Added Upload

app.py

@@ -0,0 +1,108 @@
+import streamlit as st
+from PIL import Image
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import torch
+from collections import Counter
+from torchvision.models.detection import fasterrcnn_resnet50_fpn_v2, FasterRCNN_ResNet50_FPN_V2_Weights
+from torchvision.utils import draw_bounding_boxes
+import io
+
+# Streamlit page configuration
+st.set_page_config(
+    page_title="Object Detector Dashboard",
+    page_icon="🔍",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+st.sidebar.title("Traffic Monitoring AI")
+
+weights = FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT
+categories = weights.meta["categories"]
+img_preprocess = weights.transforms()
+
+@st.cache_resource
+def load_model(threshold):
+    model = fasterrcnn_resnet50_fpn_v2(weights=weights, box_score_thresh=threshold)
+    model.eval()
+    return model
+
+def make_prediction(img, model):
+    img_processed = img_preprocess(img)
+    prediction = model(img_processed.unsqueeze(0))
+    prediction = prediction[0]
+    prediction["labels"] = [categories[label] for label in prediction["labels"]]
+    return prediction
+
+def create_image_with_bboxes(img, prediction):
+    img_tensor = torch.tensor(img)
+    img_with_bboxes = draw_bounding_boxes(
+        img_tensor,
+        boxes=prediction["boxes"],
+        labels=prediction["labels"],
+        colors=["Green" if label == "person" else "red" for label in prediction["labels"]],
+        width=1
+    )
+    img_with_bboxes_np = img_with_bboxes.detach().numpy().transpose(1, 2, 0)
+    return img_with_bboxes_np
+
+threshold = st.sidebar.slider("Confidence Threshold", min_value=0.0, max_value=1.0, value=0.1, step=0.01)
+
+st.title("Vehicle Detection")
+st.markdown("Upload your images for object detection:")
+
+# Allow users to upload multiple images
+uploaded_files = st.file_uploader("Choose images...", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+
+if uploaded_files:
+    st.markdown("**Processing uploaded images...**")
+    
+    # Load the model once
+    model = load_model(threshold)  
+    
+    all_predictions = []
+    cols = st.columns(min(len(uploaded_files), 4))  # Create columns for displaying images
+
+    with st.spinner("Processing images, please wait..."):
+        for i, uploaded_file in enumerate(uploaded_files):
+            try:
+                img = Image.open(uploaded_file)
+                img = img.convert("RGB")  # Ensure the image is in RGB format
+
+                prediction = make_prediction(img, model)
+                img_with_bbox = create_image_with_bboxes(np.array(img).transpose(2, 0, 1), prediction)
+
+                with cols[i % 4]:  
+                    st.header(f"Image {i + 1}: Object Detection Results")
+                    fig = plt.figure(figsize=(5, 5))
+                    ax = fig.add_subplot(111)
+                    plt.imshow(img_with_bbox)
+                    plt.xticks([], [])
+                    plt.yticks([], [])
+                    ax.spines[["top", "bottom", "right", "left"]].set_visible(True)
+                    st.pyplot(fig, use_container_width=True)
+
+                for label in prediction["labels"]:
+                    all_predictions.append({"Image": f"Image {i + 1}", "Label": label})
+
+            except Exception as e:
+                st.error(f"Error processing image {i + 1}: {e}")
+
+    if all_predictions:
+        image_object_counts = []
+        for i in range(1, len(uploaded_files) + 1):
+            current_image_preds = [pred['Label'] for pred in all_predictions if pred['Image'] == f"Image {i}"]
+            object_count = Counter(current_image_preds)
+            for label, count in object_count.items():
+                image_object_counts.append({"Image": f"Image {i}", "Label": label, "Count": count})
+
+        df_summary = pd.DataFrame(image_object_counts)
+
+        vehicle_categories = ['car', 'bus', 'motorcycle', 'truck', 'train', 'bicycle', 'scooter']
+        df_vehicles = df_summary[df_summary['Label'].isin(vehicle_categories)]
+
+        if not df_vehicles.empty:
+            st.header("Combined Vehicle Detection Table for All Images")
+            st.dataframe(df_vehicles, use_container_width=True)

requirements.txt

@@ -0,0 +1,11 @@
+matplotlib
+numpy
+pandas
+Pillow
+Pillow
+Requests
+selenium
+streamlit
+torch
+torchvision
+webdriver_manager