Add sketch recognition functionality with TensorFlow and OpenCV
Browse files- app.py +44 -4
- requirements.txt +2 -0
- sketch_recognition_numbers_model.h5 +3 -0
app.py
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import gradio as gr
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# import dependencies
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import gradio as gr
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import tensorflow as tf
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import cv2
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# app title
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title = "Welcome on your first sketch recognition app!"
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# app description
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head = (
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"<center>"
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"<img src='file/mnist-classes.png' width=400>"
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"The robot was trained to classify numbers (from 0 to 9). To test it, write your number in the space provided."
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"</center>"
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)
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# GitHub repository link
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ref = "Find the whole code [here](https://github.com/ovh/ai-training-examples/tree/main/apps/gradio/sketch-recognition)."
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# image size: 28x28
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img_size = 28
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# classes name (from 0 to 9)
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labels = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
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# load model (trained on MNIST dataset)
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model = tf.keras.models.load_model("./sketch_recognition_numbers_model.h5")
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# prediction function for sketch recognition
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def predict(img):
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# image shape: 28x28x1
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img = cv2.resize(img, (img_size, img_size))
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img = img.reshape(1, img_size, img_size, 1)
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# model predictions
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preds = model.predict(img)[0]
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# return the probability for each classe
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return {label: float(pred) for label, pred in zip(labels, preds)}
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# top 3 of classes
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label = gr.outputs.Label(num_top_classes=3)
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# open Gradio interface for sketch recognition
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interface = gr.Interface(fn=predict, inputs="sketchpad", outputs=label, title=title, description=head, article=ref)
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interface.launch(server_name="0.0.0.0", server_port=8080)
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requirements.txt
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tensorflow
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opencv-python
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sketch_recognition_numbers_model.h5
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version https://git-lfs.github.com/spec/v1
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oid sha256:044fd13c73edbf776ec5d7f9aa77c87e42f4c498ad02425628a96e425c6b51f2
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size 1245272
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