Delete detection.py

detection.py

@@ -1,59 +0,0 @@
-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='orange', text_color='black'):
-        labels, conf, coord = results
-        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