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import streamlit as st |
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import cv2 |
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import numpy as np |
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import pydicom |
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import tensorflow as tf |
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import keras |
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from pydicom.dataset import Dataset, FileDataset |
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from pydicom.uid import generate_uid |
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from google.cloud import storage |
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import os |
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import io |
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from PIL import Image |
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import uuid |
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import pandas as pd |
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import tensorflow as tf |
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from datetime import datetime |
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from tensorflow import image |
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from tensorflow.python.keras.models import load_model |
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from keras.applications.densenet import DenseNet121 |
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from keras.layers import Dense, GlobalAveragePooling2D |
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from keras.models import Model |
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from pydicom.pixel_data_handlers.util import apply_voi_lut |
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os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = "./da-kalbe-63ee33c9cdbb.json" |
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bucket_name = "da-kalbe-ml-result-png" |
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storage_client = storage.Client() |
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bucket_result = storage_client.bucket(bucket_name) |
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bucket_name_load = "da-ml-models" |
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bucket_load = storage_client.bucket(bucket_name_load) |
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st.sidebar.title("Configuration") |
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uploaded_file = st.sidebar.file_uploader("Upload Original Image", type=["png", "jpg", "jpeg", "dcm"]) |
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enhancement_type = st.sidebar.selectbox( |
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"Enhancement Type", |
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["Invert", "High Pass Filter", "Unsharp Masking", "Histogram Equalization", "CLAHE"] |
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) |
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st.sidebar.title("Detection") |
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uploaded_detection = st.sidebar.file_uploader("Upload image to detect", type=["png", "jpg", "jpeg", "dcm"]) |
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H_detection = 224 |
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W_detection = 224 |
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@st.cache_resource |
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def load_model_detection(): |
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model_detection = tf.keras.models.load_model("model-detection.h5", compile=False) |
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model_detection.compile( |
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loss={ |
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"bbox": "mse", |
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"class": "sparse_categorical_crossentropy" |
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}, |
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optimizer=tf.keras.optimizers.Adam(), |
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metrics={ |
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"bbox": ['mse'], |
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"class": ['accuracy'] |
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} |
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) |
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return model_detection |
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def preprocess_image(image): |
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""" Preprocess the image to the required size and normalization. """ |
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image = cv2.resize(image, (W_detection, H_detection)) |
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image = (image - 127.5) / 127.5 |
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image = np.expand_dims(image, axis=0).astype(np.float32) |
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return image |
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def predict(model_detection, image): |
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""" Predict bounding box and label for the input image. """ |
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pred_bbox, pred_class = model_detection.predict(image) |
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pred_label_confidence = np.max(pred_class, axis=1)[0] |
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pred_label = np.argmax(pred_class, axis=1)[0] |
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return pred_bbox[0], pred_label, pred_label_confidence |
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def draw_bbox(image, bbox): |
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""" Draw bounding box on the image. """ |
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h, w, _ = image.shape |
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x1, y1, x2, y2 = bbox |
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x1, y1, x2, y2 = int(x1 * w), int(y1 * h), int(x2 * w), int(y2 * h) |
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image = cv2.rectangle(image, (x1, y1), (x2, y2), (255, 0, 0), 2) |
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return image |
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st.title("AI INTEGRATION FOR CHEST X-RAY") |
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st.header("IMAGE ENHANCEMENT") |
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@st.cache_resource |
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def load_gradcam_model(): |
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model = keras.models.load_model('./model_renamed.h5', compile=False) |
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return model |
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def upload_to_gcs(image_data: io.BytesIO, filename: str, content_type='application/dicom'): |
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"""Uploads an image to Google Cloud Storage.""" |
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try: |
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blob = bucket_result.blob(filename) |
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blob.upload_from_file(image_data, content_type=content_type) |
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st.write("File ready to be seen in OHIF Viewer.") |
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except Exception as e: |
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st.error(f"An unexpected error occurred: {e}") |
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def load_dicom_from_gcs(file_name: str = "dicom_00000001_000.dcm"): |
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blob = bucket_load.blob(file_name) |
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dicom_bytes = blob.download_as_bytes() |
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dicom_stream = io.BytesIO(dicom_bytes) |
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ds = pydicom.dcmread(dicom_stream) |
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return ds |
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def png_to_dicom(image_path: str, image_name: str, dicom: str = None): |
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if dicom is None: |
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ds = load_dicom_from_gcs() |
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else: |
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ds = load_dicom_from_gcs(dicom) |
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jpg_image = Image.open(image_path) |
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print("Image Mode:", jpg_image.mode) |
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if jpg_image.mode == 'L': |
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np_image = np.array(jpg_image.getdata(), dtype=np.uint8) |
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ds.Rows = jpg_image.height |
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ds.Columns = jpg_image.width |
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ds.PhotometricInterpretation = "MONOCHROME1" |
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ds.SamplesPerPixel = 1 |
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ds.BitsStored = 8 |
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ds.BitsAllocated = 8 |
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ds.HighBit = 7 |
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ds.PixelRepresentation = 0 |
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ds.PixelData = np_image.tobytes() |
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ds.save_as(image_name) |
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elif jpg_image.mode == 'RGBA': |
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np_image = np.array(jpg_image.getdata(), dtype=np.uint8)[:, :3] |
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ds.Rows = jpg_image.height |
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ds.Columns = jpg_image.width |
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ds.PhotometricInterpretation = "RGB" |
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ds.SamplesPerPixel = 3 |
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ds.BitsStored = 8 |
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ds.BitsAllocated = 8 |
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ds.HighBit = 7 |
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ds.PixelRepresentation = 0 |
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ds.PixelData = np_image.tobytes() |
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ds.save_as(image_name) |
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elif jpg_image.mode == 'RGB': |
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np_image = np.array(jpg_image.getdata(), dtype=np.uint8)[:, :3] |
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ds.Rows = jpg_image.height |
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ds.Columns = jpg_image.width |
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ds.PhotometricInterpretation = "RGB" |
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ds.SamplesPerPixel = 3 |
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ds.BitsStored = 8 |
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ds.BitsAllocated = 8 |
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ds.HighBit = 7 |
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ds.PixelRepresentation = 0 |
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ds.PixelData = np_image.tobytes() |
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ds.save_as(image_name) |
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else: |
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raise ValueError("Unsupported image mode:", jpg_image.mode) |
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return ds |
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def save_dicom_to_bytes(dicom): |
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dicom_bytes = io.BytesIO() |
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dicom.save_as(dicom_bytes) |
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dicom_bytes.seek(0) |
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return dicom_bytes |
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def upload_folder_images(original_image_path, enhanced_image_path): |
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folder_name = os.path.splitext(uploaded_file.name)[0] |
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bucket_result.blob(folder_name + '/').upload_from_string('', content_type='application/x-www-form-urlencoded') |
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enhancement_name = enhancement_type.split('_')[-1] |
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original_dicom = png_to_dicom(original_image_path, "original_image.dcm") |
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enhanced_dicom = png_to_dicom(enhanced_image_path, enhancement_name + ".dcm") |
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original_dicom_bytes = io.BytesIO() |
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enhanced_dicom_bytes = io.BytesIO() |
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original_dicom.save_as(original_dicom_bytes) |
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enhanced_dicom.save_as(enhanced_dicom_bytes) |
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original_dicom_bytes.seek(0) |
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enhanced_dicom_bytes.seek(0) |
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upload_to_gcs(original_dicom_bytes, folder_name + '/' + 'original_image.dcm', content_type='application/dicom') |
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upload_to_gcs(enhanced_dicom_bytes, folder_name + '/' + enhancement_name + '.dcm', content_type='application/dicom') |
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def get_mean_std_per_batch(image_path, H=320, W=320): |
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sample_data = [] |
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for idx, img in enumerate(df.sample(100)["Image Index"].values): |
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sample_data.append( |
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np.array(keras.utils.load_img(image_path, target_size=(H, W)))) |
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mean = np.mean(sample_data[0]) |
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std = np.std(sample_data[0]) |
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return mean, std |
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def load_image(img_path, preprocess=True, height=320, width=320): |
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mean, std = get_mean_std_per_batch(img_path, height, width) |
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x = keras.utils.load_img(img_path, target_size=(height, width)) |
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x = keras.utils.img_to_array(x) |
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if preprocess: |
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x -= mean |
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x /= std |
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x = np.expand_dims(x, axis=0) |
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return x |
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def grad_cam(input_model, img_array, cls, layer_name): |
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grad_model = tf.keras.models.Model( |
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[input_model.inputs], |
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[input_model.get_layer(layer_name).output, input_model.output] |
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) |
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with tf.GradientTape() as tape: |
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conv_outputs, predictions = grad_model(img_array) |
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loss = predictions[:, cls] |
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output = conv_outputs[0] |
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grads = tape.gradient(loss, conv_outputs)[0] |
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gate_f = tf.cast(output > 0, 'float32') |
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gate_r = tf.cast(grads > 0, 'float32') |
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guided_grads = gate_f * gate_r * grads |
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weights = tf.reduce_mean(guided_grads, axis=(0, 1)) |
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cam = np.dot(output, weights) |
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for index, w in enumerate(weights): |
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cam += w * output[:, :, index] |
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cam = cv2.resize(cam.numpy(), (320, 320), cv2.INTER_LINEAR) |
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cam = np.maximum(cam, 0) |
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cam = cam / cam.max() |
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return cam |
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def compute_gradcam(model_gradcam, img_path, layer_name='bn'): |
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model_gradcam = load_gradcam_model() |
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preprocessed_input = load_image(img_path) |
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predictions = model_gradcam.predict(preprocessed_input) |
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original_image = load_image(img_path, preprocess=False) |
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labels = ['Cardiomegaly', 'Emphysema', 'Effusion', 'Hernia', 'Infiltration', 'Mass', |
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'Nodule', 'Atelectasis', 'Pneumothorax', 'Pleural_Thickening', |
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'Pneumonia', 'Fibrosis', 'Edema', 'Consolidation'] |
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for i in range(len(labels)): |
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st.write(f"Generating gradcam for class {labels[i]}") |
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gradcam = grad_cam(model_gradcam, preprocessed_input, i, layer_name) |
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gradcam = (gradcam * 255).astype(np.uint8) |
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gradcam = cv2.applyColorMap(gradcam, cv2.COLORMAP_JET) |
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gradcam = cv2.addWeighted(gradcam, 0.5, original_image.squeeze().astype(np.uint8), 0.5, 0) |
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st.image(gradcam, caption=f"{labels[i]}: p={predictions[0][i]:.3f}", use_column_width=True) |
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def calculate_mse(original_image, enhanced_image): |
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mse = np.mean((original_image - enhanced_image) ** 2) |
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return mse |
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def calculate_psnr(original_image, enhanced_image): |
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mse = calculate_mse(original_image, enhanced_image) |
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if mse == 0: |
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return float('inf') |
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max_pixel_value = 255.0 |
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psnr = 20 * np.log10(max_pixel_value / np.sqrt(mse)) |
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return psnr |
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def calculate_maxerr(original_image, enhanced_image): |
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maxerr = np.max((original_image - enhanced_image) ** 2) |
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return maxerr |
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def calculate_l2rat(original_image, enhanced_image): |
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l2norm_ratio = np.sum(original_image ** 2) / np.sum((original_image - enhanced_image) ** 2) |
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return l2norm_ratio |
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def process_image(original_image, enhancement_type, fix_monochrome=True): |
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if fix_monochrome and original_image.shape[-1] == 3: |
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original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY) |
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image = original_image - np.min(original_image) |
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image = image / np.max(original_image) |
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image = (image * 255).astype(np.uint8) |
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enhanced_image = enhance_image(image, enhancement_type) |
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mse = calculate_mse(original_image, enhanced_image) |
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psnr = calculate_psnr(original_image, enhanced_image) |
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maxerr = calculate_maxerr(original_image, enhanced_image) |
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l2rat = calculate_l2rat(original_image, enhanced_image) |
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return enhanced_image, mse, psnr, maxerr, l2rat |
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def apply_clahe(image): |
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clahe = cv2.createCLAHE(clipLimit=40.0, tileGridSize=(8, 8)) |
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return clahe.apply(image) |
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def invert(image): |
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return cv2.bitwise_not(image) |
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def hp_filter(image, kernel=None): |
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if kernel is None: |
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kernel = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]]) |
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return cv2.filter2D(image, -1, kernel) |
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def unsharp_mask(image, radius=5, amount=2): |
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def usm(image, radius, amount): |
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blurred = cv2.GaussianBlur(image, (0, 0), radius) |
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sharpened = cv2.addWeighted(image, 1.0 + amount, blurred, -amount, 0) |
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return sharpened |
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return usm(image, radius, amount) |
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def hist_eq(image): |
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return cv2.equalizeHist(image) |
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def enhance_image(image, enhancement_type): |
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if enhancement_type == "Invert": |
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return invert(image) |
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elif enhancement_type == "High Pass Filter": |
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return hp_filter(image) |
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elif enhancement_type == "Unsharp Masking": |
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return unsharp_mask(image) |
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elif enhancement_type == "Histogram Equalization": |
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return hist_eq(image) |
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elif enhancement_type == "CLAHE": |
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return apply_clahe(image) |
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else: |
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raise ValueError(f"Unknown enhancement type: {enhancement_type}") |
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def redirect_button(url): |
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button = st.button('Go to OHIF Viewer') |
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if button: |
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st.markdown(f'<meta http-equiv="refresh" content="0;url={url}" />', unsafe_allow_html=True) |
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if uploaded_file is not None: |
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if hasattr(uploaded_file, 'name'): |
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file_extension = uploaded_file.name.split(".")[-1] |
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if file_extension.lower() == "dcm": |
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dicom_data = pydicom.dcmread(uploaded_file) |
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pixel_array = dicom_data.pixel_array |
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metadata = {elem.keyword: elem.value for elem in dicom_data if elem.keyword} |
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metadata_dict = {str(key): str(value) for key, value in metadata.items()} |
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df = pd.DataFrame.from_dict(metadata_dict, orient='index', columns=['Value']) |
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with st.expander("Lihat Metadata"): |
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st.write("Metadata:") |
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st.dataframe(df) |
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pixel_array = dicom_data.pixel_array |
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img_array = pixel_array.astype(float) |
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img_array = (np.maximum(img_array, 0) / img_array.max()) * 255.0 |
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img_array = np.uint8(img_array) |
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img = Image.fromarray(img_array) |
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file_bytes = np.asarray(bytearray(uploaded_detection.read()), dtype=np.uint8) |
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image = cv2.imdecode(file_bytes, 1) |
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col1, col2 = st.columns(2) |
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if img_array.ndim == 3: |
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n_slices = img_array.shape[0] |
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if n_slices > 1: |
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slice_ix = st.sidebar.slider('Slice', 0, n_slices - 1, int(n_slices / 2)) |
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st.image(img_array[slice_ix, :, :], caption=f"Slice {slice_ix}", use_column_width=True) |
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else: |
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st.image(img_array[0, :, :], caption="Single Slice Image", use_column_width=True) |
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elif img_array.ndim == 2: |
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with col1: |
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st.image(img_array, caption="Original Image", use_column_width=True) |
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else: |
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st.error("Unsupported image dimensions") |
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original_image = img_array |
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if len(pixel_array.shape) > 2: |
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pixel_array = pixel_array[:, :, 0] |
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enhanced_image, mse, psnr, maxerr, l2rat = process_image(pixel_array, enhancement_type) |
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else: |
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original_image = np.array(keras.utils.load_img(uploaded_file, color_mode='rgb' if enhancement_type == "Invert" else 'grayscale')) |
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enhanced_image, mse, psnr, maxerr, l2rat = process_image(original_image, enhancement_type) |
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col1, col2 = st.columns(2) |
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with col1: |
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st.image(original_image, caption="Original Image", use_column_width=True) |
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with col2: |
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st.image(enhanced_image, caption='Enhanced Image', use_column_width=True) |
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col1, col2 = st.columns(2) |
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col3, col4 = st.columns(2) |
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col1.metric("MSE", round(mse,3)) |
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col2.metric("PSNR", round(psnr,3)) |
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col3.metric("Maxerr", round(maxerr,3)) |
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col4.metric("L2Rat", round(l2rat,3)) |
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enhanced_image_path = "enhanced_image.png" |
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cv2.imwrite(enhanced_image_path, enhanced_image) |
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enhanced_image_path = "enhanced_image.png" |
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cv2.imwrite(enhanced_image_path, enhanced_image) |
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original_image_path = "original_image.png" |
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cv2.imwrite(original_image_path, original_image) |
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col1, col2, col3 = st.columns(3) |
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with col1: |
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redirect_button("https://new-ohif-viewer-k7c3gdlxua-et.a.run.app/") |
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with col2: |
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if st.button('Generate Grad-CAM'): |
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model=load_gradcam_model() |
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st.write("Generating Grad-CAM visualizations") |
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try: |
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compute_gradcam(model_gradcam, uploaded_file) |
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except Exception as e: |
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st.error(f"Error generating Grad-CAM: {e}") |
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st.header("Chest X-ray Disease Detection") |
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st.write("Upload a chest X-ray image and click on 'Detect' to find out if there's any disease.") |
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model_detection = load_model_detection() |
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if uploaded_detection is not None: |
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file_bytes = np.asarray(bytearray(uploaded_detection.read()), dtype=np.uint8) |
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image = cv2.imdecode(file_bytes, 1) |
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if st.button('Detect'): |
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st.write("Processing...") |
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input_image = preprocess_image(image) |
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pred_bbox, pred_label, pred_label_confidence = predict(model_detection, input_image) |
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label_mapping = { |
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0: 'Atelectasis', |
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1: 'Cardiomegaly', |
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2: 'Effusion', |
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3: 'Infiltrate', |
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4: 'Mass', |
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5: 'Nodule', |
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6: 'Pneumonia', |
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7: 'Pneumothorax' |
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} |
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if pred_label_confidence < 0.2: |
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st.write("May not detect a disease.") |
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else: |
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pred_label_name = label_mapping[pred_label] |
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st.write(f"Prediction Label: {pred_label_name}") |
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st.write(f"Prediction Bounding Box: {pred_bbox}") |
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st.write(f"Prediction Confidence: {pred_label_confidence:.2f}") |
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output_image = draw_bbox(image.copy(), pred_bbox) |
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st.image(output_image, caption='Detected Image.', use_column_width=True) |