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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
backends = [cv.dnn.DNN_BACKEND_OPENCV, cv.dnn.DNN_BACKEND_CUDA]
targets = [cv.dnn.DNN_TARGET_CPU, cv.dnn.DNN_TARGET_CUDA, cv.dnn.DNN_TARGET_CUDA_FP16]
help_msg_backends = "Choose one of the computation backends: {:d}: OpenCV implementation (default); {:d}: CUDA"
help_msg_targets = "Chose one of the target computation devices: {:d}: CPU (default); {:d}: CUDA; {:d}: CUDA fp16"
try:
backends += [cv.dnn.DNN_BACKEND_TIMVX]
targets += [cv.dnn.DNN_TARGET_NPU]
help_msg_backends += "; {:d}: TIMVX"
help_msg_targets += "; {:d}: NPU"
except:
print('This version of OpenCV does not support TIM-VX and NPU. Visit https://github.com/opencv/opencv/wiki/TIM-VX-Backend-For-Running-OpenCV-On-NPU for more information.')
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='Usage: Set path to the input image. Omit for using default camera.')
parser.add_argument('--model', '-m', type=str, default='text_recognition_CRNN_EN_2021sep.onnx', help='Usage: Set model path, defaults to text_recognition_CRNN_EN_2021sep.onnx.')
parser.add_argument('--backend', '-b', type=int, default=backends[0], help=help_msg_backends.format(*backends))
parser.add_argument('--target', '-t', type=int, default=targets[0], help=help_msg_targets.format(*targets))
parser.add_argument('--save', '-s', type=str, default=False, help='Usage: Set “True” to save a file with results. Invalid in case of camera input. Default will be set to “False”.')
parser.add_argument('--vis', '-v', type=str2bool, default=True, help='Usage: Default will be set to “True” and will open a new window to show results. Set to “False” to stop visualizations from being shown. Invalid in case of camera input.')
parser.add_argument('--width', type=int, default=736,
help='Preprocess input image by resizing to a specific width. It should be multiple by 32.')
parser.add_argument('--height', type=int, default=736,
help='Preprocess input image by resizing to a specific height. It should be multiple by 32.')
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_IC15_resnet18_2021sep.onnx',
inputSize=[args.width, args.height],
binaryThreshold=0.3,
polygonThreshold=0.5,
maxCandidates=200,
unclipRatio=2.0,
backendId=args.backend,
targetId=args.target
)
# If input is an image
if args.input is not None:
original_image = cv.imread(args.input)
original_w = original_image.shape[1]
original_h = original_image.shape[0]
scaleHeight = original_h / args.height
scaleWidth = original_w / args.width
image = cv.resize(original_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))
)
# Scale the results bounding box
for i in range(len(results[0])):
for j in range(4):
box = results[0][i][j]
results[0][i][j][0] = box[0] * scaleWidth
results[0][i][j][1] = box[1] * scaleHeight
# Draw results on the input image
original_image = visualize(original_image, results, texts)
# Save results if save is true
if args.save:
print('Resutls saved to result.jpg\n')
cv.imwrite('result.jpg', original_image)
# Visualize results in a new window
if args.vis:
cv.namedWindow(args.input, cv.WINDOW_AUTOSIZE)
cv.imshow(args.input, original_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, original_image = cap.read()
if not hasFrame:
print('No frames grabbed!')
break
original_w = original_image.shape[1]
original_h = original_image.shape[0]
scaleHeight = original_h / args.height
scaleWidth = original_w / args.width
frame = cv.resize(original_image, [args.width, args.height])
# Inference of text detector
tm.start()
results = detector.infer(frame)
tm.stop()
cv.putText(frame, 'Latency - {}: {:.2f}'.format(detector.name, tm.getFPS()), (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
tm.reset()
# Inference of text recognizer
if len(results[0]) and len(results[1]):
texts = []
tm.start()
for box, score in zip(results[0], results[1]):
result = np.hstack(
(box.reshape(8), score)
)
texts.append(
recognizer.infer(frame, box.reshape(8))
)
tm.stop()
cv.putText(frame, 'Latency - {}: {:.2f}'.format(recognizer.name, tm.getFPS()), (0, 30), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))
tm.reset()
# Scale the results bounding box
for i in range(len(results[0])):
for j in range(4):
box = results[0][i][j]
results[0][i][j][0] = box[0] * scaleWidth
results[0][i][j][1] = box[1] * scaleHeight
# Draw results on the input image
original_image = visualize(original_image, results, texts)
print(texts)
# Visualize results in a new Window
cv.imshow('{} Demo'.format(recognizer.name), original_image)
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