yolov8_opencv.py

import numpy as np
import os
import cv2
import time
import sys
from ultralytics import YOLO

# define some parameters
CONFIDENCE = 0.5
font_scale = 1
thickness = 1

# loading the YOLOv8 model with the default weight file
model = YOLO("yolov8n.pt")

# loading all the class labels (objects)
labels = open("data/coco.names").read().strip().split("\n")

# generating colors for each object for later plotting
colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8")

path_name = sys.argv[1]
image = cv2.imread(path_name)
file_name = os.path.basename(path_name) # "dog.jpg"
filename, ext = file_name.split(".") # "dog", "jpg"

# measure how much it took in seconds
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(f"Time took: {time_took:.2f}s")
print(results.boxes.data)

# 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)

# display output image
cv2.imshow("Image", image)
cv2.waitKey(0)
# save output image to disk
cv2.imwrite(filename + "_yolo8." + ext, image)