Spaces:
Runtime error
Runtime error
File size: 3,001 Bytes
e7fc64a 3d3c04e e7fc64a 61be565 fc3b467 61be565 e7fc64a 61be565 e7fc64a fc3b467 61be565 e7fc64a 61be565 e7fc64a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
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='craft_text_detector'):
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') ],
inputs = [ gr.Image(label="Input") ],
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) |