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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import cv2
# st.markdown(
# """
# <style>
# .stApp {
# background-image:url( "https://cdn.discordapp.com/attachments/1086260179139579955/1099734972971102298/img.png");
# background-size: cover;
# }
# </style>
# """,
# unsafe_allow_html=True
# )
labels = ['Actinic Keratoses', 'Basal Cell Carcinoma', 'Benign Keratosis-like Lesions', 'Dermatofibroma', 'Melanoma', 'Melanocytic Nevi', 'Vascular Lesions']
model = tf.keras.models.load_model('front_model_resnet.h5')
classify_model=tf.lite.Interpreter(model_path="InceptionResNetV2Skripsi.tflite")
classify_model.allocate_tensors()
input_details = classify_model.get_input_details()
output_details = classify_model.get_output_details()
def detect_skin(image):
# Convert the image to YCrCb color space
ycrcb = cv2.cvtColor(image, cv2.COLOR_BGR2YCrCb)
# Apply skin color detection algorithm
lower_skin = np.array([0, 133, 77], dtype=np.uint8)
upper_skin = np.array([255, 173, 127], dtype=np.uint8)
mask = cv2.inRange(ycrcb, lower_skin, upper_skin)
# Apply morphological transformations to remove noise
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
mask = cv2.erode(mask, kernel, iterations=2)
mask = cv2.dilate(mask, kernel, iterations=2)
# Count the number of skin pixels
num_skin_pixels = cv2.countNonZero(mask)
# Calculate the ratio of skin pixels to total pixels
ratio = num_skin_pixels / (image.shape[0] * image.shape[1])
return ratio
def resize_image(image):
# Resize the image to 150x150 pixels
resized_image = tf.image.resize(image, [150, 150])
return resized_image.numpy()
def classify_image1(image):
# Pre-process the input image
resized_image = resize_image(image)
input_data = np.expand_dims(resized_image, axis=0).astype(np.float32)
classify_model.set_tensor(input_details[0]['index'], input_data)
# Run inference
with st.spinner('Classifying...'):
classify_model.invoke()
# Get the output probabilities
output_data = classify_model.get_tensor(output_details[0]['index'])
return output_data[0]
def classify_image(img, model):
image=img
img = img.resize((224, 224)) # Resize the image to match the model input size
img_array = np.array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
prediction = model.predict(img_array)
if prediction[0][0] > 0.5:
st.write("The image is classified as class Cancer")
# image = np.array(Image.open(image))
# st.image(image, width=150)
# Run inference on the input image
probs = classify_image1(image)
# # Display the top 3 predictions
top_3_indices = np.argsort(probs)[::-1][:3]
st.write("Top 3 predictions:")
for i in range(3):
st.write("%d. %s (%.2f%%)" % (i + 1, labels[top_3_indices[i]], probs[top_3_indices[i]] * 100))
ind=probs.argmax()
st.write("The Most possible label Will be:",labels[ind])
else:
st.write("The image is classified as class non cancer")
# Load the pre-trained model
model = tf.keras.models.load_model('front_model_resnet.h5')
classify_model=tf.lite.Interpreter(model_path="InceptionResNetV2Skripsi.tflite")
classify_model.allocate_tensors()
# Define the Streamlit app
st.title("Skin Cancer Detection")
st.sidebar.title('Input Image')
st.sidebar.markdown('Upload an image of a skin lesion to make a prediction.')
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png","HEIC"])
if uploaded_file is not None:
image = cv2.imdecode(np.fromstring(uploaded_file.read(), np.uint8), 1)
image = cv2.resize(image, (500, 500))
# image = cv2.resize(image, (224, 224))
# Detect skin in the image
ratio = detect_skin(image)
# Display the result
# st.image(image, caption="Uploaded Image", use_column_width=True)
st.write(f"Ratio of skin pixels to total pixels: {ratio:.2f}")
if ratio > 0.4:
st.write("The image contains skin.")
image = Image.open(uploaded_file)
st.image(image, width=300)
st.write("")
st.write("Classifying...")
label = classify_image(image, model)
else:
st.write("The image does not contain skin.")
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