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text_detection_video_tpu.py
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text_detection_video_tpu.py
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from imutils.video import VideoStream
from imutils.video import FPS
import numpy as np
import argparse
import imutils
import time
import cv2
from edgetpu.basic.basic_engine import BasicEngine
from edgetpu.basic import edgetpu_utils
import math
fpsstr = ""
framecount = 0
time1 = 0
def rotated_Rectangle(img, rotatedRect, color, thickness=1, lineType=cv2.LINE_8, shift=0):
(x, y), (width, height), angle = rotatedRect
pt1_1 = (int(x + width / 2), int(y + height / 2))
pt2_1 = (int(x + width / 2), int(y - height / 2))
pt3_1 = (int(x - width / 2), int(y - height / 2))
pt4_1 = (int(x - width / 2), int(y + height / 2))
t = np.array([[np.cos(angle), -np.sin(angle), x-x*np.cos(angle)+y*np.sin(angle)],
[np.sin(angle), np.cos(angle), y-x*np.sin(angle)-y*np.cos(angle)],
[0, 0, 1]])
tmp_pt1_1 = np.array([[pt1_1[0]], [pt1_1[1]], [1]])
tmp_pt1_2 = np.dot(t, tmp_pt1_1)
pt1_2 = (int(tmp_pt1_2[0][0]), int(tmp_pt1_2[1][0]))
tmp_pt2_1 = np.array([[pt2_1[0]], [pt2_1[1]], [1]])
tmp_pt2_2 = np.dot(t, tmp_pt2_1)
pt2_2 = (int(tmp_pt2_2[0][0]), int(tmp_pt2_2[1][0]))
tmp_pt3_1 = np.array([[pt3_1[0]], [pt3_1[1]], [1]])
tmp_pt3_2 = np.dot(t, tmp_pt3_1)
pt3_2 = (int(tmp_pt3_2[0][0]), int(tmp_pt3_2[1][0]))
tmp_pt4_1 = np.array([[pt4_1[0]], [pt4_1[1]], [1]])
tmp_pt4_2 = np.dot(t, tmp_pt4_1)
pt4_2 = (int(tmp_pt4_2[0][0]), int(tmp_pt4_2[1][0]))
points = np.array([pt1_2, pt2_2, pt3_2, pt4_2])
return points
def non_max_suppression(boxes, probs=None, angles=None, overlapThresh=0.3):
# if there are no boxes, return an empty list
if len(boxes) == 0:
return [], []
# if the bounding boxes are integers, convert them to floats -- this
# is important since we'll be doing a bunch of divisions
if boxes.dtype.kind == "i":
boxes = boxes.astype("float")
# initialize the list of picked indexes
pick = []
# grab the coordinates of the bounding boxes
x1 = boxes[:, 0]
y1 = boxes[:, 1]
x2 = boxes[:, 2]
y2 = boxes[:, 3]
# compute the area of the bounding boxes and grab the indexes to sort
# (in the case that no probabilities are provided, simply sort on the bottom-left y-coordinate)
area = (x2 - x1 + 1) * (y2 - y1 + 1)
idxs = y2
# if probabilities are provided, sort on them instead
if probs is not None:
idxs = probs
# sort the indexes
idxs = np.argsort(idxs)
# keep looping while some indexes still remain in the indexes list
while len(idxs) > 0:
# grab the last index in the indexes list and add the index value to the list of picked indexes
last = len(idxs) - 1
i = idxs[last]
pick.append(i)
# find the largest (x, y) coordinates for the start of the bounding box and the smallest (x, y) coordinates for the end of the bounding box
xx1 = np.maximum(x1[i], x1[idxs[:last]])
yy1 = np.maximum(y1[i], y1[idxs[:last]])
xx2 = np.minimum(x2[i], x2[idxs[:last]])
yy2 = np.minimum(y2[i], y2[idxs[:last]])
# compute the width and height of the bounding box
w = np.maximum(0, xx2 - xx1 + 1)
h = np.maximum(0, yy2 - yy1 + 1)
# compute the ratio of overlap
overlap = (w * h) / area[idxs[:last]]
# delete all indexes from the index list that have overlap greater than the provided overlap threshold
idxs = np.delete(idxs, np.concatenate(([last], np.where(overlap > overlapThresh)[0])))
# return only the bounding boxes that were picked
return boxes[pick].astype("int"), angles[pick]
def decode_predictions(scores, geometry1, geometry2):
# 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 = []
angles = []
# 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 = geometry1[0, 0, y]
xData1 = geometry1[0, 1, y]
xData2 = geometry1[0, 2, y]
xData3 = geometry1[0, 3, y]
anglesData = geometry2[0, 0, 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])
angles.append(angle)
# return a tuple of the bounding boxes and associated confidences
return (rects, confidences, angles)
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-east", "--east", type=str, default="east_text_detection_320x320_full_integer_quant_edgetpu.tflite", 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)")
ap.add_argument("-cw", "--camera_width", type=int, default=640, help='USB Camera resolution (width). (Default=640)')
ap.add_argument("-ch", "--camera_height", type=int, default=480, help='USB Camera resolution (height). (Default=480)')
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
# load the pre-trained EAST text detector
print("[INFO] loading EAST text detector...")
devices = edgetpu_utils.ListEdgeTpuPaths(edgetpu_utils.EDGE_TPU_STATE_UNASSIGNED)
engine = BasicEngine(model_path=args["east"], device_path=devices[0])
offset = 0
output_offsets = [0]
for size in engine.get_all_output_tensors_sizes():
offset += int(size)
output_offsets.append(offset)
# 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:
t1 = time.perf_counter()
# 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=640)
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
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = np.expand_dims(frame, axis=0)
frame = frame.astype(np.uint8)
inference_time, output = engine.run_inference(frame.flatten())
outputs = [output[i:j] for i, j in zip(output_offsets, output_offsets[1:])]
scores = outputs[0].reshape(1, int(args["height"]/4), int(args["width"]/4), 1)
geometry1 = outputs[1].reshape(1, int(args["height"]/4), int(args["width"]/4), 4)
geometry2 = outputs[2].reshape(1, int(args["height"]/4), int(args["width"]/4), 1)
scores = np.transpose(scores, [0, 3, 1, 2])
geometry1 = np.transpose(geometry1, [0, 3, 1, 2])
geometry2 = np.transpose(geometry2, [0, 3, 1, 2])
# decode the predictions, then apply non-maxima suppression to
# suppress weak, overlapping bounding boxes
(rects, confidences, angles) = decode_predictions(scores, geometry1, geometry2)
boxes, angles = non_max_suppression(np.array(rects), probs=confidences, angles=np.array(angles))
# loop over the bounding boxes
for ((startX, startY, endX, endY), angle) in zip(boxes, angles):
# 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
width = abs(endX - startX)
height = abs(endY - startY)
centerX = int(startX + width / 2)
centerY = int(startY + height / 2)
rotatedRect = ((centerX, centerY), ((endX - startX), (endY - startY)), -angle)
points = rotated_Rectangle(orig, rotatedRect, color=(0, 255, 0), thickness=2)
cv2.polylines(orig, [points], isClosed=True, color=(0, 255, 0), thickness=2, lineType=cv2.LINE_8, shift=0)
cv2.putText(orig, fpsstr, (args["camera_width"]-170,15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (38,0,255), 1, cv2.LINE_AA)
# update the FPS counter
fps.update()
# show the output frame
cv2.imshow("Text Detection", orig)
if cv2.waitKey(1)&0xFF == ord('q'):
break
# FPS calculation
framecount += 1
if framecount >= 10:
fpsstr = "(Playback) {:.1f} FPS".format(time1/10)
framecount = 0
time1 = 0
t2 = time.perf_counter()
elapsedTime = t2-t1
time1 += 1/elapsedTime
# 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()