opencv案例实战之工业印刷品数字识别_opencv工业应用实例

一、环境准备

• Python语言包
• OpenCV-python开发包
• OpenCV DNN模块
• OpenCV ML模块
• pycharm2019

项目地址：https://github.com/zxinyang38/opencv-

二、结果预览

从给定的印刷品图像进行数字识别。

三、实验步骤

1、EAST TEXT对象检测模型（使用EAST网络模型实现文字区域检测）

• EAST网络架构
  
• 加载获取网络各层信息
  east_text权重文件放在本人github地址：https://github.com/zxinyang38/opencv-

import cv2 as cv
import numpy as np

net = cv.dnn.readNet('C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/frozen_east_text_detection.pb')
names = net.getLayerNames()
for name in names:
    print(name)
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以上代码可以输出每一层的名字：其中feature_fusion/Conv_7/Sigmoid对应的是EAST网络中score map部分
feature_fusion/concat_3对应的是EAST网络架构中最右边的RBOX geometry部分

• 使用网络

   def detect(self,image):
       (H,W) = image.shape[:2]
       rH = H / float(320)
       rW = W / float(320)
       blob = cv.dnn.blobFromImage(image,1.0,(320,320),(123.68, 116.78, 103.94),swapRB=True,crop=False)
       self.net.setInput(blob)
       (scores, geometry) = self.net.forward(self.layerNames)
       print(scores)
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详见：text_area_detect.py函数

2、非最大抑制（NMS）

检测出来的图像可能时下图这种：

故而引入NMSBoxes API（非最大信号抑制去掉差的区域）:

import cv2 as cv
import numpy as np

class TextAreaDetector:
   def __init__(self,model_path):
       self.net = cv.dnn.readNet(model_path)
       names = self.net.getLayerNames()
       for name in names:
           print(name)
       self.threshold = 0.5
       self.layerNames = ["feature_fusion/Conv_7/Sigmoid","feature_fusion/concat_3"]


   def detect(self,image):
       (H,W) = image.shape[:2]
       rH = H / float(320)
       rW = W / float(320)
       blob = cv.dnn.blobFromImage(image,1.0,(320,320),(123.68, 116.78, 103.94),swapRB=True,crop=False)
       self.net.setInput(blob)
       (scores, geometry) = self.net.forward(self.layerNames)
       print(scores)

       (numRows, numCols) = scores.shape[2:4]
       rects = []
       confidences = []

       # start to decode the output
       for y in range(0, numRows):
           scoresData = scores[0, 0, y]
           xData0 = geometry[0, 0, y]
           xData1 = geometry[0, 1, y]
           xData2 = geometry[0, 2, y]
           xData3 = geometry[0, 3, y]
           anglesData = geometry[0, 4, y]

           # loop over the number of columns
           for x in range(0, numCols):
               # if our score does not have sufficient probability, ignore it
               if scoresData[x] < self.threshold:
                   continue

               # compute the offset factor as our resulting feature maps will
               # be 4x smaller than the input image
               (offsetX, offsetY) = (x * 4.0, y * 4.0)

               # extract the rotation angle for the prediction and then
               # compute the sin and cosine
               angle = anglesData[x]
               cos = np.cos(angle)
               sin = np.sin(angle)

               # use the geometry volume to derive the width and height of
               # the bounding box
               h = xData0[x] + xData2[x]
               w = xData1[x] + xData3[x]

               # compute both the starting and ending (x, y)-coordinates for
               # the text prediction bounding box
               endX = int(offsetX + (cos * xData1[x]) + (sin * xData2[x]))
               endY = int(offsetY - (sin * xData1[x]) + (cos * xData2[x]))
               startX = int(endX - w)
               startY = int(endY - h)

               # add the bounding box coordinates and probability score to
               # our respective lists
               rects.append([startX, startY, endX, endY])
               confidences.append(float(scoresData[x]))

       # 非最大抑制
       boxes = cv.dnn.NMSBoxes(rects, confidences, self.threshold, 0.8)
       result = np.zeros(image.shape[:2], dtype=np.uint8)
       #result = np.copy(image)
       for i in boxes:
           i = i[0]
           box = rects[i]
           startX = box[0]
           startY = box[1]
           endX = box[2]
           endY = box[3]
           startX = int(startX * rW)
           startY = int(startY * rH)
           endX = int(endX * rW)
           endY = int(endY * rH)

           # draw the bounding box on the image
           cv.rectangle(result, (startX, startY), (endX, endY), (255), 2)
       return result

if __name__ == "__main__":
    text_detector = TextAreaDetector('C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/frozen_east_text_detection.pb')
    frame = cv.imread('C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/1.jpg')
    cv.imshow("input",frame)
    result = text_detector.detect(frame)
    cv.imshow("result",result)
    cv.waitKey(0)
    cv.destroyAllWindows()


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输入图像：

最后的输出结果为下图

其中对矩形框进行非最大抑制代码为：boxes = cv.dnn.NMSBoxes(rects, confidences, self.threshold, 0.8)

• rects：矩形框
• confidences：各个矩形框得分
• self.threshold：confidence小于阈值抑制，大于confidence被保留
• 0.8：为交并比的值，值越大表示越相似，对大于0.8交并比的合并

详见：text_area_detect.py函数

3、形态学合并

#膨胀
se = cv.getStructuringElement(cv.MORPH_RECT,(45,1))
result = cv.morphologyEx(result, cv.MORPH_DILATE,se)
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#发现轮廓
contours, hierachy = cv.findContours(result, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
for c in range(len(contours)):
    box = cv.boundingRect(contours[c])
    if box[2] < 10 or box[3] < 10:
        continue
    cv.rectangle(frame,(box[0],box[1]),(box[0]+box[2],box[1]+box[3]),(0,0,255),2,8)
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详见：text_area_detect.py函数

4、排序与块分割

#roi块区域的排序与分割
nums = len(text_boxes)
    for i in range(nums):
        for j in range(i + 1, nums, 1):
            x1, y1, w1, h1 = text_boxes[i]
            x2, y2, w2, h2 = text_boxes[j]
            if y1 > y2:
                temp = text_boxes[i]
                text_boxes[i] = text_boxes[j]
                text_boxes[j] = temp
    for x,y,w,h in text_boxes:
        text_roi = frame[y:y+h+5, x:x+w, :]
        cv.imshow("text_roi", text_roi)
        cv.waitKey(0)

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详见：text_area_detect.py函数

5、行文本分割

对text_roi图像做二值化，对二值图像做Y-Projection图像投影

二值化之后，Y轴方向投影预览：

可以从投影的图像看出，中间有一部分断开，断开的地方可以用于行文本分割。

代码：

gray = cv.cvtColor(text_roi, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
print("threshold : %.2f" % ret)
cv.imshow("text_roi", gray)
text_lines = split_lines(binary)
if len(text_lines) == 1:
    cv.imshow("line", binary)
    cv.waitKey(0)
else:
    for line in text_lines:
        cv.imshow("line", line)
        cv.waitKey(0)
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详见：text_area_detect.py函数

其中text_lines = split_lines(binary)中的split_lines定义如下：

def split_lines(image):
    print("start to analysis text layout...");
    # Y-Projection
    h, w = image.shape
    hist = np.zeros(h, dtype=np.int32)
    for i in range(h):
        for c in range(w):
            pv = image[i, c]
            if pv == 255:
                hist[i] += 1

    # x = np.arange(h)
    # plt.bar(x, height=hist)
    # plt.show()

    # find lines
    hist[np.where(hist>5)] = 255
    hist[np.where(hist<=5)] = 0

    text_lines = []
    found = False
    count = 0
    start = -1
    for i in range(h):
        if hist[i] > 0 and found is False:
            found = True
            start = i
            count += 1
        if hist[i] > 0 and found is True:
            count += 1
        if hist[i] == 0 and found is True:
            found = False
            text_lines.append(image[start-2:start+count+2, 0:w])
            start = -1
            count = 0

    if found is True:
        text_lines.append(image[start - 2:start + count + 2, 0:w])
    print(len(text_lines))
    return text_lines
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详见：ocrutils.py函数

最后的结果就是text_roi区域被分割为两个line图像


6、字符分割与提取

预期效果：

字符ROI与排序

• 获取字符ROI（findcontours轮廓发现）

def get_data_set(image):
    print("generate dataset...")
    contours, hireachy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    # get all digits
    rois = []
    for c in range(len(contours)):
        box = cv.boundingRect(contours[c])
        if box[3] < 10:
            continue
        rois.append(box)

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详见：ocrutils.py函数

• 排序ROI（对x进行排序）

 # sort(box)
    num = len(rois)
    for i in range(num):
        for j in range(i+1, num, 1):
            x1, y1, w1, h1 = rois[i]
            x2, y2, w2, h2 = rois[j]
            if x2 < x1:
                temp = rois[j]
                rois[j] = rois[i]
                rois[i] = temp

    bgr = cv.cvtColor(image, cv.COLOR_GRAY2BGR)
    index = 0
    digit_data = np.zeros((num, 28*48), dtype=np.float32)
    for x, y, w, h in rois:
        cv.rectangle(bgr, (x, y), (x+w, y+h), (0, 0, 255), 2, 8)
        cv.putText(bgr, str(index), (x, y+10), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0), 1)
        digit = image[y:y+h,x:x+w]
        img = cv.resize(digit, (28, 48))
        row = np.reshape(img, (-1, 28 * 48))
        digit_data[index] = row
        index += 1
    cv.imshow("split digits", bgr)
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详见：ocrutils.py函数

主函数代码：

gray = cv.cvtColor(text_roi, cv.COLOR_BGR2GRAY)
ret, binary = cv.threshold(gray, 0, 255, cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
print("threshold : %.2f" % ret)
cv.imshow("text_roi", gray)
text_lines = split_lines(binary)
if len(text_lines) == 1:
    cv.imshow("line", binary)
    data, boxes = get_data_set(binary)
    cv.waitKey(0)
else:
    for line in text_lines:
        cv.imshow("line", line)
        data, boxes = get_data_set(binary)
        cv.waitKey(0)
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运行结果实现了对每个自取区域的分割与排序。

7、字符数据集生成与SVM训练数据集

下边是生成数据集然后对每个字符进行训练分类（基于ml模块的svm）

数据集生成：

• 每个字符为一行，大小为48(h)x28(w)
• 每一行对于label标签中一个数字
  

def load_data():
    images = []
    labels = []
    files = os.listdir("C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/digits")
    count = len(files)
    sample_data = np.zeros((count, 28*48), dtype=np.float32)
    index = 0
    for file_name in files:
        file_path = os.path.join("C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/digits", file_name)
        if os.path.isfile(file_path) is True:
            images.append(file_path)
            labels.append(file_name[:1])
            img = cv.imread(file_path, cv.IMREAD_GRAYSCALE)
            img = cv.resize(img, (28, 48))
            row = np.reshape(img, (-1, 28*48))
            sample_data[index] = row
            index += 1
    return sample_data, np.asarray(labels, np.int32)

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详见train.py函数

下边是使用SVM训练：

• OpenCV ML模块介绍
• SVM训练与保存模型
• SVM使用模型预测

# load data stage
train_data, train_labels = load_data()

# train stage
svm = cv.ml.SVM_create()
svm.setKernel(cv.ml.SVM_LINEAR)
svm.setType(cv.ml.SVM_C_SVC)
svm.setC(2.67)
svm.setGamma(5.383)
svm.train(train_data, cv.ml.ROW_SAMPLE, train_labels)
svm.save("svm_data.yml")

svm = cv.ml.SVM_load("svm_data.yml")
result = svm.predict(train_data)[1]
print(result)
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详见train.py函数

8、使用模型预测与显示预测结果

对给定image数据部分的提取

index = 0
    digit_data = np.zeros((num, 28*48), dtype=np.float32)
    for x, y, w, h in rois:
        cv.rectangle(bgr, (x, y), (x+w, y+h), (0, 0, 255), 2, 8)
        cv.putText(bgr, str(index), (x, y+10), cv.FONT_HERSHEY_PLAIN, 1.0, (0, 255, 0), 1)
        digit = image[y:y+h,x:x+w]
        img = cv.resize(digit, (28, 48))
        row = np.reshape(img, (-1, 28 * 48))
        digit_data[index] = row
        index += 1
    cv.imshow("split digits", bgr)
    return digit_data, rois
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详见：ocrutils.py函数

字符识别：

• 使用SVM预测

显示结果：

• 多行预测与显示
  完整的main函数：

if __name__ == "__main__":
    text_detector = TextAreaDetector('C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/frozen_east_text_detection.pb')
    frame = cv.imread('C:/Program Files (x86)/pycharm/pycharm/PycharmProjects/ML/ocr_demo/1.jpg')
    cv.imshow("input",frame)
    result = text_detector.detect(frame)
    se = cv.getStructuringElement(cv.MORPH_RECT,(45,1))
    result = cv.morphologyEx(result, cv.MORPH_DILATE,se)

    text_boxes = []
    contours, hierachy = cv.findContours(result, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
    for c in range(len(contours)):
        box = cv.boundingRect(contours[c])
        if box[2] < 10 or box[3] < 10:
            continue
        #cv.rectangle(frame,(box[0],box[1]),(box[0]+box[2],box[1]+box[3]),(0,0,255),2,8)
        text_boxes.append(box)

    nums = len(text_boxes)
    for i in range(nums):
        for j in range(i+1,nums,1):
            x1, y1, w1, h1 = text_boxes[i]
            x2, y2, w2, h2 = text_boxes[j]
            if y1 > y2:
                temp = text_boxes[i]
                text_boxes[i] = text_boxes[j]
                text_boxes[j] = temp


    dr = DigitNumberRecognizer()
    for x,y,w,h in text_boxes:
        text_roi = frame[y:y+h+5, x:x+w, :]
        gray = cv.cvtColor(text_roi, cv.COLOR_BGR2GRAY)
        ret, binary = cv.threshold(gray,0,255,cv.THRESH_BINARY_INV | cv.THRESH_OTSU)
        print("threshold : %.2f"%ret)
        cv.imshow("text_roi", gray)
        text_lines = split_lines(binary)
        if len(text_lines) == 1:
            cv.imshow("line", binary)
            data, boxes = get_data_set(binary)
            ocr_text = dr.predict(data)
            cv.putText(frame, ocr_text, (x+w,y), cv.FONT_HERSHEY_SIMPLEX ,1.0, (0,0,255), 2)
            cv.imshow("result", frame)
            cv.waitKey(0)
        else:
            gap = 0
            for line in text_lines:
                cv.imshow("line", line)
                data, boxes = get_data_set(line)
                ocr_text = dr.predict(data)
                cv.putText(frame, ocr_text, (x + w, y + gap), cv.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255), 2)
                cv.imshow("result", frame)
                cv.waitKey(0)
                gap += 50


   # cv.imshow("result",frame)
    cv.waitKey(0)
    cv.destroyAllWindows()

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详见：text_area_detect.py函数

最后输出结果：

四、总结

附上完整代码：https://github.com/zxinyang38/opencv-