File size: 1,647 Bytes
e4fcc21 b5effdf e4fcc21 b5effdf e4fcc21 b5effdf e4fcc21 |
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 |
import gradio as gr
from PIL import Image
import pytesseract
import yolov5
# Pytesseract init
choices = pytesseract.get_languages(config='')
def text_inference(filepath, languages):
return pytesseract.image_to_string(Image.open(filepath), lang=', '.join(languages))
# YOLOv5 Init
model = yolov5.load('nickgambirasi/yolov5s-recycling')
model.conf = 0.2
model.iou = 0.45
model.agnostic = False
model.multi_label = False
model.max_det = 1000
def yolo_inference(image):
results = model(Image.open(image), size=640)
predictions = results.pred[0]
scores = predictions[:, 4]
return scores
def full_inference(image, languages):
text_infer = text_inference(image, languages)
symbol_infer = yolo_inference(image)
output = {}
if text_infer:
output.update({'language_id': 'all_pass'})
output.update({'text': text_infer})
else:
output.update({'language_id': 'fail'})
if any(symbol_infer > model.conf):
output.update({'symbols': 'all_pass'})
else:
output.update({'symbols': 'fail'})
return output
title = "Hyperintelligent Art Parser"
description = "Gradio deployment of models for text and symbol detection on product labels, powered by Tesseract and YOLOv5"
gr.Interface(
full_inference,
[gr.inputs.Image(type="filepath", label="Upload Your Label"), gr.inputs.CheckboxGroup(choices, type="value", default=['eng'], label="Language of Text")],
'text',
title=title,
description=description,
article=None,
examples=None
).launch(enable_queue=True) |