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| import PIL.Image | |
| import gradio as gr | |
| import torch | |
| import numpy as np | |
| def detect_with_craft_text_detector(image: np.ndarray): | |
| from craft_text_detector import Craft | |
| craft = Craft(output_dir='output', crop_type="box", cuda=torch.cuda.is_available(), export_extra=True) | |
| result = craft.detect_text( image) | |
| annotated = PIL.Image.open('output/image_text_detection.png') # image with boxes displayed | |
| return annotated, result['boxes'], is_signature(result['boxes_as_ratios']) | |
| def detect_with_craft_hw_ocr(image: np.ndarray): | |
| from craft_hw_ocr import OCR | |
| ocr = OCR.load_models() | |
| image, results = OCR.detection(image, ocr[2]) | |
| bboxes, _ = OCR.recoginition(image, results, ocr[0], ocr[1]) | |
| h,w,_=np.shape(image) # third dimension is color channel | |
| annotated = OCR.visualize(image, results) | |
| m=(np.asarray([w,h]))[np.newaxis,np.newaxis,:] | |
| return annotated, bboxes, is_signature(bboxes/m) | |
| def process(image:np.ndarray, lib:str): | |
| if image is None: | |
| return None,'','' | |
| annotated, boxes, signed = detect_with_craft_text_detector(image) if lib=='craft_text_detector' else detect_with_craft_hw_ocr( image) | |
| return annotated, len(boxes), signed | |
| dw=0.3 # width ratio | |
| dh=0.25 | |
| def is_nw(box): | |
| """ | |
| A box happen to be a 4-pixel list in order | |
| 1 -- 2 | |
| 4 -- 3 | |
| """ | |
| return box[2][0]<=dw and box[2][1]<= dh | |
| def is_ne(box): | |
| return box[3][0]>=1-dw and box[3][1]<= dh | |
| def is_se(box): | |
| return box[0][0]>=1-dw and box[0][1]>= 1-dh | |
| def is_sw(box): | |
| return box[1][0]<=dw and box[1][1]>= 1-dh | |
| def is_corner(box)->bool: | |
| """ @:returns true if the box is located in any corner """ | |
| return is_nw(box) or is_ne(box) or is_se(box) or is_sw(box) | |
| dhhf=0.2 # dh for header and footer | |
| def is_footer(box)->bool: | |
| """ true if for the 2 first points, y>0.8 """ | |
| return box[0][1]>=1-dhhf and box[1][1]>=1-dhhf | |
| def is_header(box)->bool: | |
| """ true if for the 2 last points, y<0.2 """ | |
| return box[2][1]<=dhhf and box[3][1]<=dhhf | |
| # def is_signature(prediction_result) -> bool: | |
| def is_signature(boxes) -> bool: | |
| """ true if any of the boxes is at any corner, or header or footer """ | |
| for box in boxes: | |
| if box[1][0]-box[0][0]<0.05: # not large enough | |
| continue | |
| if is_corner(box) or is_header(box) or is_footer(box): | |
| return True | |
| return False | |
| gr.Interface( | |
| fn = process, | |
| inputs = [ gr.Image(label="Input"), gr.inputs.Radio(label='Model', choices=["craft_text_detector", "craft_hw_ocr"], default='craft_text_detector') ], | |
| outputs = [ gr.Image(label="Output"), gr.Label(label="nb of text detections"), gr.Label(label="Has signature") ], | |
| title="Detect signature in image", | |
| description="Is the photo or image watermarked by a signature?", | |
| examples=[['data/photologo-1-1.jpg'], ['data/times-square.jpg'], ['data/photologo-3.jpg']], | |
| allow_flagging="never" | |
| ).launch(debug=True, enable_queue=True) |