Committed Changes

Test_Realtime_label.py

@@ -18,7 +18,7 @@ client = InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=INFLUX_ORG)
 write_api = client.write_api(write_options=WriteOptions(batch_size=1))
 
 # MQTT Setup
-MQTT_BROKER = "192.168.10.51"
+MQTT_BROKER = "172.20.29.125"
 MQTT_TOPIC = "fruit/classification"
 
 mqtt_client = mqtt.Client()

Test_Track_updated.py

@@ -8,6 +8,11 @@ import time
 from datetime import datetime
 import ssl
 import os
+import tkinter as tk
+from tkinter import ttk
+from PIL import Image, ImageTk
+import threading
+
 
 # InfluxDB Configuration
 INFLUX_URL = "http://localhost:8086"
@@ -20,20 +25,75 @@ client = InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=INFLUX_ORG)
 write_api = client.write_api(write_options=WriteOptions(batch_size=1))
 
 # MQTT Setup
-MQTT_BROKER = "192.168.10.57"
+MQTT_BROKER = "192.168.8.172"
 MQTT_TOPIC = "fruit/classification"
 
+def start_loading():
+    for i in range(101):  # 0 to 100%
+        time.sleep(0.38)   # 0.4s * 100 = 40 seconds
+        progress_var.set(i)
+        progress_bar.update_idletasks()
+    root.destroy()
+
+# Set up full-screen window
+root = tk.Tk()
+root.title("Starting Up")
+root.attributes('-fullscreen', True)
+
+# Get screen size
+screen_width = root.winfo_screenwidth()
+screen_height = root.winfo_screenheight()
+
+# Load and resize the background image
+try:
+    bg_img = Image.open("comicrobodog.png")  # Replace with your image
+    bg_img = bg_img.resize((screen_width, screen_height), Image.ANTIALIAS)
+    bg_photo = ImageTk.PhotoImage(bg_img)
+
+    # Set as background using a label
+    bg_label = tk.Label(root, image=bg_photo)
+    bg_label.place(x=0, y=0, relwidth=1, relheight=1)
+except Exception as e:
+    print("Error loading background image:", e)
+    root.configure(bg='black')  # Fallback
+
+# Overlay content frame (transparent background)
+overlay = tk.Frame(root, bg='', padx=20, pady=20)
+overlay.place(relx=0.5, rely=0.5, anchor='center')
+
+# Message label
+label = tk.Label(
+    overlay,
+    text="Computer Vision Vignette is Starting Up",
+    font=("Helvetica", 32, "bold"),
+    fg="white"
+)
+label.pack(pady=10)
+
+# Progress bar
+progress_var = tk.IntVar()
+progress_bar = ttk.Progressbar(
+    overlay,
+    maximum=100,
+    variable=progress_var,
+    length=800
+)
+progress_bar.pack(pady=20)
+
+# Start the progress in a thread
+threading.Thread(target=start_loading, daemon=True).start()
+
+# Close on ESC
+root.bind("<Escape>", lambda e: root.destroy())
+
+root.mainloop()
+
+
 mqtt_client = mqtt.Client()
 
 
 # Set up TLS/SSL for MQTT connection
-
+mqtt_client.connect(MQTT_BROKER, 1883, 60000)
-# mqtt_client.tls_set(
-#    ca_certs="/Users/vel/Desktop/CvModel/mosquitto/mosquitto/certs/ca.crt",  # Path to the CA certificate
-#    tls_version=ssl.PROTOCOL_TLS  # Specify the TLS version
-#)
-#mqtt_client.tls_insecure_set(True)
-mqtt_client.connect(MQTT_BROKER, 1883, 6000)
 
 # Allow duplicate loading of OpenMP runtime
 os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
@@ -50,7 +110,7 @@ width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
 height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
 
 # Load the YOLO model
-model = YOLO(r"/Users/vel/Desktop/CvModel/CV_AG/runs/detect/train4/weights/best.pt")  # Load custom model
+model = YOLO(r"/Users/ag_cv_gaaim/Desktop/CV_AG/runs/detect/train4/weights/best.pt")  # Load custom model
 
 # Define class labels
 class_labels = {
@@ -75,6 +135,7 @@ lemon_send_history = []
 
 # Set the display window to be resizable
 cv2.namedWindow("Live Detection", cv2.WINDOW_NORMAL)
+cv2.setWindowProperty("Live Detection", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
 
 # Smoothing function:
 # If the current detected label is not in smoothing_labels, clear the target's history and return the current label;
@@ -109,7 +170,7 @@ def get_smoothed_label(obj_id, current_label):
 # Use streaming tracking mode to maintain tracker state
 results = model.track(
     source=camera_index,    # Get video stream directly from the camera
-    conf=0.45,
+    conf=0.3,
     tracker=yaml_path,      # Use the YAML configuration file
     persist=True,           # Persist tracking (do not reset)
     stream=True,            # Stream processing, not frame-by-frame calling
@@ -117,14 +178,39 @@ results = model.track(
     device = 'mps' #'cpu' 
 )
 
+# Create variables to store the tracking results
+num_defective = 0
+num_good = 0
+num_notripe = 0
+last_classification = None
+
 # Iterate over streaming tracking results
 for result in results:
 
     frame = result.orig_img  # Current frame
+
+    frame = cv2.flip(frame, 1)
+
     detections = result.boxes  # Detection box information
 
+    # Create bounding box for classification area
+    cv2.rectangle(frame, (0, 370), (1000, 700), (0, 0, 0), -1)  # Black background for text
+    cv2.rectangle(frame, (0, 0), (1000, 200), (0, 0, 0), -1)  # Black background for text
+    cv2.rectangle(frame, (600, 200), (660, 370), (255, 255, 255), 2)
+    cv2.putText(frame, "Classification Area", (560, 190), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
+    # Display the number of lemons in the top left corner
+    cv2.putText(frame, f"Defective: {num_defective}", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
+    cv2.putText(frame, f"Good: {num_good}", (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
+    cv2.putText(frame, f"Not Ripe: {num_notripe}", (10, 90), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 100, 80), 2)
+    cv2.putText(frame, f"Last Classification: {last_classification}", (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
+    cv2.putText(frame, f"Total Lemons: {num_defective + num_good + num_notripe}", (10, 150), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 255), 2)
+    
     for box in detections:
         x1, y1, x2, y2 = map(int, box.xyxy[0])  # Detection box coordinates
+
+        # Adjust x-coordinates for the flipped frame
+        x1, x2 = width - x2, width - x1
+
         obj_id = int(box.id) if box.id is not None else -1  # Tracking ID
         class_id = int(box.cls)  # Class ID
         score = box.conf  # Confidence
@@ -160,12 +246,21 @@ for result in results:
                     cv2.rectangle(frame, (x1, y1), (x2, y2), box_color, 2)
                     cv2.putText(frame, display_text, (text_x, text_y),
                                 cv2.FONT_HERSHEY_TRIPLEX, 0.6, box_color, 2)
+                    cv2.rectangle(frame, (500, 0), (1000, 170), (0, 0, 0), -1)  # Black background for text
 
-                    if x1 > 750 and x1 < 850 and y2 < 410 and y1 > 190 and obj_id not in lemon_send_history:
+                    if x1 > 600 and x1 < 660 and y2 < 410 and y1 > 190 and obj_id not in lemon_send_history:
                         if final_label in ["DefectiveLemon", "NotRipeLemon", "GoodLemon"]:
                             mqtt_message = f"lemon_classification classification=\"{final_label}\" {int(time.time()*1e9)}"
                             lemon_send_history.append(obj_id)
                             mqtt_client.publish(MQTT_TOPIC, mqtt_message)
+                            # Update Tracking Variables
+                            if final_label == "DefectiveLemon":
+                                num_defective += 1
+                            elif final_label == "GoodLemon":
+                                num_good += 1
+                            elif final_label == "NotRipeLemon":
+                                num_notripe += 1
+                            last_classification = final_label
             else:
                 # For other classes, display the current detection result directly and clear history (if exists)
                 if obj_id in lemon_history:

autostart.sh

@@ -0,0 +1,3 @@
+#!/usr/bin/env bash
+source /Users/ag_cv_gaaim/Desktop/CV_AG/cvag/bin/activate
+python3 /Users/ag_cv_gaaim/Desktop/CV_AG/Test_Track_updated.py

loadingscreen2.py

@@ -0,0 +1,65 @@
+import tkinter as tk
+from tkinter import ttk
+from PIL import Image, ImageTk
+import time
+import threading
+
+def start_loading():
+    for i in range(101):  # 0 to 100%
+        time.sleep(0.4)   # 0.4s * 100 = 40 seconds
+        progress_var.set(i)
+        progress_bar.update_idletasks()
+    root.destroy()
+
+# Set up full-screen window
+root = tk.Tk()
+root.title("Starting Up")
+root.attributes('-fullscreen', True)
+
+# Get screen size
+screen_width = root.winfo_screenwidth()
+screen_height = root.winfo_screenheight()
+
+# Load and resize the background image
+try:
+    bg_img = Image.open("comicrobodog.png")  # Replace with your image
+    bg_img = bg_img.resize((screen_width, screen_height), Image.ANTIALIAS)
+    bg_photo = ImageTk.PhotoImage(bg_img)
+
+    # Set as background using a label
+    bg_label = tk.Label(root, image=bg_photo)
+    bg_label.place(x=0, y=0, relwidth=1, relheight=1)
+except Exception as e:
+    print("Error loading background image:", e)
+    root.configure(bg='black')  # Fallback
+
+# Overlay content frame (transparent background)
+overlay = tk.Frame(root, bg='', padx=20, pady=20)
+overlay.place(relx=0.5, rely=0.5, anchor='center')
+
+# Message label
+label = tk.Label(
+    overlay,
+    text="Computer Vision Vignette is Starting Up",
+    font=("Helvetica", 32, "bold"),
+    fg="white"
+)
+label.pack(pady=10)
+
+# Progress bar
+progress_var = tk.IntVar()
+progress_bar = ttk.Progressbar(
+    overlay,
+    maximum=100,
+    variable=progress_var,
+    length=800
+)
+progress_bar.pack(pady=20)
+
+# Start the progress in a thread
+threading.Thread(target=start_loading, daemon=True).start()
+
+# Close on ESC
+root.bind("<Escape>", lambda e: root.destroy())
+
+root.mainloop()