initial commit

.gitignore

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+.venv/**

app.py

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+import gradio as gr
+import fastai.vision.all as fv
+from PIL import Image, ImageDraw
+import skimage
+
+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)
+iface.launch()

model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e34e8f433c7ea43cb69797f534eb30f4c73f29571620742c3cad7abcd32e0100
+size 46956998

requirements.txt

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+fastai
+scikit-image