Spaces:
Runtime error
Runtime error
File size: 2,924 Bytes
3c4d877 2748201 3c4d877 6a8052b a00b28d e769983 ca99111 a00b28d e769983 a00b28d 3c4d877 3ab8166 6a8052b 3c4d877 1db9a6e cd196c7 7f9fd20 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 |
import streamlit as st
import gradio as gr
import cv2
import numpy as np
from tensorflow.keras.models import load_model
from tensorflow.keras.applications.vgg16 import preprocess_input
from tensorflow.keras.preprocessing import image
# Loading Models
# braintumor_model = load_model('models/brain_tumor_binary.h5')
braintumor_model = load_model('models/braintumor.h5')
# Configuring Streamlit
st.set_page_config(page_title="Brain Tumor Prediction App", page_icon=":brain:")
def preprocess_image(img):
# If it's a NumPy array, use it directly
if isinstance(img, np.ndarray):
img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
else:
# Convert Gradio image data to bytes
img_bytes = img.read()
# Convert to NumPy array
nparr = np.frombuffer(img_bytes, np.uint8)
# Decode image
img_gray = cv2.imdecode(nparr, cv2.IMREAD_GRAYSCALE)
# Crop and preprocess the grayscale image
img_processed = preprocess_imgs([img_gray], (224, 224))
return img_processed
# Handle binary decision
def binary_decision(confidence):
return 1 if confidence >= 0.5 else 0
def predict_braintumor(img):
# Preprocess the image
img_processed = preprocess_image(img)
# Make prediction
pred = braintumor_model.predict(img_processed)
# Handle binary decision
confidence = pred[0][0]
return "Brain Tumor Not Found!" if binary_decision(confidence) == 1 else "Brain Tumor Found!"
def preprocess_imgs(set_name, img_size):
set_new = []
for img in set_name:
img = cv2.resize(img, dsize=img_size, interpolation=cv2.INTER_CUBIC)
set_new.append(preprocess_input(img))
return np.array(set_new)
def crop_imgs(set_name, add_pixels_value=0):
set_new = []
for img in set_name:
gray = cv2.GaussianBlur(img, (5, 5), 0)
thresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]
thresh = cv2.erode(thresh, None, iterations=2)
thresh = cv2.dilate(thresh, None, iterations=2)
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
c = max(cnts, key=cv2.contourArea)
extLeft = tuple(c[c[:, :, 0].argmin()][0])
extRight = tuple(c[c[:, :, 0].argmax()][0])
extTop = tuple(c[c[:, :, 1].argmin()][0])
extBot = tuple(c[c[:, :, 1].argmax()][0])
ADD_PIXELS = add_pixels_value
new_img = img[extTop[1] - ADD_PIXELS:extBot[1] + ADD_PIXELS,
extLeft[0] - ADD_PIXELS:extRight[0] + ADD_PIXELS].copy()
set_new.append(new_img)
return np.array(set_new)
# Gradio interface
iface = gr.Interface(
fn=predict_braintumor,
inputs="image",
outputs="text",
examples=[["examples/1_no.jpeg"], ["examples/2_no.jpeg"], ["examples/3_no.jpg"], ["examples/Y1.jpg"], ["examples/Y2.jpg"], ["examples/Y3.jpg"]]
)
iface.launch()
|