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text-opencv/Images/billboard1.jpeg

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+# USAGE
+# python text_detection.py --image images/lebron_james.jpg --east frozen_east_text_detection.pb
+
+# import the necessary packages
+from imutils.object_detection import non_max_suppression
+import numpy as np
+import argparse
+import time
+import cv2
+
+# 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="resized image width (should be multiple of 32)")
+ap.add_argument("-e", "--height", type=int, default=320,
+	help="resized image height (should be multiple of 32)")
+args = vars(ap.parse_args())
+
+# load the input image and grab the image dimensions
+image = cv2.imread(args["image"])
+orig = image.copy()
+(H, W) = 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 = W / float(newW)
+rH = H / 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)
+start = time.time()
+net.setInput(blob)
+(scores, geometry) = net.forward(layerNames)
+end = time.time()
+
+# show timing information on text prediction
+print("[INFO] text detection took {:.6f} seconds".format(end - start))
+
+# 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])
+
+# apply non-maxima suppression to suppress weak, overlapping bounding
+# boxes
+boxes = non_max_suppression(np.array(rects), probs=confidences)
+
+# 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 output image
+cv2.imshow("Text Detection", orig)
+cv2.waitKey(0)

text-opencv/Images/billboard2.jpeg

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+# USAGE
+# python text_detection_video.py --east frozen_east_text_detection.pb
+
+# import the necessary packages
+from imutils.video import VideoStream
+from imutils.video import FPS
+from imutils.object_detection import non_max_suppression
+import numpy as np
+import argparse
+import imutils
+import time
+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("-east", "--east", type=str, required=True,
+	help="path to input EAST text detector")
+ap.add_argument("-v", "--video", type=str,
+	help="path to optinal input video file")
+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="resized image width (should be multiple of 32)")
+ap.add_argument("-e", "--height", type=int, default=320,
+	help="resized image height (should be multiple of 32)")
+args = vars(ap.parse_args())
+
+# initialize the original frame dimensions, new frame dimensions,
+# and ratio between the dimensions
+(W, H) = (None, None)
+(newW, newH) = (args["width"], args["height"])
+(rW, rH) = (None, None)
+
+# 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"])
+
+# if a video path was not supplied, grab the reference to the web cam
+if not args.get("video", False):
+	print("[INFO] starting video stream...")
+	vs = VideoStream(src=0).start()
+	time.sleep(1.0)
+
+# otherwise, grab a reference to the video file
+else:
+	vs = cv2.VideoCapture(args["video"])
+
+# start the FPS throughput estimator
+fps = FPS().start()
+
+# loop over frames from the video stream
+while True:
+	# grab the current frame, then handle if we are using a
+	# VideoStream or VideoCapture object
+	frame = vs.read()
+	frame = frame[1] if args.get("video", False) else frame
+
+	# check to see if we have reached the end of the stream
+	if frame is None:
+		break
+
+	# resize the frame, maintaining the aspect ratio
+	frame = imutils.resize(frame, width=1000)
+	orig = frame.copy()
+
+	# if our frame dimensions are None, we still need to compute the
+	# ratio of old frame dimensions to new frame dimensions
+	if W is None or H is None:
+		(H, W) = frame.shape[:2]
+		rW = W / float(newW)
+		rH = H / float(newH)
+
+	# resize the frame, this time ignoring aspect ratio
+	frame = cv2.resize(frame, (newW, newH))
+
+	# construct a blob from the frame and then perform a forward pass
+	# of the model to obtain the two output layer sets
+	blob = cv2.dnn.blobFromImage(frame, 1.0, (newW, newH),
+		(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)
+
+	# 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 frame
+		cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)
+
+	# update the FPS counter
+	fps.update()
+
+	# show the output frame
+	cv2.imshow("Text Detection", orig)
+	key = cv2.waitKey(1) & 0xFF
+
+	# if the `q` key was pressed, break from the loop
+	if key == ord("q"):
+		break
+
+# stop the timer and display FPS information
+fps.stop()
+print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
+print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
+
+# if we are using a webcam, release the pointer
+if not args.get("video", False):
+	vs.stop()
+
+# otherwise, release the file pointer
+else:
+	vs.release()
+
+# close all windows
+cv2.destroyAllWindows()