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yfivefanrealycontrol.py
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yfivefanrealycontrol.py
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import torch
import numpy as np
import cv2
import csv
import time
from torchvision.ops.boxes import nms
import RPi.GPIO as GPIO
# Set up GPIO pins
object_pins = {
'cat': 17,
'person': 18,
'dog': 19,
'car': 20,
'bus': 21
}
GPIO.setmode(GPIO.BCM)
for pin in object_pins.values():
GPIO.setup(pin, GPIO.OUT)
# Load the YOLOv5 model
weights = "yolov5s.pt"
model = torch.hub.load('ultralytics/yolov5', 'custom', path=weights)
# Load the dataset configuration
data = "data/coco128.yaml"
model.yaml = data
# Set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model.to(device).eval()
# OpenCV setup for video capture
cap = cv2.VideoCapture(0) # Use webcam (change the index if you have multiple cameras)
fps = cap.get(cv2.CAP_PROP_FPS)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# CSV setup for saving detection results
csv_file = 'detection_results.csv'
csv_fields = ['timestamp', 'class', 'confidence', 'x', 'y', 'width', 'height']
csv_output = open(csv_file, 'w')
csv_writer = csv.DictWriter(csv_output, fieldnames=csv_fields)
csv_writer.writeheader()
# Folder setup for saving cropped detections
output_folder = 'detection_crops'
# Load class labels
class_labels = model.names
# Initialize variables
object_detected = {}
object_detection_time = {}
# Object detection loop
while True:
ret, frame = cap.read()
if not ret:
break
# Perform object detection
results = model(frame)
# Get detection information
detections = results.pandas().xyxy[0]
# Filter out bench detections
for object_label, object_pin in object_pins.items():
object_detections = detections[detections['name'] == object_label]
if len(object_detections) > 0:
# Apply non-maximum suppression (NMS)
boxes = object_detections[['xmin', 'ymin', 'xmax', 'ymax']].values.astype(np.float32)
scores = object_detections['confidence'].values.astype(np.float32)
keep_indices = nms(torch.tensor(boxes), torch.tensor(scores), iou_threshold=0.5)
keep_indices = keep_indices.cpu().numpy().astype(np.int32) # Convert to NumPy array of integers
# Get the timestamp
timestamp = time.strftime("%I:%M %p")
# Save detection results in CSV and crop images
for idx in keep_indices:
detection = object_detections.iloc[idx]
class_label = detection['name']
confidence = detection['confidence']
x = detection['xmin']
y = detection['ymin']
width = detection['xmax'] - detection['xmin']
height = detection['ymax'] - detection['ymin']
# Write to CSV
csv_writer.writerow({
'timestamp': timestamp,
'class': class_label,
'confidence': confidence,
'x': x,
'y': y,
'width': width,
'height': height
})
# Crop image
crop = frame[int(y):int(y + height), int(x):int(x + width)]
cv2.imwrite(f"{output_folder}/{class_label}_{timestamp}.jpg", crop)
# Store object detection and time
object_detected[object_label] = True
object_detection_time[object_label] = time.time()
# Check if the object detection time has exceeded 12 seconds
for object_label in object_detected.keys():
if object_label in object_pins:
pin = object_pins[object_label]
if object_detected[object_label] and (time.time() - object_detection_time[object_label]) >= 12:
# Turn off the corresponding LED
GPIO.output(pin, GPIO.LOW)
object_detected[object_label] = False
# Show the frame with detections
cv2.imshow('Object Detection', frame)
if cv2.waitKey(100) == 27: # Press Esc to exit
break
# Release resources
cap.release()
cv2.destroyAllWindows()
csv_output.close()
GPIO.cleanup()