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import gradio as gr
import numpy as np
import os
import json
import cv2
import sys
import torch
import torch.nn as nn
import torchvision
sys.path.append(os.getcwd())
from src.predict import *
def visualize_image(image, detections):
for bbox, detected_class, conf, text, _ in detections:
x1, y1, x2, y2 = bbox
x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)
image = cv2.rectangle(image, (x1, y1), (x2, y2), color=(255, 0, 0), thickness=2)
image = cv2.putText(image, f"{conf:.2f} {text}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 2)
return image
def pipeline(image):
image = np.array(image)
predictions = prediction(image)
# Filter low conf boxes
filter_predictions = []
dict_predictions = {}
num_textbox = 1
for bbox, cls, conf, text, encoded_text in predictions:
if conf > 0.7:
filter_predictions.append([bbox, cls, conf, text, encoded_text])
xmin, ymin, xmax, ymax = bbox
xmin, ymin, xmax, ymax = int(xmin), int(ymin), int(xmax), int(ymax)
dict_predictions.update({
f"textbox {num_textbox}":{
"bounding box": str([xmin, ymin, xmax, ymax]),
"conf": np.round(conf, 2),
"text": text
}
})
num_textbox += 1
image = visualize_image(image, filter_predictions)
return image, json.dumps(dict_predictions, indent=5)
demo = gr.Interface(
fn=pipeline,
inputs=gr.Image(type="pil", label="Input Image"),
outputs=[
gr.Image(type="pil", label="Output Image"),
gr.Textbox(type="text", label="Recognized Text")
],
title="Scene Text Recognization",
description="Recognize text in scene images"
)
demo.launch(share=True)
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