# 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()