Update app.py

app.py

@@ -1,126 +1,137 @@
-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.")
+import os
+import sys
+import subprocess
+
+# ⬇️ Install `segment_anything` if not already installed
+try:
+    from segment_anything import SamPredictor, sam_model_registry
+except ImportError:
+    subprocess.check_call([sys.executable, "-m", "pip", "install", "git+https://github.com/facebookresearch/segment-anything.git"])
+    from segment_anything import SamPredictor, sam_model_registry
+
+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.")