copyright @ DEPA MSU 2023 | Public
YoloV8-Silva || YouTube
The most recent and cutting-edge YOLO model, YoloV8, can be utilized for applications including object identification, image categorization, and instance segmentation. Ultralytics, who also produced the influential YOLOv5 model that defined the industry, developed YOLOv8. Compared to YOLOv5, YOLOv8 has a number of architectural updates and enhancements.
We are simply using YOLO models in a python environment with opencv on Windows, Mac or Linux system. It can be imported from the ultralytics module
Two example codes were defined for the module
yolov8_basics.py and yolov8_n_opencv.py
Note
Install requirements.txt file in a Python>=3.7.0 environment, including PyTorch>=1.7
pip install -r requirements.txtNote
Link to official
YoloV8 GitHub page.
python yolov8_basics.pyUse YoloV8 is an OpenCV way. Have control over detection on each frame and choose what happens per detection.
python yolov8_n_opencv.pyimport numpy as np
import cv2
from ultralytics import YOLO
import random
# opening the file in read mode
my_file = open("utils/coco
6896
.txt", "r")
# reading the file
data = my_file.read()
# replacing end splitting the text | when newline ('\n') is seen.
class_list = data.split("\n")
my_file.close()
# print(class_list)
# Generate random colors for class list
detection_colors = []
for i in range(len(class_list)):
r = random.randint(0,255)
g = random.randint(0,255)
b = random.randint(0,255)
detection_colors.append((b,g,r))
# load a pretrained YOLOv8n model
model = YOLO("weights/yolov8n.pt", "v8")
# Vals to resize video frames | small frame optimise the run
frame_wid = 640
frame_hyt = 480
# cap = cv2.VideoCapture(1)
cap = cv2.VideoCapture("inference/videos/afriq0.MP4")
if not cap.isOpened():
print("Cannot open camera")
exit()
while True:
# Capture frame-by-frame
ret, frame = cap.read()
# if frame is read correctly ret is True
if not ret:
print("Can't receive frame (stream end?). Exiting ...")
break
# resize the frame | small frame optimise the run
# frame = cv2.resize(frame, (frame_wid, frame_hyt))
# Predict on image
detect_params = model.predict(source=[frame], conf=0.45, save=False)
# Convert tensor array to numpy
DP = detect_params[0].numpy()
print(DP)
if len(DP) != 0:
for i in range(len(detect_params[0])):
print(i)
boxes = detect_params[0].boxes
box = boxes[i] # returns one box
clsID = box.cls.numpy()[0]
conf = box.conf.numpy()[0]
bb = box.xyxy.numpy()[0]
cv2.rectangle(
frame,
(int(bb[0]), int(bb[1])),
(int(bb[2]), int(bb[3])),
detection_colors[int(clsID)],
3,
)
# Display class name and confidence
font = cv2.FONT_HERSHEY_COMPLEX
cv2.putText(
frame,
class_list[int(clsID)]
+ " "
+ str(round(conf, 3))
+ "%",
(int(bb[0]), int(bb[1]) - 10),
font,
1,
(255, 255, 255),
2,
)
# Display the resulting frame
cv2.imshow('ObjectDetection', frame)
# Terminate run when "Q" pressed
if cv2.waitKey(1) == ord('q'):
break
# When everything done, release the capture
cap.release()
cv2.destroyAllWindows()