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import gradio as gr
import fastai.vision.all as fv
from PIL import Image, ImageDraw
import skimage
import os
learn = fv.load_learner("model.pkl")
def call(image, step_size:int=100, blocks:int=4):
# print(image)
original_image = Image.fromarray(image).resize((400,400))
image = Image.new(mode='RGB', size=(step_size*blocks, step_size*blocks)) #, color=255
draw = ImageDraw.Draw(image)
for (x,y) in [ (x,y) for x in range(0, blocks * step_size, step_size) for y in range(0, blocks * step_size, step_size)]:
cropped_image = original_image.crop((x, y, x+step_size, y+step_size))
image.paste(cropped_image, (x,y))
prediction = learn.predict(cropped_image)
print(prediction)
marker = f"{prediction[0][0].upper()} {prediction[2][prediction[1].item()].item()*100:.0f}"
position = (x+10, y+10)
bbox = draw.textbbox(position, marker, font=None)
draw.rectangle(bbox, fill="white")
draw.text(position, marker, font=None, fill="black")
draw = ImageDraw.Draw(image)
for x in range(0, blocks * step_size, step_size):
# vertical line
line = ((x, 0), (x, blocks * step_size))
draw.line(line, fill=128, width=3)
# horizontal line
line = ((0, x), (blocks * step_size, x))
draw.line(line, fill=128, width=3)
return image
title = "Traffic Light Detector"
description = "Experiment traffic light detection to evaluate the value of captcha security controls"
iface = gr.Interface(fn=call,
inputs="image",
outputs="image",
title=title,
description=description,
examples=[
os.path.join(os.path.dirname(__file__), "examples/1.jpg"),
os.path.join(os.path.dirname(__file__), "examples/2.jpg")
],
thumbnail="thumbnail.webp")
iface.launch() |