live_yolov8_opencv.py

import cv2
import numpy as np

import time
import sys

from ultralytics import YOLO


CONFIDENCE = 0.5
font_scale = 1
thickness = 1
labels = open("data/coco.names").read().strip().split("\n")
colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

model = YOLO("yolov8n.pt")

cap = cv2.VideoCapture(0)
_, image = cap.read()
h, w = image.shape[:2]
fourcc = cv2.VideoWriter_fourcc(*"XVID")
out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h))
while True:
    _, image = cap.read()
    
    start = time.perf_counter()
    # run inference on the image 
    # see: https://docs.ultralytics.com/modes/predict/#arguments for full list of arguments
    results = model.predict(image, conf=CONFIDENCE)[0]
    time_took = time.perf_counter() - start
    print("Time took:", time_took)

    # loop over the detections
    for data in results.boxes.data.tolist():
        # get the bounding box coordinates, confidence, and class id 
        xmin, ymin, xmax, ymax, confidence, class_id = data
        # converting the coordinates and the class id to integers
        xmin = int(xmin)
        ymin = int(ymin)
        xmax = int(xmax)
        ymax = int(ymax)
        class_id = int(class_id)

        # draw a bounding box rectangle and label on the image
        color = [int(c) for c in colors[class_id]]
        cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color=color, thickness=thickness)
        text = f"{labels[class_id]}: {confidence:.2f}"
        # calculate text width & height to draw the transparent boxes as background of the text
        (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0]
        text_offset_x = xmin
        text_offset_y = ymin - 5
        box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height))
        overlay = image.copy()
        cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED)
        # add opacity (transparency to the box)
        image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0)
        # now put the text (label: confidence %)
        cv2.putText(image, text, (xmin, ymin - 5), cv2.FONT_HERSHEY_SIMPLEX,
            fontScale=font_scale, color=(0, 0, 0), thickness=thickness)

    # end time to compute the fps
    end = time.perf_counter()
    # calculate the frame per second and draw it on the frame
    fps = f"FPS: {1 / (end - start):.2f}"
    cv2.putText(image, fps, (50, 50),
                cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 255, 0), 6)
    out.write(image)
    cv2.imshow("image", image)
    
    if ord("q") == cv2.waitKey(1):
        break


cap.release()
cv2.destroyAllWindows()