Computer vision YOLO11 model

2025-01-24 10:04:59 -05:00
commit 2e6f7a8a1f
22 changed files with 476 additions and 0 deletions
--- a/Test_Realtime.py
+++ b/Test_Realtime.py
@@ -0,0 +1,94 @@
+import cv2
+from ultralytics import YOLO
+
+# Camera index (default camera is 0)
+camera_index = 0
+
+# Load the YOLO model
+model = YOLO(r"D:\AIM\lemon\runs\detect\train4\weights\best.pt")  # Load custom model
+
+# Initialize the camera
+cap = cv2.VideoCapture(camera_index)
+if not cap.isOpened():
+    print("Unable to open the camera. Please check the device.")
+    exit()
+
+fps = int(cap.get(cv2.CAP_PROP_FPS))
+width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+print(f"Camera resolution: {width}x{height}, FPS: {fps} FPS")
+
+# Dictionary to track the state of each lemon
+lemon_states = {}  # Format: {ID: "State"}
+
+# Define class labels
+class_labels = {
+    0: "Bruised",
+    1: "DefectiveLemon",
+    2: "GoodLemon",
+    3: "NotRipeLemon",
+    4: "Rotten"
+}
+
+# Classes that require ID assignment
+id_tracked_classes = ["DefectiveLemon", "GoodLemon", "NotRipeLemon"]
+
+# Set the window to be resizable
+cv2.namedWindow("Live Detection", cv2.WINDOW_NORMAL)
+
+# Process video stream in real-time
+while cap.isOpened():
+    ret, frame = cap.read()
+    if not ret:
+        print("Unable to read camera input. Terminating program.")
+        break
+
+    # Perform object tracking using BoT-SORT
+    results = model.track(source=frame, conf=0.5, tracker='botsort.yaml', show=False)
+
+    for result in results:
+        frame = result.orig_img  # Current frame
+        detections = result.boxes  # Detection box information
+
+        for box in detections:
+            x1, y1, x2, y2 = map(int, box.xyxy[0])  # Detection box coordinates
+            obj_id = int(box.id) if box.id is not None else -1  # Tracking object ID
+            class_id = int(box.cls)  # Class ID
+            score = box.conf  # Confidence score
+            label = class_labels.get(class_id, "Unknown")  # Get class label
+
+            # Update lemon state and output information for tracked boxes
+            if obj_id != -1 and label in id_tracked_classes:
+                if obj_id not in lemon_states:
+                    lemon_states[obj_id] = label
+                else:
+                    # Once detected as "DefectiveLemon," the state remains "DefectiveLemon"
+                    if lemon_states[obj_id] != "DefectiveLemon":
+                        lemon_states[obj_id] = label
+
+                # Output ID, position, and label
+                position = f"({x1}, {y1}, {x2}, {y2})"
+                print(f"ID: {obj_id}, Position: {position}, Label: {lemon_states[obj_id]}")
+
+            # Draw detection boxes and labels (including untracked ones)
+            if obj_id != -1 and label in id_tracked_classes:
+                display_text = f"ID {obj_id} | {lemon_states[obj_id]}"
+            else:
+                display_text = label  # For untracked labels, only show the class
+
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+            cv2.putText(frame, display_text, (x1, y1 - 10),
+                        cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+
+    # Display the processed video stream
+    cv2.imshow("Live Detection", frame)
+
+    # Exit the loop when ESC key is pressed
+    if cv2.waitKey(1) & 0xFF == 27:  # 27 is the ASCII value for ESC key
+        print("ESC key detected. Exiting the program.")
+        break
+
+# Release resources
+cap.release()
+cv2.destroyAllWindows()
+print("Camera video processing complete. Program terminated.")