cam.py

from ultralytics import YOLO
import cv2
import math 
import time

# start webcam
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)

# model
model = YOLO("yolo-Weights/yolov8m.pt")

# object classes
classNames = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck", "boat",
              "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat",
              "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella",
              "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", "baseball bat",
              "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", "wine glass", "cup",
              "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", "broccoli",
              "carrot", "hot dog", "pizza", "donut", "cake", "chair", "sofa", "pottedplant", "bed",
              "diningtable", "toilet", "tvmonitor", "laptop", "mouse", "remote", "keyboard", "cell phone",
              "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors",
              "teddy bear", "hair drier", "toothbrush"
              ]

# class name to detect
target_class_name = "person"
target_class_index = classNames.index(target_class_name)

while True:
    # Read the frame from the camera
    time.sleep(1)
    success, img = cap.read()

    # Pass the image through the model
    results = model(img, stream=True)

    # coordinates
    for r in results:
        boxes = r.boxes

        for box in boxes:
         # class index
            cls = int(box.cls[0])

            # only process if the detected object is a person
            if cls == target_class_index:
       
                # bounding box
                x1, y1, x2, y2 = box.xyxy[0]
                x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2) # convert to int values
    
                # put box in cam
                cv2.rectangle(img, (x1, y1), (x2, y2), (255, 0, 255), 3)
    
                # confidence
                confidence = math.ceil((box.conf[0]*100))/100
                print("Confidence --->",confidence)
    
                # class name
                cls = int(box.cls[0])
                print("Class name -->", classNames[cls])
    
                # object details
                org = [x1, y1]
                font = cv2.FONT_HERSHEY_SIMPLEX
                fontScale = 1
                color = (255, 0, 0)
                thickness = 2
    
                cv2.putText(img, classNames[cls], org, font, fontScale, color, thickness)

    cv2.imshow('Webcam', img)
    if cv2.waitKey(1) == ord('q'):
        break

cap.release()
cv2.destroyAllWindows()