Update app.py

app.py

@@ -1,14 +1,12 @@
 import numpy as np
-import cvlib as cv
-from cvlib.object_detection import draw_bbox
+import cv2  # OpenCV for loading the YOLO model
 import streamlit as st
 from PIL import Image
 from collections import Counter
-import cv2  # OpenCV for loading the YOLO model
 
 # Streamlit app title
 st.set_page_config(page_title="Object Detection App", page_icon="🖼️", layout="centered")
-st.title("Object Detection with cvlib 🖼️")
+st.title("Object Detection with OpenCV and YOLO 🖼️")
 
 # Custom header and instructions
 st.markdown("""
@@ -21,48 +19,80 @@ The application will display the image with bounding boxes around the objects.
 st.markdown("### Step 1: Upload an Image for Object Detection")
 uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
 
-# Function to skip download and use local files for the model
-def detect_common_objects_skip_progress(image, model='yolov3'):
+# Function to detect objects using OpenCV's YOLO
+def detect_objects_with_yolo(image):
+    # Load YOLO model configuration and weights
     model_config = 'yolov3.cfg'  # Path to your downloaded .cfg file
     model_weights = 'yolov3.weights'  # Path to your downloaded .weights file
-
-    # Load the YOLO model manually using OpenCV
     net = cv2.dnn.readNet(model_weights, model_config)
 
-    # Perform object detection using cvlib (cvlib internally uses the loaded model)
-    box, label, count = cv.detect_common_objects(image, model='yolov3', net=net)
-    return box, label, count
+    # Get the names of the output layers
+    layer_names = net.getLayerNames()
+    output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
+
+    # Prepare the image for YOLO
+    blob = cv2.dnn.blobFromImage(image, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
+    net.setInput(blob)
+    outs = net.forward(output_layers)
 
+    # Process YOLO outputs
+    boxes = []
+    labels = []
+    confidences = []
+
+    for out in outs:
+        for detection in out:
+            scores = detection[5:]
+            class_id = np.argmax(scores)
+            confidence = scores[class_id]
+            if confidence > 0.5:  # Confidence threshold
+                center_x = int(detection[0] * image.shape[1])
+                center_y = int(detection[1] * image.shape[0])
+                w = int(detection[2] * image.shape[1])
+                h = int(detection[3] * image.shape[0])
+                x = int(center_x - w / 2)
+                y = int(center_y - h / 2)
+                boxes.append([x, y, w, h])
+                confidences.append(float(confidence))
+                labels.append(class_id)
+
+    return boxes, labels, confidences
+
+# Display the uploaded image and detect objects
 if uploaded_file is not None:
     # Convert image file to OpenCV format
     image = np.array(Image.open(uploaded_file))
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert from RGB to BGR for OpenCV
 
-    # Perform object detection with manually provided paths
-    box, label, count = detect_common_objects_skip_progress(image)
+    # Perform object detection with YOLO
+    boxes, labels, confidences = detect_objects_with_yolo(image)
 
     # Draw bounding boxes on the image
-    output_image = draw_bbox(image, box, label, count)
+    for i, box in enumerate(boxes):
+        x, y, w, h = box
+        label = labels[i]
+        confidence = confidences[i]
+        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
+        cv2.putText(image, f"{label} {confidence:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
 
     # Step 2: Display image with bounding boxes
     st.markdown("### Step 2: Detected Objects in the Image")
-    st.image(output_image, channels="BGR", use_column_width=True)
+    st.image(image, channels="BGR", use_column_width=True)
 
     # Step 3: Display the count of detected objects dynamically
     st.markdown("### Step 3: Detected Objects Count")
-
-    # Count each label in the image using Counter
-    label_counts = Counter(label)
+    label_counts = Counter(labels)
 
     # Display counts in a well-formatted table
     for obj, count in label_counts.items():
-        st.markdown(f"**{obj.capitalize()}s**: {count}")
+        st.markdown(f"**Object ID {obj}**: {count}")
 
     st.markdown("""
     ---
     ### Tips:
     - You can upload different images to see how the object detection model works.
     - Supported formats: PNG, JPG, JPEG.
-    - The app uses cvlib to detect common objects in the images, such as people, cars, trucks, and more.
+    - The app uses OpenCV with YOLO for detecting common objects.
     """)
 
 # Display a footer