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import cv2 |
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from tensorflow.keras.models import load_model |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from PIL import Image |
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import gradio as gr |
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def recog(img): |
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model = load_model('hack36_2.h5') |
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img_array = np.asarray(img) |
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clone = img_array.copy() |
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clone_resized = cv2.resize(clone, (64,64)) |
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img_array=clone_resized/255 |
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img_final = np.expand_dims(img_array, axis=0) |
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prediction = model.predict(img_final).tolist()[0] |
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alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'space', 'space', 'space'] |
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return {alphabet[i]: prediction[i] for i in range(29)} |
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title = "ASL Fingerspelling Recognition" |
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desc = "" |
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input = gr.inputs.Image(type="pil", source="webcam") |
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output = gr.outputs.Label(num_top_classes=5) |
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examples = [ |
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["B_test.jpg"], |
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["C_test.jpg"], |
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["Y_test.jpg"] |
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] |
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iface = gr.Interface( |
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fn=recog, |
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title=title, |
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description=desc, |
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examples=examples, |
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inputs=input, |
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outputs=output |
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) |
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iface.launch() |
