Add Files

main.py

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+import streamlit as st
+from detectron2.config import get_cfg
+from detectron2.engine import DefaultPredictor
+from detectron2 import model_zoo
+from PIL import Image
+import numpy as np
+
+from util import visualize, set_background
+
+
+set_background('bg.png')
+
+
+# set title
+st.title('Brain MRI tumor detection')
+
+# set header
+st.header('Please upload an image')
+
+# upload file
+file = st.file_uploader('', type=['png', 'jpg', 'jpeg'])
+
+# load model
+cfg = get_cfg()
+cfg.merge_from_file(model_zoo.get_config_file('COCO-Detection/retinanet_R_101_FPN_3x.yaml'))
+cfg.MODEL.WEIGHTS = 'model_0001999.pth'
+cfg.MODEL.DEVICE = 'cpu'
+
+predictor = DefaultPredictor(cfg)
+
+# load image
+if file:
+    image = Image.open(file).convert('RGB')
+
+    image_array = np.asarray(image)
+
+    # detect objects
+    outputs = predictor(image_array)
+
+    threshold = 0.5
+
+    # Display predictions
+    preds = outputs["instances"].pred_classes.tolist()
+    scores = outputs["instances"].scores.tolist()
+    bboxes = outputs["instances"].pred_boxes
+
+    bboxes_ = []
+    for j, bbox in enumerate(bboxes):
+        bbox = bbox.tolist()
+
+        score = scores[j]
+        pred = preds[j]
+
+        if score > threshold:
+            x1, y1, x2, y2 = [int(i) for i in bbox]
+            bboxes_.append([x1, y1, x2, y2])
+
+    # visualize
+    visualize(image, bboxes_)

model_0001999.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ea65ddcf276040cb0d20480482d5d8e8964deccfc18641ce583bf5ee99f97262
+size 455037799

requirements.txt

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+streamlit==1.23.1
+Pillow==9.5.0
+numpy==1.24.3
+torch==2.0.0
+torchvision==0.15.1
+opencv-python==4.6.0.66
+matplotlib==3.5.3
+plotly==5.15.0
+git+https://github.com/facebookresearch/detectron2.git

util.py

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+import base64
+
+import plotly.graph_objects as go
+import streamlit as st
+
+
+def set_background(image_file):
+    """
+    This function sets the background of a Streamlit app to an image specified by the given image file.
+
+    Parameters:
+        image_file (str): The path to the image file to be used as the background.
+
+    Returns:
+        None
+    """
+    with open(image_file, "rb") as f:
+        img_data = f.read()
+    b64_encoded = base64.b64encode(img_data).decode()
+    style = f"""
+        <style>
+        .stApp {{
+            background-image: url(data:image/png;base64,{b64_encoded});
+            background-size: cover;
+        }}
+        </style>
+    """
+    st.markdown(style, unsafe_allow_html=True)
+
+
+def visualize(image, bboxes):
+    """
+    Visualizes the image with bounding boxes using Plotly.
+
+    Args:
+        image: The input image.
+        bboxes (list): A list of bounding boxes in the format [x1, y1, x2, y2].
+
+    """
+    # Get the width and height of the image
+    width, height = image.size
+
+    shapes = []
+    for bbox in bboxes:
+        x1, y1, x2, y2 = bbox
+
+        # Convert bounding box coordinates to the format expected by Plotly
+        shapes.append(dict(
+            type="rect",
+            x0=x1,
+            y0=height - y2,
+            x1=x2,
+            y1=height - y1,
+            line=dict(color='red', width=6),
+        ))
+
+    fig = go.Figure()
+
+    # Add the image as a layout image
+    fig.update_layout(
+        images=[dict(
+            source=image,
+            xref="x",
+            yref="y",
+            x=0,
+            y=height,
+            sizex=width,
+            sizey=height,
+            sizing="stretch"
+        )]
+    )
+
+    # Set the axis ranges and disable axis labels
+    fig.update_xaxes(range=[0, width], showticklabels=False)
+    fig.update_yaxes(scaleanchor="x",
+                     scaleratio=1,
+                     range=[0, width], showticklabels=False)
+
+    fig.update_layout(
+        height=800,
+        updatemenus=[
+            dict(
+                direction='left',
+                pad=dict(r=10, t=10),
+                showactive=True,
+                x=0.11,
+                xanchor="left",
+                y=1.1,
+                yanchor="top",
+                type="buttons",
+                buttons=[
+                    dict(label="Original",
+                         method="relayout",
+                         args=["shapes", []]),
+                    dict(label="Detections",
+                         method="relayout",
+                         args=["shapes", shapes])
+                     ],
+            )
+        ]
+    )
+
+    st.plotly_chart(fig)