--- a
+++ b/EAST_text_recognition.py
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+# 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("-east", "--east", type=str,default=r'\EAST-Text-Detection-and-Extraction\frozen_east_text_detection.pb'
+	,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(r"PATH TO INPUT 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]
+
+	# 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)
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