text_detector.py

from imutils.object_detection import non_max_suppression
import numpy as np
import pytesseract
import argparse
import cv2


def decode_predictions(scores, geometry):
    # 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] < args["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

if __name__ == '__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", type=str,
                    help="path to input image")
    ap.add_argument("-east", "--east", type=str,
                    help="path to input EAST text detector")
    ap.add_argument("-c", "--min-confidence", type=float, default=0.5,
                    help="minimum probability required to inspect a region")
    ap.add_argument("-w", "--width", type=int, default=320,
                    help="nearest multiple of 32 for resized width")
    ap.add_argument("-e", "--height", type=int, default=320,
                    help="nearest multiple of 32 for resized height")
    ap.add_argument("-p", "--padding", type=float, default=0.0,
                    help="amount of padding to add to each border of ROI")
    args = vars(ap.parse_args())

    # load the input image and grab the image dimensions
    image = cv2.imread(args["image"])
    orig = image.copy()
    (origH, origW) = image.shape[:2]

    # set the new width and height and then determine the ratio in change
    # for both the width and height
    (newW, newH) = (args["width"], args["height"])
    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 that
    # we are interested in -- 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(args["east"])

    # 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)
    net.setInput(blob)
    (scores, geometry) = net.forward(layerNames)

    # decode the predictions, then  apply non-maxima suppression to
    # suppress weak, overlapping bounding boxes
    (rects, confidences) = decode_predictions(scores, geometry)
    boxes = non_max_suppression(np.array(rects), probs=confidences)

    # initialize the list of results
    results = []

    # loop over the bounding boxes
    for (startX, startY, endX, endY) in boxes:
        # scale the bounding box 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
        # apply 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) * args["padding"])
        dY = int((endY - startY) * args["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]

        # 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")
        text = pytesseract.image_to_string(roi, config=config)

        # add the bounding box coordinates and OCR'd text to the list
        # of results
        results.append(((startX, startY, endX, endY), text))

    # sort the results bounding box coordinates from top to bottom
    results = sorted(results, key=lambda r: r[0][1])

    # 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, then draw the text and a bounding box surrounding
        # the text region of the input image
        text = "".join([c if ord(c) < 128 else "" for c in text]).strip()
        output = orig.copy()
        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)
        cv2.waitKey(0)