240 lines
9.1 KiB
Python
240 lines
9.1 KiB
Python
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'''
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Text detection model: https://github.com/argman/EAST
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Download link: https://www.dropbox.com/s/r2ingd0l3zt8hxs/frozen_east_text_detection.tar.gz?dl=1
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CRNN Text recognition model taken from here: https://github.com/meijieru/crnn.pytorch
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How to convert from pb to onnx:
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Using classes from here: https://github.com/meijieru/crnn.pytorch/blob/master/models/crnn.py
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More converted onnx text recognition models can be downloaded directly here:
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Download link: https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing
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And these models taken from here:https://github.com/clovaai/deep-text-recognition-benchmark
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import torch
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from models.crnn import CRNN
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model = CRNN(32, 1, 37, 256)
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model.load_state_dict(torch.load('crnn.pth'))
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dummy_input = torch.randn(1, 1, 32, 100)
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torch.onnx.export(model, dummy_input, "crnn.onnx", verbose=True)
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'''
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# Import required modules
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import numpy as np
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import cv2 as cv
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import math
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import argparse
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############ Add argument parser for command line arguments ############
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parser = argparse.ArgumentParser(
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description="Use this script to run TensorFlow implementation (https://github.com/argman/EAST) of "
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"EAST: An Efficient and Accurate Scene Text Detector (https://arxiv.org/abs/1704.03155v2)"
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"The OCR model can be obtained from converting the pretrained CRNN model to .onnx format from the github repository https://github.com/meijieru/crnn.pytorch"
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"Or you can download trained OCR model directly from https://drive.google.com/drive/folders/1cTbQ3nuZG-EKWak6emD_s8_hHXWz7lAr?usp=sharing")
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parser.add_argument('--input',
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help='Path to input image or video file. Skip this argument to capture frames from a camera.')
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parser.add_argument('--model', '-m', required=True,
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help='Path to a binary .pb file contains trained detector network.')
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parser.add_argument('--ocr', default="crnn.onnx",
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help="Path to a binary .pb or .onnx file contains trained recognition network", )
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parser.add_argument('--width', type=int, default=320,
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help='Preprocess input image by resizing to a specific width. It should be multiple by 32.')
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parser.add_argument('--height', type=int, default=320,
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help='Preprocess input image by resizing to a specific height. It should be multiple by 32.')
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parser.add_argument('--thr', type=float, default=0.5,
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help='Confidence threshold.')
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parser.add_argument('--nms', type=float, default=0.4,
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help='Non-maximum suppression threshold.')
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args = parser.parse_args()
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############ Utility functions ############
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def fourPointsTransform(frame, vertices):
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vertices = np.asarray(vertices)
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outputSize = (100, 32)
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targetVertices = np.array([
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[0, outputSize[1] - 1],
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[0, 0],
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[outputSize[0] - 1, 0],
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[outputSize[0] - 1, outputSize[1] - 1]], dtype="float32")
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rotationMatrix = cv.getPerspectiveTransform(vertices, targetVertices)
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result = cv.warpPerspective(frame, rotationMatrix, outputSize)
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return result
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def decodeText(scores):
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text = ""
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alphabet = "0123456789abcdefghijklmnopqrstuvwxyz"
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for i in range(scores.shape[0]):
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c = np.argmax(scores[i][0])
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if c != 0:
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text += alphabet[c - 1]
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else:
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text += '-'
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# adjacent same letters as well as background text must be removed to get the final output
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char_list = []
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for i in range(len(text)):
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if text[i] != '-' and (not (i > 0 and text[i] == text[i - 1])):
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char_list.append(text[i])
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return ''.join(char_list)
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def decodeBoundingBoxes(scores, geometry, scoreThresh):
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detections = []
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confidences = []
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############ CHECK DIMENSIONS AND SHAPES OF geometry AND scores ############
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assert len(scores.shape) == 4, "Incorrect dimensions of scores"
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assert len(geometry.shape) == 4, "Incorrect dimensions of geometry"
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assert scores.shape[0] == 1, "Invalid dimensions of scores"
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assert geometry.shape[0] == 1, "Invalid dimensions of geometry"
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assert scores.shape[1] == 1, "Invalid dimensions of scores"
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assert geometry.shape[1] == 5, "Invalid dimensions of geometry"
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assert scores.shape[2] == geometry.shape[2], "Invalid dimensions of scores and geometry"
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assert scores.shape[3] == geometry.shape[3], "Invalid dimensions of scores and geometry"
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height = scores.shape[2]
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width = scores.shape[3]
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for y in range(0, height):
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# Extract data from scores
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scoresData = scores[0][0][y]
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x0_data = geometry[0][0][y]
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x1_data = geometry[0][1][y]
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x2_data = geometry[0][2][y]
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x3_data = geometry[0][3][y]
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anglesData = geometry[0][4][y]
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for x in range(0, width):
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score = scoresData[x]
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# If score is lower than threshold score, move to next x
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if (score < scoreThresh):
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continue
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# Calculate offset
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offsetX = x * 4.0
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offsetY = y * 4.0
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angle = anglesData[x]
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# Calculate cos and sin of angle
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cosA = math.cos(angle)
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sinA = math.sin(angle)
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h = x0_data[x] + x2_data[x]
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w = x1_data[x] + x3_data[x]
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# Calculate offset
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offset = ([offsetX + cosA * x1_data[x] + sinA * x2_data[x], offsetY - sinA * x1_data[x] + cosA * x2_data[x]])
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# Find points for rectangle
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p1 = (-sinA * h + offset[0], -cosA * h + offset[1])
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p3 = (-cosA * w + offset[0], sinA * w + offset[1])
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center = (0.5 * (p1[0] + p3[0]), 0.5 * (p1[1] + p3[1]))
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detections.append((center, (w, h), -1 * angle * 180.0 / math.pi))
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confidences.append(float(score))
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# Return detections and confidences
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return [detections, confidences]
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def main():
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# Read and store arguments
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confThreshold = args.thr
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nmsThreshold = args.nms
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inpWidth = args.width
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inpHeight = args.height
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modelDetector = args.model
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modelRecognition = args.ocr
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# Load network
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detector = cv.dnn.readNet(modelDetector)
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recognizer = cv.dnn.readNet(modelRecognition)
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# Create a new named window
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kWinName = "EAST: An Efficient and Accurate Scene Text Detector"
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cv.namedWindow(kWinName, cv.WINDOW_NORMAL)
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outNames = []
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outNames.append("feature_fusion/Conv_7/Sigmoid")
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outNames.append("feature_fusion/concat_3")
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# Open a video file or an image file or a camera stream
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cap = cv.VideoCapture(args.input if args.input else 0)
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tickmeter = cv.TickMeter()
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while cv.waitKey(1) < 0:
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# Read frame
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hasFrame, frame = cap.read()
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if not hasFrame:
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cv.waitKey()
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break
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# Get frame height and width
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height_ = frame.shape[0]
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width_ = frame.shape[1]
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rW = width_ / float(inpWidth)
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rH = height_ / float(inpHeight)
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# Create a 4D blob from frame.
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blob = cv.dnn.blobFromImage(frame, 1.0, (inpWidth, inpHeight), (123.68, 116.78, 103.94), True, False)
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# Run the detection model
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detector.setInput(blob)
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tickmeter.start()
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outs = detector.forward(outNames)
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tickmeter.stop()
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# Get scores and geometry
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scores = outs[0]
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geometry = outs[1]
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[boxes, confidences] = decodeBoundingBoxes(scores, geometry, confThreshold)
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# Apply NMS
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indices = cv.dnn.NMSBoxesRotated(boxes, confidences, confThreshold, nmsThreshold)
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for i in indices:
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# get 4 corners of the rotated rect
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vertices = cv.boxPoints(boxes[i])
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# scale the bounding box coordinates based on the respective ratios
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for j in range(4):
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vertices[j][0] *= rW
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vertices[j][1] *= rH
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# get cropped image using perspective transform
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if modelRecognition:
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cropped = fourPointsTransform(frame, vertices)
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cropped = cv.cvtColor(cropped, cv.COLOR_BGR2GRAY)
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# Create a 4D blob from cropped image
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blob = cv.dnn.blobFromImage(cropped, size=(100, 32), mean=127.5, scalefactor=1 / 127.5)
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recognizer.setInput(blob)
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# Run the recognition model
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tickmeter.start()
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result = recognizer.forward()
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tickmeter.stop()
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# decode the result into text
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wordRecognized = decodeText(result)
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cv.putText(frame, wordRecognized, (int(vertices[1][0]), int(vertices[1][1])), cv.FONT_HERSHEY_SIMPLEX,
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0.5, (255, 0, 0))
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for j in range(4):
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p1 = (int(vertices[j][0]), int(vertices[j][1]))
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p2 = (int(vertices[(j + 1) % 4][0]), int(vertices[(j + 1) % 4][1]))
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cv.line(frame, p1, p2, (0, 255, 0), 1)
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# Put efficiency information
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label = 'Inference time: %.2f ms' % (tickmeter.getTimeMilli())
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cv.putText(frame, label, (0, 15), cv.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
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# Display the frame
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cv.imshow(kWinName, frame)
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tickmeter.reset()
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if __name__ == "__main__":
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main()
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