python - 无法在函数'c中打开 “frozen_east_text_detection.pb”

Question

完整的错误说:

cv2.error:OpenCV(4.1.0)C:\ projects \ opencv-python \ opencv \ modules \ dnn \ src \ caffe \ caffe_io.cpp:1132:错误:(-2:未指定错误)失败:fs.is_open( )。无法在函数'c中打开“frozen_east_text_detection.pb”

这里我有代码:

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

imagePath = "C:/xampp/htdocs/Tensorflow/TextDetection-master/images/tabla1.jpg"
east = "frozen_east_text_detection.pb" 
newW = 640
newH = 480
min_confidence = 0.5 

image = cv2.imread(imagePath) # it is working variable
orig = image.copy()
(H,W) = image.shape[:2]

将新宽度设置为高度，然后确定变化率

rW = W / float(newW)
rH = H / float(newH)

调整图像大小并获取新的图像尺寸

image = cv2.resize(image, (newW, newH))
(H,W) = image.shape[:2]

"""
In order to perform text detection using OpenCV and the EAST deep learning model, 
we need to extract the output feature maps of TWO LAYERS
"""
# Define the TWO output layer names ofr the EAST detector model
# FIRST LAYER : output probabilities of a region containing text or not.
# SECOND LAYER: bounding box coordinates of text.

layerNames = [
    "feature_fusion/Conv_7/Sigmoid", 
    "feature_fusion/concat_3"
]

print("[INFO] loading EAST detector")
net = cv2.dnn.readNet( east ) #Load the Nerual Network into memory

# 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( #https://www.pyimagesearch.com/2017/11/06/deep-learning-opencvs-blobfromimage-works/ 
    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()
print("[INFO] text detection took {:.6f} seconds".format(end-start))

# grab the umber of rows and columns from the scores volume, then
# initialize aour set of bounding box rectagles and corresponding
# cofidence 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):
        #i 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]  )

#apply non-maxima suppression to suppress weak, overlapping bounding boxes
boxes = non_max_suppression(np.array(rects) , probs=confidences) #imutils-> https://github.com/jrosebr1/imutils/blob/master/imutils/object_detection.py#L4
#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)
    #draw the bounding box on the image
    cv2.rectangle( orig , (startX,startY) , (endX,endY) , (0,255,0) , 2 )
#show the outut image
cv2.imshow("Text detection",orig)
cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

您可以通过从代码的主要功能中提供frozen_east_text_detection.pb的绝对文件路径来解决此问题