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# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
import sys
import argparse
import numpy as np
import cv2 as cv
from crnn import CRNN
sys.path.append('../text_detection_db')
from db import DB
def str2bool(v):
if v.lower() in ['on', 'yes', 'true', 'y', 't']:
return True
elif v.lower() in ['off', 'no', 'false', 'n', 'f']:
return False
else:
raise NotImplementedError
parser = argparse.ArgumentParser(
description="An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition (https://arxiv.org/abs/1507.05717)")
parser.add_argument('--input', '-i', type=str, help='Path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='text_recognition_CRNN_VGG_BiLSTM_CTC.onnx', help='Path to the model.')
parser.add_argument('--width', type=int, default=736,
help='The width of input image being sent to the text detector.')
parser.add_argument('--height', type=int, default=736,
help='The height of input image being sent to the text detector.')
parser.add_argument('--save', '-s', type=str, default=False, help='Set true to save results. This flag is invalid when using camera.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Set true to open a window for result visualization. This flag is invalid when using camera.')
args = parser.parse_args()
def visualize(image, boxes, texts, color=(0, 255, 0), isClosed=True, thickness=2):
output = image.copy()
pts = np.array(boxes[0])
output = cv.polylines(output, pts, isClosed, color, thickness)
for box, text in zip(boxes[0], texts):
cv.putText(output, text, (box[1].astype(np.int32)), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
return output
if __name__ == '__main__':
# Instantiate CRNN for text recognition
recognizer = CRNN(modelPath=args.model)
# Instantiate DB for text detection
detector = DB(modelPath='../text_detection_db/text_detection_db.onnx',
inputSize=[args.width, args.height],
binaryThreshold=0.3,
polygonThreshold=0.5,
maxCandidates=200,
unclipRatio=2.0
)
# If input is an image
if args.input is not None:
image = cv.imread(args.input)
image = cv.resize(image, [args.width, args.height])
# Inference
results = detector.infer(image)
texts = []
for box, score in zip(results[0], results[1]):
texts.append(
recognizer.infer(image, box.reshape(8))
)
# Draw results on the input image
image = visualize(image, results, texts)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg\n')
cv.imwrite('result.jpg', image)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, image)
cv.waitKey(0)
else: # Omit input to call default camera
deviceId = 0
cap = cv.VideoCapture(deviceId)
tm = cv.TickMeter()
while cv.waitKey(1) < 0:
hasFrame, frame = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
frame = cv.resize(frame, [args.width, args.height])
# Inference of text detector
tm.start()
results = detector.infer(frame)
tm.stop()
latency_detector = tm.getFPS()
tm.reset()
# Inference of text recognizer
texts = []
tm.start()
for box, score in zip(results[0], results[1]):
result = np.hstack(
(box.reshape(8), score)
)
texts.append(
recognizer.infer(frame, result)
)
tm.stop()
latency_recognizer = tm.getFPS()
tm.reset()
# Draw results on the input image
frame = visualize(frame, results, texts)
cv.putText(frame, 'Latency - {}: {}'.format(detector.name, latency_detector), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
cv.putText(frame, 'Latency - {}: {}'.format(recognizer.name, latency_recognizer), (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
# Visualize results in a new Window
cv.imshow('{} Demo'.format(recognizer.name), frame) |