ocr图像预处理

说明：文字方向校正(fft方式和放射变换方式)参考了网上的代码，只做了少量修改
只针对医疗影像图像，自然场景下的另说
因为处理的图像都很大很大，居然有11000*12000这种分辨率的，有90M大小，我也是醉了，绝大部分都是6000左右分辨率的图像，这种图像直接送到CTPN里的话，效果不是太好，太大了 而且效率感人，所以必须做一下预处理。大部分的X光图像很简单，直接缩放送CTPN即可，而CT和MRI图像虽然一张上有很多小图像，但好在要么有虚线分割要么中间都会留有空白的地方，于是就可以利用直线检测和投影检测来把图片分割成若干小图像了。(吐槽一下之前老外写的代码，不管三七二十一把所有图像都是分成上下两部分，然后上下再各分成上下两部分，四个部分再分别循环他的N个算法，搞的整个系统70%以上的资源都在跑OCR，一张很简单的图片最低也要几分钟才能出结果，复杂一点的都是10几分钟 真想知道这是怎么过验收的！)

1. 图像分割，思想很简单 有虚线的直接做直线检测，有空白的做X、Y轴的投影，都没有的就是X光图像了，直接把整张图像当做ROI送CTPN

def _img_split_with_hough(img, min=100, max=220):    """    :param img: 读入的二值化图    :param min: 边缘检测阈值    :param max: 边缘检测阈值    :return: 水平和垂直线的坐标集合    """    h = img.shape[0]    w = img.shape[1]    edges = cv2.Canny(img, min, max)    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 30, minLineLength=100, maxLineGap=10)    lines1 = lines[:, 0, :]    h_line = []    v_line = []    for x1, y1, x2, y2 in lines1[:]:        if y2 == y1:            flag = False            for element in h_line:                if abs(element[1] - y1) < 10:                    flag = True                    break            if flag == False and abs(x1 - x2) > w * 0.5:                h_line.append((x1, y1, x2, y2))        elif x1 == x2:            flag = False            for element in v_line:                if abs(element[0] - x1) < 10:                    flag = True                    break            if flag == False and abs(y1 - y2) > h * 0.5:                v_line.append((x1, y1, x2, y2))    return h_line, v_line    def _img_split_with_shadow(gray_img, threshold_value=180):    """    :param binary_img: 读入的灰度图    :param img_show:    :return: 水平和垂直线的坐标集合    """    h = gray_img.shape[0]    w = gray_img.shape[1]    # 按行求和    sum_x = np.sum(gray_img, axis=1)    # 按列求和    sum_y = np.sum(gray_img, axis=0)    h_line_index = np.argwhere(sum_x < 10)    v_line_index = np.argwhere(sum_y < 10)    h_line_index = np.reshape(h_line_index, (h_line_index.shape[0],))    v_line_index = np.reshape(v_line_index, (v_line_index.shape[0],))    h_line = [(0, h_line_index[0], w - 1, h_line_index[0]), (0, h_line_index[-1], w - 1, h_line_index[-1])] if len(        h_line_index) > 0 else []    v_line = [(v_line_index[0], 0, v_line_index[0], h - 1), (v_line_index[-1], 0, v_line_index[-1], h - 1)] if len(        v_line_index) > 0 else []    for i in range(len(h_line_index) - 1):        if h_line_index[i + 1] - h_line_index[i] > 2:            h_line.append((0, h_line_index[i], w - 1, h_line_index[i]))    for i in range(len(v_line_index) - 1):        if v_line_index[i + 1] - v_line_index[i] > 2:            v_line.append((v_line_index[i], 0, v_line_index[i], h - 1))    return h_line, v_linedef _combine_rect(h_lines, v_lines, w, h):    rects = []    # 添加第一行(列)和最后一行(列)    x_axis = sorted(set([0, w - 1] + [item[0] for item in v_lines]))    y_axis = sorted(set([0, h - 1] + [item[1] for item in h_lines]))    point_list = []    for y in y_axis:        point = []        for x in x_axis:            point.append((y, x))        point_list.append(point)    for y_index in range(len(y_axis) - 1):        if y_axis[y_index + 1] - y_axis[y_index] <= 10:            continue        for x_index in range(len(x_axis) - 1):            if x_axis[x_index + 1] - x_axis[x_index] <= 10:                continue            rects.append((y_axis[y_index], x_axis[x_index],                          y_axis[y_index + 1], x_axis[x_index + 1]))    return rectsdef img_split(img_file, threshold_value=180, img_show=False):    """    :param img_file: 输入图片路径    :param img_show: 是否显示    :return: 分割后的子图像rect列表    """    img = cv2.imread(img_file, 1)    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)    gray = color_nomal(gray)    # ret, binary = cv2.threshold(gray, threshold_value, 255, cv2.THRESH_BINARY)    h = img.shape[0]    w = img.shape[1]    rate = h // w if h > w else w // h    h_line, v_line = _img_split_with_shadow(gray)    if len(h_line) < 1 and len(v_line) < 1:        h_line, v_line = _img_split_with_hough(gray)    rects = _combine_rect(h_line, v_line, w, h)    split_imgs = []    for rect in rects:        split_imgs.append(img[rect[0]:rect[2], rect[1]:rect[3]])    if img_show:        for rect in rects:            cv2.rectangle(img, (rect[1], rect[0]), (rect[3], rect[2]), (0, 255, 0), 2)                img = cv2.resize(img, (int(h * 0.7), int(h * 0.7 / rate)))        cv2.imshow('cece', img)        cv2.waitKey()    return split_imgs

分割结果

2. 文字方向校正，可以使用FFT变换后校正然后再逆变换回来，也可以直接使用查找包含文字区域的矩形，旋转这个矩形，但是这种方法对于垂直的图像就没效果了，因为会发现包含文字的矩形区域就是方方正正的 不用校正。在二值化的时候采用了自适应二值化，这样做的好处是能更精确的定位文字区域，全局二值化可能会造成有些地方一团黑。

def rotated_img_with_fft(gray):    # 图像延扩    h, w = gray.shape[:2]    new_h = cv2.getOptimalDFTSize(h)    new_w = cv2.getOptimalDFTSize(w)    right = new_w - w    bottom = new_h - h    nimg = cv2.copyMakeBorder(gray, 0, bottom, 0, right, borderType=cv2.BORDER_CONSTANT, value=0)    # 执行傅里叶变换，并过得频域图像    f = np.fft.fft2(nimg)    fshift = np.fft.fftshift(f)    fft_img = np.log(np.abs(fshift))    fft_img = (fft_img - np.amin(fft_img)) / (np.amax(fft_img) - np.amin(fft_img))    fft_img *= 255    ret, thresh = cv2.threshold(fft_img, 150, 255, cv2.THRESH_BINARY)    # 霍夫直线变换    thresh = thresh.astype(np.uint8)    lines = cv2.HoughLinesP(thresh, 1, np.pi / 180, 30, minLineLength=40, maxLineGap=100)    try:        lines1 = lines[:, 0, :]    except Exception as e:        lines1 = []    # 创建一个新图像，标注直线    # lineimg = np.ones(nimg.shape,dtype=np.uint8)    # lineimg = lineimg * 255    piThresh = np.pi / 180    pi2 = np.pi / 2    angle = 0    for line in lines1:        # x1, y1, x2, y2 = line[0]        x1, y1, x2, y2 = line        # cv2.line(lineimg, (x1, y1), (x2, y2), (0, 255, 0), 2)        if x2 - x1 == 0:            continue        else:            theta = (y2 - y1) / (x2 - x1)        if abs(theta) < piThresh or abs(theta - pi2) < piThresh:            continue        else:            angle = abs(theta)            break        angle = math.atan(angle)    angle = angle * (180 / np.pi)    print(angle)    # cv2.imshow("line image", lineimg)    center = (w // 2, h // 2)    height_1 = int(w * fabs(sin(radians(angle))) + h * fabs(cos(radians(angle))))    width_1 = int(h * fabs(sin(radians(angle))) + w * fabs(cos(radians(angle))))    M = cv2.getRotationMatrix2D(center, angle, 1.0)    M[0, 2] += (width_1 - w) / 2    M[1, 2] += (height_1 - h) / 2    rotated = cv2.warpAffine(gray, M, (width_1, height_1), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)    cv2.imshow('rotated', rotated)    cv2.waitKey(0)    return rotateddef rotated_img_with_radiation(gray, is_show=False):    thresh = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV, 11, 2)    if is_show:        cv2.imshow('thresh', thresh)    # 计算包含了旋转文本的最小边框    coords = np.column_stack(np.where(thresh > 0))    # 该函数给出包含着整个文字区域矩形边框，这个边框的旋转角度和图中文本的旋转角度一致    angle = cv2.minAreaRect(coords)[-1]    print(angle)    # 调整角度    if angle < -45:        angle = -(90 + angle)    else:        angle = -angle    # 仿射变换    h, w = gray.shape[:2]    center = (w // 2, h // 2)    print(angle)    M = cv2.getRotationMatrix2D(center, angle, 1.0)    rotated = cv2.warpAffine(gray, M, (w, h), flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)    if is_show:                cv2.putText(rotated, 'Angle: {:.2f} degrees'.format(angle), (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7,                    (0, 0, 255), 2)        print('[INFO] angel :{:.3f}'.format(angle))        cv2.imshow('Rotated', rotated)        cv2.waitKey()    return rotated

放射校正结果：

原图：

放射校正：

fft校正，计算的时候有大概2度的误差