = 0
    return thresh    

Wynik działania funkc: Na miało zaznaczone są zlokalizowane obszary tekstu: następnie ten obraz przekazujemy do funkcji locate_MRZ, która rzyjmuje że największy „bialy” obszar jest obszarem MRZ i na tej podstawie wycina dane ze zdjęcia oryginalnego (celowo zamaskowano nr dowodu) :

def locate_MRZ(self, _thresh, _oryginal):
    _oryginal = cv2.adaptiveThreshold(_oryginal, 255, 1, 1, 11, 2)
 
    cnts = cv2.findContours(_thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
    cnts = sorted(cnts, key=cv2.contourArea, reverse=True)
    if len(cnts) > 0:
        (x, y, w, h) = cv2.boundingRect(cnts[0])
        # Shape[1] jest najwiekszy zatem to zawsze bedzie MZR
        pX = int((x + w) * 0.03)
        pY = int((y + h) * 0.03)
        (x, y) = (x - pX, y - pY)
        (w, h) = (w + (pX * 2), h + (pY * 2))
        result = _oryginal[y:y + h, x:x + w].copy()
        return self.decode(result) 

Wynik dziłania funkcji locate_MRZ:

def decode(self, image):
    if len(image) > 0 and image is not None:
        contours = self.extract_lines(image)
        MZR_lines = []
        if contours is not None:
            for key, value in contours:
                # Get bounding box
                x, y, w, h = value
                line_image = image[y:y + h, x:x + w]
                MZR_lines.append(self.ocr(line_image))
                cv2.imshow("linie", line_image)
            return MZR_lines
    return None

def extract_chars_from_line(self, image):
    image, contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    char_list = []
    i = 0
    lista_rect = {}
    for cnt in contours:
        [x, y, w, h] = cv2.boundingRect(cnt)
        if cv2.contourArea(cnt) > 10:
            char_list.append([x, y, w, h])
            lista_rect[x] = [x, y, w, h]
    return sorted(lista_rect.items())

Następnie na podstawie zwróconych parametrów znaku funkcja OCR wycina obrazek, skaluje do rozmiaru 20×20 i przekazuje do Sieci KNN jako dane wejściowe. Jako informacje zwrotną sieć KNN zwraca kod rozpoznanego znaku.

 def ocr(self, image):
    self.clear_folder('./log/ocr_img/')
    result = ""
    chars = self.extract_chars_from_line(image)
    i = 0
    for key, value in chars:
        x, y, w, h = value
        if h > 10:
            # cv2.rectangle(oryginal,(x,y),(x+w,y+h),(0,255,0),2)
            # cv2.imshow('oryginal', oryginal)
            roi = image[y:y + h, x:x + w]
            roismall = cv2.resize(roi, (20, 20))
            file_name = "./log/ocr_img/%s" % i
            self.save_image_to_png(roismall, file_name)
            roismall = roismall.reshape((1, 400))
            roismall = np.float32(roismall)
            retval, results, neigh_resp, dists = self.model.findNearest(roismall, k=1)
            string = str(chr((results[0][0])))
            result += (string)
    return result