Python-OpenCV-- 台式机外接摄像头EAST文本检测+OCR识别

一、代码和训练文件:https://download.csdn.net/download/GGY1102/16681984

  • 利用 OpenCV 的 EAST 文本检测器定位图像中的文本区域。
  • 提取每个文本 ROI,然后使用 OpenCV 和 Tesseract v4 进行文本识别。
  •  

二、实际测试代码

from imutils.object_detection import non_max_suppression
from PIL import Image
import numpy as np
import pytesseract
import time
import cv2

from matplotlib import pyplot as plt
import os

cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FPS, 15)


def decode_predictions(scores, geometry):
    """
    EAST 文本检测器两个参数:
        scores:文本区域的概率。
        geometry:文本区域的边界框位置。
    """
    # The minimum probability of a detected text region
    min_confidence = 0.5

    # grab the number of rows and columns from the scores volume, then
    # initialize our set of bounding box rectangles and corresponding
    # confidence scores
    numRows, numCols = scores.shape[2:4]
    rects = []
    confidences = []

    # loop over the number of rows
    for y in range(0, numRows):
        # extract the scores (probabilities), followed by the
        # geometrical data used to derive potential bounding box
        # coordinates that surround text
        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] < min_confidence:
                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(scoresData[x])

    # return a tuple of the bounding boxes and associated confidences
    return (rects, confidences)


def text_recognition(image):
    east_model = "frozen_east_text_detection.pb"
    # img_path = "images/road-sign-2-768x347.jpg"

    # set the new width and height and then determine the ratio in change for
    # both the width and height, both of them are multiples of 32
    newW, newH = 320, 320

    #  The (optional) amount of padding to add to each ROI border
    # You can try 0.05 for 5% or 0.10 for 10% (and so on) if find OCR result is incorrect
    padding = 0.0

    # in order to apply Tesseract v4 to OCR text we must supply
    # (1) a language, (2) an OEM flag of 4, indicating that the we
    # wish to use the LSTM neural net model for OCR, and finally
    # (3) an OEM value, in this case, 7 which implies that we are
    # treating the ROI as a single line of text
    config = ("-l eng --oem 1 --psm 7")  # chi_sim


    orig = image.copy()
    origH, origW = image.shape[:2]

    # calculate ratios that will be used to scale bounding box coordinates
    rW = origW / float(newW)
    rH = origH / float(newH)

    # resize the image and grab the new image dimensions
    image = cv2.resize(image, (newW, newH))
    (H, W) = image.shape[:2]

    # define the two output layer names for the EAST detector model the first is the output probabilities
    # and the second can be used to derive the bounding box coordinates of text
    layerNames = ["feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"]

    # load the pre-trained EAST text detector
    print("[INFO] loading EAST text detector...")
    net = cv2.dnn.readNet(east_model)

    # construct a blob from the image and then perform a forward pass of
    # the model to obtain the two output layer sets
    blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
                                 (123.68, 116.78, 103.94), swapRB=True, crop=False)
    start = time.time()
    net.setInput(blob)
    (scores, geometry) = net.forward(layerNames)
    end = time.time()

    # show timing information on text prediction
    print("[INFO] text detection cost {:.6f} seconds".format(end - start))

    # decode the predictions, then apply non-maxima suppression to
    # suppress weak, overlapping bounding boxes
    (rects, confidences) = decode_predictions(scores, geometry)
    # NMS effectively takes the most likely text regions, eliminating other overlapping regions
    boxes = non_max_suppression(np.array(rects), probs=confidences)

    # initialize the list of results to contain our OCR bounding boxes and text
    results = []

    # the bounding boxes represent where the text regions are, then recognize the text.
    # loop over the bounding boxes and process the results, preparing the stage for actual text recognition
    for (startX, startY, endX, endY) in boxes:
        # scale the bounding boxes coordinates based on the respective ratios
        startX = int(startX * rW)
        startY = int(startY * rH)
        endX = int(endX * rW)
        endY = int(endY * rH)

        # in order to obtain a better OCR of the text we can potentially
        # add a bit of padding surrounding the bounding box -- here we
        # are computing the deltas in both the x and y directions
        dX = int((endX - startX) * padding)
        dY = int((endY - startY) * padding)

        # apply padding to each side of the bounding box, respectively
        startX = max(0, startX - dX)
        startY = max(0, startY - dY)
        endX = min(origW, endX + (dX * 2))
        endY = min(origH, endY + (dY * 2))

        # extract the actual padded ROI
        roi = orig[startY:endY, startX:endX]

        # use Tesseract v4 to recognize a text ROI in an image
        text = pytesseract.image_to_string(roi, config=config)

        # add the bounding box coordinates and actual text string to the results list
        results.append(((startX, startY, endX, endY), text))

    # sort the bounding boxes coordinates from top to bottom based on the y-coordinate of the bounding box
    results = sorted(results, key=lambda r: r[0][1])

    output = orig.copy()
    # loop over the results
    for ((startX, startY, endX, endY), text) in results:
        # display the text OCR'd by Tesseract
        print("OCR TEXT")
        print("========")
        print("{}\n".format(text))

        # strip out non-ASCII text so we can draw the text on the image using OpenCV
        text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
        # draw the text and a bounding box surrounding the text region of the input image
        cv2.rectangle(output, (startX, startY), (endX, endY), (0, 0, 255), 2)
        cv2.putText(output, text, (startX, startY - 20),
                    cv2.FONT_HERSHEY_SIMPLEX, 1.2, (0, 0, 255), 3)

    # show the output image
    cv2.imshow("Text Detection", output)

while True:
    ret, image = cap.read()
    text_recognition(image)

  #  cv2.imshow('img', image)
    if cv2.waitKey(10) == ord("q"):
        break
#随时准备按q退出
cap.release()
cv2.destroyAllWindows()

 

参考:https://zhuanlan.zhihu.com/p/64857243

 

 

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