深度学习的端到端文本OCR:使用EAST从自然场景图片中提取文本（二）

##Loading the necessary packages  
import numpy as np
import cv2
from imutils.object_detection import non_max_suppression
import pytesseract
from matplotlib import pyplot as plt#Creating argument dictionary for the default arguments needed in the code.
args = {"image":"../input/text-detection/example-images/Example-images/ex24.jpg", "east":"../input/text-detection/east_text_detection.pb", "min_confidence":0.5, "width":320, "height":320}

#Give location of the image to be read.
#"Example-images/ex24.jpg" image is being loaded here. args['image']="../input/text-detection/example-images/Example-images/ex24.jpg"
image = cv2.imread(args['image'])#Saving a original image and shape
orig = image.copy()
(origH, origW) = image.shape[:2]# set the new height and width to default 320 by using args #dictionary.  
(newW, newH) = (args["width"], args["height"])#Calculate the ratio between original and new image for both height and weight.
#This ratio will be used to translate bounding box location on the original image.
rW = origW / float(newW)
rH = origH / float(newH)# resize the original image to new dimensions
image = cv2.resize(image, (newW, newH))
(H, W) = image.shape[:2]# construct a blob from the image to forward pass it to EAST model
blob = cv2.dnn.blobFromImage(image, 1.0, (W, H),
(123.68, 116.78, 103.94), swapRB=True, crop=False)

# load the pre-trained EAST model for text detection
net = cv2.dnn.readNet(args["east"])# We would like to get two outputs from the EAST model.
#1. Probabilty scores for the region whether that contains text or not.
#2. Geometry of the text -- Coordinates of the bounding box detecting a text
# The following two layer need to pulled from EAST model for achieving this.
layerNames = [
"feature_fusion/Conv_7/Sigmoid",
"feature_fusion/concat_3"]

#Forward pass the blob from the image to get the desired output layers
net.setInput(blob)
(scores, geometry) = net.forward(layerNames)

## Returns a bounding box and probability score if it is more than minimum confidence
def predictions(prob_score, geo):
(numR, numC) = prob_score.shape[2:4]
boxes = []
 confidence_val = []# loop over rows
for y in range(0, numR):
 scoresData = prob_score[0, 0, y]
 x0 = geo[0, 0, y]
 x1 = geo[0, 1, y]
 x2 = geo[0, 2, y]
 x3 = geo[0, 3, y]
 anglesData = geo[0, 4, y]# loop over the number of columns
 for i in range(0, numC):
 if scoresData[i] < args["min_confidence"]:
 continue(offX, offY) = (i * 4.0, y * 4.0)# extracting the rotation angle for the prediction and computing the sine and cosine
 angle = anglesData[i]
 cos = np.cos(angle)
 sin = np.sin(angle)# using the geo volume to get the dimensions of the bounding box
 h = x0[i] + x2[i]
 w = x1[i] + x3[i]# compute start and end for the text pred bbox
 endX = int(offX + (cos * x1[i]) + (sin * x2[i]))
 endY = int(offY - (sin * x1[i]) + (cos * x2[i]))
 startX = int(endX - w)
 startY = int(endY - h)boxes.append((startX, startY, endX, endY))
 confidence_val.append(scoresData[i])# return bounding boxes and associated confidence_val
return (boxes, confidence_val)

# Find predictions and apply non-maxima suppression
(boxes, confidence_val) = predictions(scores, geometry)
boxes = non_max_suppression(np.array(boxes), probs=confidence_val)

# initialize the list of results
results = []# loop over the bounding boxes to find the coordinate of bounding boxes
for (startX, startY, endX, endY) in boxes:
# scale the coordinates based on the respective ratios in order to reflect bounding box on the original image
startX = int(startX * rW)
startY = int(startY * rH)
endX = int(endX * rW)
 endY = int(endY * rH)#extract the region of interest
 r = orig[startY:endY, startX:endX]#configuration setting to convert image to string.  
configuration = ("-l eng --oem 1 --psm 8")
    ##This will recognize the text from the image of bounding box
 text = pytesseract.image_to_string(r, config=configuration)# append bbox coordinate and associated text to the list of results
results.append(((startX, startY, endX, endY), text))

l: language, chosen English in the above code.
    oem(OCR Engine modes):
    0 Legacy engine only.
    1 Neural nets LSTM engine only.
    2 Legacy + LSTM engines.
    3 Default, based on what is available.
    psm(Page segmentation modes):
    0 Orientation and script detection (OSD) only.
    1 Automatic page segmentation with OSD.
    2 Automatic page segmentation, but no OSD, or OCR. (not implemented)
    3 Fully automatic page segmentation, but no OSD. (Default)
    4 Assume a single column of text of variable sizes.
    5 Assume a single uniform block of vertically aligned text.
    6 Assume a single uniform block of text.
    7 Treat the image as a single text line.
    8 Treat the image as a single word.
    9 Treat the image as a single word in a circle.
    10 Treat the image as a single character.
    11 Sparse text. Find as much text as possible in no particular order.
    12 Sparse text with OSD.
    13 Raw line. Treat the image as a single text line, bypassing hacks that are Tesseract-specific.

#Display the image with bounding box and recognized text
orig_image = orig.copy()# Moving over the results and display on the image
for ((start_X, start_Y, end_X, end_Y), text) in results:
# display the text detected by Tesseract
 print("{}\n".format(text))# Displaying text
text = "".join([x if ord(x) < 128 else "" for x in text]).strip()
cv2.rectangle(orig_image, (start_X, start_Y), (end_X, end_Y),
 (0, 0, 255), 2)
cv2.putText(orig_image, text, (start_X, start_Y - 30),
 cv2.FONT_HERSHEY_SIMPLEX, 0.7,(0,0, 255), 2)plt.imshow(orig_image)
plt.title('Output')
plt.show()