Delete detection.py

detection.py

@@ -1,157 +0,0 @@
-
-from ultralytics import YOLOv10
-import os
-import torch
-
-def yolov10_inference(image, model_id, image_size, conf_threshold):
-    model = YOLOv10.from_pretrained(f'jameslahm/{model_id}')
-    results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
-    detections = []
-    if results and len(results) > 0:
-        for result in results:
-            for box in result.boxes:
-                detections.append({
-                    "coords": box.xyxy.cpu().numpy(),
-                    "class": result.names[int(box.cls.cpu())],
-                    "conf": box.conf.cpu().numpy()
-                    #"bbox": box.xyxy.cpu().numpy().tolist()
-                })
-    return results[0].plot() if results and len(results) > 0 else image, detections
-
-def calculate_iou(boxA, boxB):
-    xA = max(boxA[0], boxB[0])
-    yA = max(boxA[1], boxB[1])
-    xB = min(boxA[2], boxB[2])
-    yB = min(boxA[3], boxB[3])
-    interArea = max(0, xB - xA) * max(0, yB - yA)
-    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
-    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
-    iou = interArea / float(boxAArea + boxBArea - interArea)
-    return iou
-
-
-
-def calculate_detection_metrics(detections_true, detections_pred):
-    true_positives = 0
-    pred_positives = len(detections_pred)
-    real_positives = len(detections_true)
-    ious = []
-    for pred in detections_pred:
-        for real in detections_true:
-            if pred['class'] == real['class']:
-                iou = calculate_iou(pred['coords'].flatten(), real['coords'].flatten())
-                if iou >= 0.5:
-                    true_positives += 1
-                    ious.append(iou)
-                    break
-    precision = true_positives / pred_positives if pred_positives > 0 else 0
-    recall = true_positives / real_positives if real_positives > 0 else 0
-    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
-    average_iou = sum(ious) / len(ious) if ious else 0
-    return {"Precision": precision, "Recall": recall, "F1-Score": f1_score, "IOU": average_iou}
-
-
-
-
-def read_kitti_annotations(file_path):
-    ground_truths = []
-    with open(file_path, 'r') as file:
-        for line in file:
-            parts = line.strip().split()
-            if parts[0] != 'DontCare':  # Ignorar 'DontCare'
-                class_label = parts[0].lower()  # Clase en minúscula
-                bbox = [float(parts[4]), float(parts[5]), float(parts[6]), float(parts[7])]
-                ground_truths.append({'class': class_label, 'bbox': bbox})
-    return ground_truths
-
-
-def save_detections(detections, output_dir, filename='detections.txt'):
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir)
-    with open(os.path.join(output_dir, filename), 'w') as file:
-        for detection in detections:
-            class_label = detection['class']
-            bbox = ','.join(map(str, detection['bbox']))
-            file.write(f"{class_label},[{bbox}]\n")
-
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-
-def yolov10_inference_1(image, model_id, image_size, conf_threshold):
-    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-    model = YOLOv10.from_pretrained(f'jameslahm/{model_id}').to(device)
-    results = model.predict(source=image, imgsz=image_size, conf=conf_threshold)
-    detections = []
-    if results and len(results) > 0:
-        for result in results:
-            for box in result.boxes:
-                detections.append({
-                    #"coords": box.xyxy.cpu().numpy(),
-                    "class": result.names[int(box.cls.cpu())],
-                    #"conf": box.conf.cpu().numpy()
-
-                    "bbox": box.xyxy.cpu().numpy().tolist()
-                })
-    return results[0].plot() if results and len(results) > 0 else image, detections
-
-
-def calculate_iou_1(boxA, boxB):
-    # Asegúrate de que boxA y boxB son listas planas de flotantes
-    # Esto es necesario porque la función max() y min() requieren comparar elementos directamente
-    boxA = [float(num) for sublist in boxA for num in sublist] if isinstance(boxA[0], list) else [float(num) for num in boxA]
-    boxB = [float(num) for sublist in boxB for num in sublist] if isinstance(boxB[0], list) else [float(num) for num in boxB]
-
-    # Determina las coordenadas de la intersección
-    xA = max(boxA[0], boxB[0])
-    yA = max(boxA[1], boxB[1])
-    xB = min(boxA[2], boxB[2])
-    yB = min(boxA[3], boxB[3])
-
-    # Calcula el área de la intersección
-    interArea = max(0, xB - xA) * max(0, yB - yA)
-
-    # Calcula el área de cada cuadro delimitador y la unión
-    boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1])
-    boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1])
-    unionArea = boxAArea + boxBArea - interArea
-
-    # Calcula el IoU
-    iou = interArea / float(unionArea)
-    return iou
-
-
-def calculate_detection_metrics_1(detections_true, detections_pred):
-    true_positives = 0
-    pred_positives = len(detections_pred)
-    real_positives = len(detections_true)
-    ious = []
-    for pred in detections_pred:
-        pred_bbox = pred['bbox']
-        pred_class = pred['class']
-        for real in detections_true:
-            real_bbox = real['bbox']
-            real_class = real['class']
-            if pred_class == real_class:
-                iou = calculate_iou_1(pred_bbox, real_bbox)
-                if iou >= 0.5:
-                    true_positives += 1
-                    ious.append(iou)
-                    break
-    precision = true_positives / pred_positives if pred_positives > 0 else 0
-    recall = true_positives / real_positives if real_positives > 0 else 0
-    f1_score = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
-    average_iou = sum(ious) / len(ious) if ious else 0
-    return {"Precision": precision, "Recall": recall, "F1-Score": f1_score, "IOU": average_iou}
-
-