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Real-time Object Detection.py
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Real-time Object Detection.py
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import cv2
import numpy as np
from PIL import Image, ImageDraw
# Load YOLOv3 model
yolo_net = cv2.dnn.readNet("path/to/yolov3.weights", "path/to/yolov3.cfg")
# Load class labels
yolo_classes = []
with open('path/to/coco.names', 'r') as f:
yolo_classes = f.read().splitlines()
# Function to perform YOLO object detection on a single image
def perform_yolo_detection(img):
height, width, _ = img.shape # Get image dimensions
# Preprocess image for YOLO
blob = cv2.dnn.blobFromImage(img, 1/255, (320, 320), (0, 0, 0), swapRB=True, crop=False)
yolo_net.setInput(blob)
# Get YOLO output
output_layers_names = yolo_net.getUnconnectedOutLayersNames()
layer_outputs = yolo_net.forward(output_layers_names)
# Parse YOLO output
boxes = []
confidences = []
class_ids = []
for output in layer_outputs:
for detection in output:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.7:
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
x = int(center_x - w/2)
y = int(center_y - h/2)
boxes.append([x, y, w, h])
confidences.append(float(confidence))
class_ids.append(class_id)
# Apply non-maximum suppression
indexes = np.array(cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)).flatten()
# Draw bounding boxes and labels on the image
font = cv2.FONT_HERSHEY_SIMPLEX # Change font type
thickness = 3 # Thickness of the rectangle border
colors = np.random.uniform(0, 255, size=(len(boxes), 3))
for i in indexes.flatten():
x, y, w, h = boxes[i]
label = str(yolo_classes[class_ids[i]])
confidence = str(round(confidences[i], 2))
color = tuple(int(c) for c in colors[i])
# Draw outer rectangle using OpenCV (unchanged)
cv2.rectangle(img, (x, y), (x+w, y+h), color, thickness)
# Calculate text size for dynamic positioning (unchanged)
(label_width, label_height), baseline = cv2.getTextSize(label + " " + confidence, font, 0.5, 2)
# Draw inner rectangle using PIL for rounded corners
pil_img = Image.fromarray(img) # Convert OpenCV image to PIL image
draw = ImageDraw.Draw(pil_img)
draw.rounded_rectangle((x + 3, y - 20, x + label_width + 3, y + 2), radius=5, fill=color, outline=color, width=3)
img = np.array(pil_img) # Convert back to OpenCV image
# Draw text using OpenCV (unchanged)
cv2.putText(img, label + " " + confidence, (x, y - 5), font, 0.5, (255, 255, 255), 1)
return img
# Open a live camera feed
video_capture = cv2.VideoCapture(0) # 0 indicates the default camera, you can change it if you have multiple cameras
while True:
ret, frame = video_capture.read()
if not ret:
break
# Perform YOLO object detection on the frame
yolo_detected_frame = perform_yolo_detection(frame)
# Display the image with bounding boxes
cv2.imshow('YOLO Object Detection', yolo_detected_frame)
if cv2.waitKey(10) & 0xFF == ord('q'): # Press 'q' to exit the loop
break
video_capture.release()
cv2.destroyAllWindows()