zinnah1995 / yolov8-silva

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.

Silva-Flow

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

Installation

pip install -r requirements.txt

Note

Link to official YoloV8 GitHub page.

Using yolov8_basics.py

python yolov8_basics.py

Using yolov8_n_opencv.py

Use YoloV8 is an OpenCV way. Have control over detection on each frame and choose what happens per detection.

python yolov8_n_opencv.py

Code | yolov8_n_opencv.py |

import numpy as np
import cv2
from ultralytics import YOLO
import random

# opening the file in read mode
my_file = open("utils/coco.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()