Opencv '；numpy.ndarray和#x27；对象没有属性'；拆分'；

我读过那篇文章，但没有找到答案

我正在关注这篇文章。这说明了如何使用TesseAct检测和提取图像上的文本。我已在系统中装入了所有内容，但当我运行代码时，收到以下错误：

Traceback (most recent call last):
File "text_recognition.py", line 157, in <module>
text = pytesseract.image_to_string(roi, config=config)
File "C:\Users\prince.bhatia\AppData\Local\Programs\Python\Python36\lib\site-p
ackages\pytesseract\pytesseract.py", line 104, in image_to_string
  if len(image.split()) == 4:
AttributeError: 'numpy.ndarray' object has no attribute 'split'

回溯（最近一次呼叫最后一次）：
文件“text_recognition.py”，第157行，在
text=pytesseract.image\u to\u字符串（roi，config=config）
文件“C:\Users\prince.bhatia\AppData\Local\Programs\Python\Python36\lib\site-p
packages\pytesseract\pytesseract.py”，第104行，在图像\u到\u字符串中
如果len（image.split（））==4：
AttributeError:'numpy.ndarray'对象没有属性'split'

以下是我的完整代码：

# USAGE
# python text_recognition.py --east frozen_east_text_detection.pb --image images/example_01.jpg
# python text_recognition.py --east frozen_east_text_detection.pb --image images/example_04.jpg --padding 0.05

# import the necessary packages
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)

# construct the argument parser and parse the arguments
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 -- 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]
    print(roi)

    # 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)#here i cam receiving error

    # 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)

#用法
#python text_recognition.py--east freezed_east_text_detection.pb--image image/example_01.jpg
#python text_recognition.py--east freezed_east_text_detection.pb--image image/example_04.jpg--padding 0.05
#导入必要的包
从imutils.object\u detection导入非最大值抑制
将numpy作为np导入
导入pytesseract
导入argparse
进口cv2
def解码_预测（分数、几何）：
#从分数卷中获取行数和列数，然后
#初始化边界框矩形集和相应的
#信心分数
（numRows，numCols）=分数。形状[2:4]
rects=[]
信任=[]
#循环计算行数
对于范围内的y（0，numRows）：
#提取分数（概率），然后是
#用于导出潜在边界框的几何数据
#环绕文本的坐标
分数数据=分数[0,0,y]
xData0=几何体[0，0，y]
xData1=几何体[0，1，y]
xData2=几何体[0，2，y]
xData3=几何体[0,3,y]
角度数据=几何体[0,4,y]
#循环计算列数
对于范围内的x（0，numCols）：
#如果我们的分数没有足够的概率，
#别理它
如果得分数据[x]