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| import os | |
| os.environ["CUDA_VISIBLE_DEVICES"] = "-1" | |
| import codecs | |
| import keras.backend.tensorflow_backend as tb | |
| tb._SYMBOLIC_SCOPE.value = True | |
| import numpy as np | |
| import gradio as gr | |
| import cv2 | |
| from PIL import Image | |
| from tensorflow.keras.models import load_model | |
| # Get model weights | |
| os.system("wget https://huggingface.co/hasibzunair/melanet/resolve/main/MelaNet.h5") | |
| # Load model | |
| model = None | |
| model = load_model("MelaNet.h5", compile=False) | |
| model.summary() | |
| # Class label list | |
| labels = ["Benign", "Malignant"] | |
| # Helpers | |
| def preprocess_image(img_array): | |
| # Normalize to [0,1] | |
| img_array = img_array.astype("float32") | |
| img_array /= 255 | |
| # Check that images are 2D arrays | |
| if len(img_array.shape) > 2: | |
| img_array = img_array[:, :, 0] | |
| # Convert to 3-channel | |
| img_array = np.stack((img_array, img_array, img_array), axis=-1) | |
| # Convert to array | |
| img_array = cv2.resize(img_array, (256, 256)) | |
| return img_array | |
| # Main inference function | |
| def inference(img_path): | |
| img = Image.open(img_path).convert("RGB") | |
| img = np.array(img) | |
| img = preprocess_image(img) | |
| img = np.expand_dims(img, 0) | |
| preds = model.predict(img) | |
| # Predict | |
| preds = model.predict(img)[0] | |
| labels_probs = {labels[i]: float(preds[i]) for i, _ in enumerate(labels)} | |
| return labels_probs | |
| title = "Melanoma Detection using Adversarial Training and Deep Transfer Learning" | |
| description = codecs.open("description.html", "r", "utf-8").read() | |
| article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2004.06824' target='_blank'>Melanoma Detection using Adversarial Training and Deep Transfer Learning</a> | <a href='https://github.com/hasibzunair/adversarial-lesions' target='_blank'>Github</a></p>" | |
| demo = gr.Interface( | |
| fn=inference, | |
| title=title, | |
| description=description, | |
| article=article, | |
| inputs=gr.inputs.Image(type="filepath", label="Input"), | |
| outputs="label", | |
| examples=[f"examples/{fname}.png" for fname in ["benign", "malignant"]], | |
| allow_flagging="never", | |
| analytics_enabled=False, | |
| ) | |
| demo.launch() | |