Update src/streamlit_app.py

src/streamlit_app.py

@@ -37,4 +37,132 @@ st.altair_chart(alt.Chart(df, height=700, width=700)
         y=alt.Y("y", axis=None),
         color=alt.Color("idx", legend=None, scale=alt.Scale()),
         size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+    ))
+
+
+import streamlit as st
+import cv2
+import numpy as np
+import pandas as pd
+from PIL import Image
+import torch
+from torchvision.transforms import Compose, Resize, ToTensor, Normalize
+from segment_anything import SamPredictor, sam_model_registry
+
+# Set Streamlit configuration
+st.set_page_config(page_title="Volume Estimator", layout="wide")
+st.title("📦 Volume Estimation using SAM Segmentation + MiDaS Depth")
+
+# Load SAM and MiDaS models
+@st.cache_resource
+def load_models():
+    sam_checkpoint = "C:/Users/Administrator/Desktop/streamlit_tl/models/sam_vit_h_4b8939.pth"
+    sam = sam_model_registry["vit_h"](checkpoint=sam_checkpoint).to("cuda" if torch.cuda.is_available() else "cpu")
+    predictor = SamPredictor(sam)
+
+    midas = torch.hub.load("intel-isl/MiDaS", "DPT_Large")
+    midas.eval()
+    midas_transform = Compose([
+        Resize(384),
+        ToTensor(),
+        Normalize(mean=[0.5]*3, std=[0.5]*3)
+    ])
+    return predictor, midas, midas_transform
+
+predictor, midas_model, midas_transform = load_models()
+
+# Input source selection
+source_option = st.radio("Select input source", ("Upload Image", "Use Webcam"))
+
+uploaded_file = None
+image_pil = None
+
+if source_option == "Upload Image":
+    uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    if uploaded_file:
+        image_pil = Image.open(uploaded_file).convert("RGB")
+
+elif source_option == "Use Webcam":
+    run_camera = st.checkbox("Start Camera")
+
+    if run_camera:
+        cap = cv2.VideoCapture(0)
+        stframe = st.empty()
+        capture = False
+
+        while run_camera and cap.isOpened():
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            stframe.image(frame_rgb, caption="Live Camera Feed", channels="RGB")
+
+            if st.button("📸 Capture Frame"):
+                image_pil = Image.fromarray(frame_rgb)
+                run_camera = False
+                cap.release()
+                break
+
+# Continue processing if we have an image
+if image_pil:
+    image_np = np.array(image_pil)
+    img_h, img_w = image_np.shape[:2]
+    st.image(image_pil, caption="Selected Image", use_container_width=True)
+
+    # Real-world reference dimensions
+    real_image_width_cm = 100
+    real_image_height_cm = 75
+    assumed_max_depth_cm = 100
+
+    pixel_to_cm_x = real_image_width_cm / img_w
+    pixel_to_cm_y = real_image_height_cm / img_h
+
+    # SAM Segmentation
+    predictor.set_image(image_np)
+    masks, _, _ = predictor.predict(multimask_output=False)
+
+    # MiDaS Depth Estimation
+    input_tensor = midas_transform(image_pil).unsqueeze(0)
+    with torch.no_grad():
+        depth_prediction = midas_model(input_tensor).squeeze().cpu().numpy()
+    depth_resized = cv2.resize(depth_prediction, (img_w, img_h))
+
+    # Object volume computation
+    volume_data = []
+    for i, mask in enumerate(masks):
+        mask_np = mask
+        x, y, w, h = cv2.boundingRect(mask_np.astype(np.uint8))
+        width_px = w
+        height_px = h
+
+        width_cm = width_px * pixel_to_cm_x
+        height_cm = height_px * pixel_to_cm_y
+
+        depth_masked = depth_resized[mask_np > 0.5]
+
+        if depth_masked.size == 0:
+            continue
+
+        normalized_depth = (depth_masked - np.min(depth_resized)) / (np.max(depth_resized) - np.min(depth_resized) + 1e-6)
+        depth_cm = np.mean(normalized_depth) * assumed_max_depth_cm
+
+        volume_cm3 = round(depth_cm * width_cm * height_cm, 2)
+
+        volume_data.append({
+            "Object": f"Object #{i+1}",
+            "Length (Depth)": f"{round(depth_cm, 2)} cm",
+            "Breadth (Width)": f"{round(width_cm, 2)} cm",
+            "Height": f"{round(height_cm, 2)} cm",
+            "Volume": f"{volume_cm3} cm³"
+        })
+
+    # Display volume table
+    if volume_data:
+        df = pd.DataFrame(volume_data)
+        st.markdown("### 📊 Object Dimensions and Volume")
+        st.dataframe(df)
+
+        csv = df.to_csv(index=False).encode('utf-8')
+        st.download_button("📂 Download Volume Table as CSV", csv, "object_volumes_with_units.csv", "text/csv")
+    else:
+        st.warning("🚫 No objects were segmented.")