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CV_AG
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AG_CV_GAAIM 2025-06-10 10:24:36 -07:00
parent e3b8320ad2
commit 990de6345d
5 changed files with 175 additions and 12 deletions
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2
Test_Realtime_label.py
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@ -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()

117
Test_Track_updated.py
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@ -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.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)
mqtt_client.connect(MQTT_BROKER, 1883, 60000)
# 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:

3
autostart.sh Executable file
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@ -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

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comicrobodog.png Normal file
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65
loadingscreen2.py Normal file
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@ -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()