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text.py
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text.py
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from imutils.object_detection import non_max_suppression
import numpy as np
import pytesseract
import argparse
import cv2
# load the pre-trained EAST text detector
#print("[INFO] loading EAST text detector...")
#net = cv2.dnn.readNet("./frozen_east_text_detection.pb")
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] < 0.5:
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 detect_all_words(image):
config = ("-l eng --oem 1 --psm 7")
#cv2.imshow("Detecting words in", image)
#cv2.waitKey(0)
text = pytesseract.image_to_string(image)
return text
def detect_words(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) = (640, 640)
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"]
# 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)
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
padding = 0.0
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]
# 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])
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, 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 = cv2.rectangle(output, (startX, startY), (endX, endY),
(0, 0, 255), 2)
output = cv2.putText(output, text, (startX, startY - 20),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 0, 255), 3)
# show the output image
output = cv2.resize(output, (600, 600))
cv2.imshow("Text Detection", output)
cv2.waitKey(0)