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yolo_car_detection.py
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yolo_car_detection.py
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import cv2
import numpy as np
# Load YOLO
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
layer_names = net.getLayerNames()
output_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
# Initialize webcam
cap = cv2.VideoCapture(0)
cap.set(3, 1280) # set width of the frame
cap.set(4, 720) # set height of the frame
while True:
ret, frame = cap.read()
height, width, channels = frame.shape
# Detecting objects
blob = cv2.dnn.blobFromImage(frame, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
net.setInput(blob)
outs = net.forward(output_layers)
# Showing informations on the screen
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5 and classes[class_id] == 'car':
# Object detected
center_x = int(detection[0] * width)
center_y = int(detection[1] * height)
w = int(detection[2] * width)
h = int(detection[3] * height)
# Rectangle coordinates
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)
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
confidence = confidences[i]
color = (0, 255, 0) if label == 'car' else (0, 0, 255)
cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)
cv2.putText(frame, f"{label} {confidence:.2f}", (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.imshow("Car Detection", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()