vcs-python-repository/YOLO python/webcam-test.py

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2024-04-13 17:01:16 -07:00
# source from https://dipankarmedh1.medium.com/real-time-object-detection-with-yolo-and-webcam-enhancing-your-computer-vision-skills-861b97c78993
from ultralytics import YOLO
import cv2
import math
# model
model = YOLO("yolov8x.pt")
# start webcam
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
# 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"
]
while True:
success, img = cap.read()
results = model.track(img, persist=True, stream=True)
# coordinates
for r in results:
boxes = r.boxes
for box in boxes:
# 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()