added finished test track and botsort yaml
This commit is contained in:
M1MacMini
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ce6b1c6705
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e3b8320ad2
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@ -1,8 +1,40 @@
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import cv2
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from ultralytics import YOLO
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from collections import deque
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import paho.mqtt.client as mqtt
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from influxdb import InfluxDBClient
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from influxdb_client import InfluxDBClient, Point, WriteOptions
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import time
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from datetime import datetime
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import ssl
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import os
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# InfluxDB Configuration
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INFLUX_URL = "http://localhost:8086"
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INFLUX_TOKEN = "export INFLUX_TOKEN=duVTQHPpHqr6WmdYfpSStqm-pxnvZHs-W0-3lXDnk8Tn6PGt59MlnTSR6egjMWdYvmL_ZI6xt3YUzGVBZHvc7w=="
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INFLUX_ORG = "GAAIM"
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INFLUX_BUCKET = "AGVIGNETTE"
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# Connect to InfluxDB
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client = InfluxDBClient(url=INFLUX_URL, token=INFLUX_TOKEN, org=INFLUX_ORG)
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write_api = client.write_api(write_options=WriteOptions(batch_size=1))
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# MQTT Setup
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MQTT_BROKER = "192.168.10.57"
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MQTT_TOPIC = "fruit/classification"
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mqtt_client = mqtt.Client()
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# Set up TLS/SSL for MQTT connection
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# mqtt_client.tls_set(
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# ca_certs="/Users/vel/Desktop/CvModel/mosquitto/mosquitto/certs/ca.crt", # Path to the CA certificate
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# tls_version=ssl.PROTOCOL_TLS # Specify the TLS version
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#)
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#mqtt_client.tls_insecure_set(True)
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mqtt_client.connect(MQTT_BROKER, 1883, 6000)
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# Allow duplicate loading of OpenMP runtime
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os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
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@ -10,10 +42,15 @@ os.environ["KMP_DUPLICATE_LIB_OK"] = "True"
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yaml_path = "botsort.yaml"
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# Camera index (default camera index, 1 indicates an external camera)
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camera_index = 1
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camera_index = 0
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cap = cv2.VideoCapture(camera_index)
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cap.set(cv2.CAP_PROP_FPS, 30)
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width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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# Load the YOLO model
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model = YOLO(r"D:\AIM\lemon\runs\detect\train4\weights\best.pt") # Load custom model
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model = YOLO(r"/Users/vel/Desktop/CvModel/CV_AG/runs/detect/train4/weights/best.pt") # Load custom model
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# Define class labels
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class_labels = {
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@ -34,6 +71,7 @@ GOOD_THRESHOLD = 0.7 # Threshold for "GoodLemon" and "NotRipeLemon" proportio
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# State history for each target (used for smoothing), format: {ID: deque([...], maxlen=HISTORY_LENGTH)}
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lemon_history = {}
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lemon_send_history = []
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# Set the display window to be resizable
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cv2.namedWindow("Live Detection", cv2.WINDOW_NORMAL)
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@ -71,15 +109,17 @@ def get_smoothed_label(obj_id, current_label):
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# Use streaming tracking mode to maintain tracker state
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results = model.track(
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source=camera_index, # Get video stream directly from the camera
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conf=0.5,
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conf=0.45,
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tracker=yaml_path, # Use the YAML configuration file
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persist=True, # Persist tracking (do not reset)
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stream=True, # Stream processing, not frame-by-frame calling
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show=False
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show=False,
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device = 'mps' #'cpu'
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)
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# Iterate over streaming tracking results
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for result in results:
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frame = result.orig_img # Current frame
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detections = result.boxes # Detection box information
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@ -100,6 +140,32 @@ for result in results:
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if final_label in smoothing_labels:
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position = f"({x1}, {y1}, {x2}, {y2})"
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print(f"ID: {obj_id}, Position: {position}, Label: {display_text}")
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# Draw detection box and label with color based on classification
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if final_label == "DefectiveLemon":
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box_color = (100, 100, 255) # Red for defective
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elif final_label == "NotRipeLemon":
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box_color = (255, 100, 80) # Blue for unripe
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elif final_label == "GoodLemon":
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box_color = (0, 255, 0) # Green for good
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else:
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box_color = (255, 255, 255) # White for unknown or other classes
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# Add background rectangle for text
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text_size = cv2.getTextSize(display_text, cv2.FONT_HERSHEY_TRIPLEX, 0.6, 2)[0]
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text_x, text_y = x1, y1 - 10
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text_w, text_h = text_size[0], text_size[1]
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cv2.rectangle(frame, (text_x, text_y - text_h - 5), (text_x + text_w, text_y + 5), (0, 0, 0), -1)
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# Draw detection box and text
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cv2.rectangle(frame, (x1, y1), (x2, y2), box_color, 2)
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cv2.putText(frame, display_text, (text_x, text_y),
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cv2.FONT_HERSHEY_TRIPLEX, 0.6, box_color, 2)
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if x1 > 750 and x1 < 850 and y2 < 410 and y1 > 190 and obj_id not in lemon_send_history:
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if final_label in ["DefectiveLemon", "NotRipeLemon", "GoodLemon"]:
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mqtt_message = f"lemon_classification classification=\"{final_label}\" {int(time.time()*1e9)}"
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lemon_send_history.append(obj_id)
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mqtt_client.publish(MQTT_TOPIC, mqtt_message)
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else:
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# For other classes, display the current detection result directly and clear history (if exists)
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if obj_id in lemon_history:
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@ -108,11 +174,6 @@ for result in results:
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else:
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display_text = label
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# Draw detection box and label
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cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
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cv2.putText(frame, display_text, (x1, y1 - 10),
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cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
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# Display the processed frame
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cv2.imshow("Live Detection", frame)
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@ -123,3 +184,5 @@ for result in results:
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cv2.destroyAllWindows()
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print("Camera video processing complete. Program terminated.")
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16
botsort.yaml
16
botsort.yaml
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@ -4,18 +4,18 @@
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# For documentation and examples see https://docs.ultralytics.com/modes/track/
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# For BoT-SORT source code see https://github.com/NirAharon/BoT-SORT
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tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
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track_high_thresh: 0.25 # threshold for the first association
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track_low_thresh: 0.1 # threshold for the second association
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new_track_thresh: 0.4 # threshold for init new track if the detection does not match any tracks
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track_buffer: 30 # buffer to calculate the time when to remove tracks
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match_thresh: 0.7 # threshold for matching tracks
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tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
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track_high_thresh: 0.2 # threshold for the first association
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track_low_thresh: 0.05 # threshold for the second association
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new_track_thresh: 0.3 # threshold for init new track if the detection does not match any tracks
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track_buffer: 50 # buffer to calculate the time when to remove tracks
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match_thresh: 0.8 # threshold for matching tracks
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fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
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# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
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# BoT-SORT settings
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gmc_method: sparseOptFlow # method of global motion compensation
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# ReID model related thresh (not supported yet)
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proximity_thresh: 0.5
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appearance_thresh: 0.25
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proximity_thresh: 0.6
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appearance_thresh: 0.2
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with_reid: False
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