init

app.py

@@ -0,0 +1,80 @@
+# Created by yarramsettinaresh GORAKA DIGITAL PRIVATE LIMITED at 24/10/24
+import gradio as gr
+import cv2
+import time
+from ultralytics import YOLO
+
+# Load your YOLO model (adjust model path or type as needed)
+model_path = "/Users/yarramsettinaresh/Downloads/model_- 11 october 2024 11_07.pt"
+model = YOLO(model_path)
+
+
+def ultralytics_predict(model, frame):
+    confidence_threshold = 0.2
+    start_time = time.time()
+    results = model(frame)  # Perform inference on the frame
+    end_time = time.time()
+
+    duration = end_time - start_time
+    print(f"Prediction duration: {duration:.4f} seconds")
+    duration_str = f"{duration:.4f} S"
+
+    object_count = {}  # Dictionary to store counts of detected objects
+
+    for detection in results[0].boxes:  # Iterate through detections
+        conf = float(detection.conf[0])  # Confidence score
+        if conf > confidence_threshold:
+            conf, pos, text, color = ultralytics(detection, duration_str)
+            cv2.rectangle(frame, pos[0], pos[1], color, 2)
+            cv2.putText(frame, text, (pos[0][0], pos[0][1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
+
+            # Update object count
+            class_id = int(detection.cls[0])
+            class_name = model.names[class_id]
+            object_count[class_name] = object_count.get(class_name, 0) + 1
+
+    return object_count  # Return the count of detected objects
+
+
+def ultralytics(detection, duration):
+    COLOUR_MAP = {
+        0: (0, 0, 255),  # Red in BGR format
+        1: (0, 255, 0)  # Green in BGR format
+    }
+
+    conf = float(detection.conf[0])  # Confidence score
+    class_id = int(detection.cls[0])  # Class ID
+    name = model.names[class_id]  # Get class name
+    xmin, ymin, xmax, ymax = map(int, detection.xyxy[0])  # Bounding box coordinates
+    color = COLOUR_MAP.get(class_id, (255, 255, 255))  # Default to white if not found
+
+    # Draw bounding box and label on the frame
+    pos = (xmin, ymin), (xmax, ymax)
+    text = f"{name} {round(conf, 2)} :{duration}"
+
+    return conf, pos, text, color
+
+
+def process_frame(frame):
+    object_count = ultralytics_predict(model, frame)
+    return frame, object_count  # Return frame and object count
+
+
+def detect_image(image):
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert to BGR format for OpenCV
+    result_frame, object_count = process_frame(image)
+    result_frame = cv2.cvtColor(result_frame, cv2.COLOR_BGR2RGB)  # Convert back to RGB for Gradio
+    return result_frame, object_count  # Return both the frame and the count
+
+
+# Create Gradio Interface
+gr.Interface(
+    fn=detect_image,
+    inputs=gr.Image(type="numpy"),  # Updated input format
+    outputs=[
+        gr.Image(type="numpy"),  # Image output
+        gr.JSON(),  # Object count output as JSON
+    ],
+    title="YOLO Object Detection",
+    description="Upload an image to detect objects using YOLO model."
+).launch()

model_- 11 october 2024 11_07.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e4f23f88664dbf1177b44c2c989c627da63d8c27ba3db4456da86f26271e7a44
+size 5480979

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+opencv-python
+ultralytics