detection.py

import cv2
import IPython
from PIL import ImageColor
from ultralytics import YOLO

class ObjectDetection:
    def __init__(self, model_name='Yolov8'):
        self.model_name = model_name
        self.model = self.load_model()
        self.classes = self.model.names
        self.device = 'cpu' 

    def load_model(self):
        model = YOLO(f"weights/{self.model_name}_best.pt")
        return model
    
    def v8_score_frame(self, frame):
            results = self.model(frame)
    
            labels = []
            confidences = []
            coords = []
    
            for result in results:
                boxes = result.boxes.cpu().numpy()
                
                label = boxes.cls
                conf = boxes.conf
                coord = boxes.xyxy
    
                labels.extend(label)
                confidences.extend(conf)
                coords.extend(coord)
    
            return labels, confidences, coords

    def get_coords(self, frame, row):
        return int(row[0]), int(row[1]), int(row[2]), int(row[3])
    
    def class_to_label(self, x):
        return self.classes[int(x)]
    
    def get_color(self, code):
        rgb = ImageColor.getcolor(code, "RGB")
        return rgb 
    
    def plot_bboxes(self, results, frame, threshold=0.5, box_color='red', text_color='white'):
        labels, conf, coord = results

        frame = frame.copy()
        box_color = self.get_color(box_color)
        text_color = self.get_color(text_color)

        for i in range(len(labels)):
            if conf[i] >= threshold:
                x1, y1, x2, y2 = self.get_coords(frame, coord[i])
                class_name = self.class_to_label(labels[i])

                cv2.rectangle(frame, (x1, y1), (x2, y2), box_color, 2)
                cv2.putText(frame, f"{class_name} - {conf[i]*100:.2f}%", (x1, y1), cv2.FONT_HERSHEY_COMPLEX, 0.5, text_color)
        
        return frame