| import requests | |
| import gradio as gr | |
| import pandas as pd | |
| from PIL import Image, ImageDraw | |
| def infer(im): | |
| im.save('converted.png') | |
| url = 'https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition' | |
| files = { | |
| 'image': ('converted.png', open('converted.png', 'rb'), 'image/png'), | |
| 'model_type': (None, 'detection'), | |
| 'media_type': (None, 'photo'), | |
| } | |
| r = requests.post(url, files=files) | |
| res = r.json() | |
| print(r, res) | |
| img = im.convert('RGB') | |
| words = [] | |
| draw = ImageDraw.Draw(img,'RGBA') | |
| for output in res['response']['output']: | |
| for poly in output['bounding_poly']: | |
| words += [c['class'] for c in poly['classes']] | |
| draw.rectangle((poly['dimensions']['left']-2,poly['dimensions']['top']-2,poly['dimensions']['right']+2,poly['dimensions']['bottom']+2), outline=(0,255,0,255), fill=(0,255,0,50),width=2) | |
| img.save('result.png') | |
| return 'result.png', '\n'.join([o['block_text'] for o in res['response']['output']]), pd.DataFrame(words) | |
| iface = gr.Interface( | |
| fn=infer, | |
| title="Hive OCR", | |
| description="Demo for Hive OCR.Transcribe and analyze media depicting typed, written, or graphic text", | |
| inputs=[gr.inputs.Image(label='image', type='pil')], | |
| outputs=['image', 'text', gr.outputs.Dataframe(headers=['word'])], | |
| examples=['testocr.png', 'receipt.webp', '20131216170659.jpg'], | |
| article="<a href=\"https://thehive.ai/hive-ocr-solutions\">Hive OCR</a>", | |
| ).launch() |