保存检测结果¶

Supervision 提供了便捷的方式将检测结果保存为 .CSV 和 .JSON 文件，以便进行离线处理。本指南将演示如何使用 Inference、 Ultralytics 或 Transformers 包执行视频推理，并使用 sv.CSVSink 和 sv.JSONSink 保存其结果。

执行检测¶

首先，您需要从目标检测或分割模型中获取预测结果。您可以在我们的如何检测和标注指南中了解更多相关信息。

InferenceUltralyticsTransformers

import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

for frame in frames_generator:

    results = model.infer(image)[0]
    detections = sv.Detections.from_inference(results)

import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

for frame in frames_generator:

    results = model(frame)[0]
    detections = sv.Detections.from_ultralytics(results)

import torch
import supervision as sv
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

for frame in frames_generator:

    frame = sv.cv2_to_pillow(frame)
    inputs = processor(images=frame, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)

    width, height = frame.size
    target_size = torch.tensor([[height, width]])
    results = processor.post_process_object_detection(
        outputs=outputs, target_sizes=target_size)[0]
    detections = sv.Detections.from_transformers(results)

以 CSV 格式保存检测结果¶

要将检测结果保存到 .CSV 文件，请打开我们的 sv.CSVSink，然后将推理产生的 sv.Detections 对象传递给它。其字段将被解析并保存到磁盘。

InferenceUltralyticsTransformers

import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame in frames_generator:

        results = model.infer(image)[0]
        detections = sv.Detections.from_inference(results)
        sink.append(detections, {})

import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame in frames_generator:

        results = model(frame)[0]
        detections = sv.Detections.from_ultralytics(results)
        sink.append(detections, {})

import torch
import supervision as sv
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame in frames_generator:

        frame = sv.cv2_to_pillow(frame)
        inputs = processor(images=frame, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = frame.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        detections = sv.Detections.from_transformers(results)
        sink.append(detections, {})

x_min	y_min	x_max	y_max	class_id	confidence	class_name
2941.14	1269.31	3220.77	1500.67	2	0.8517	car
944.889	899.641	1235.42	1308.80	7	0.6752	truck
1439.78	1077.79	1621.27	1231.40	2	0.6450	car

自定义字段¶

除了 sv.Detections 中的常规字段外， sv.CSVSink 还允许您向每行添加自定义信息，这些信息可以通过 custom_data 字典传递。让我们利用此功能来保存检测来源的帧索引信息。

InferenceUltralyticsTransformers

import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        results = model.infer(image)[0]
        detections = sv.Detections.from_inference(results)
        sink.append(detections, {"frame_index": frame_index})

import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        results = model(frame)[0]
        detections = sv.Detections.from_ultralytics(results)
        sink.append(detections, {"frame_index": frame_index})

import torch
import supervision as sv
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.CSVSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        frame = sv.cv2_to_pillow(frame)
        inputs = processor(images=frame, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = frame.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        detections = sv.Detections.from_transformers(results)
        sink.append(detections, {"frame_index": frame_index})

x_min	y_min	x_max	y_max	class_id	confidence	class_name
2941.14	1269.31	3220.77	1500.67	2	0.8517	car
944.889	899.641	1235.42	1308.80	7	0.6752	truck
1439.78	1077.79	1621.27	1231.40	2	0.6450	car

以 JSON 格式保存检测结果¶

如果您倾向于将结果保存为 .JSON 文件而不是 .CSV 文件，您只需将 sv.CSVSink 替换为 sv.JSONSink。

InferenceUltralyticsTransformers

import supervision as sv
from inference import get_model

model = get_model(model_id="yolov8n-640")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        results = model.infer(image)[0]
        detections = sv.Detections.from_inference(results)
        sink.append(detections, {"frame_index": frame_index})

import supervision as sv
from ultralytics import YOLO

model = YOLO("yolov8n.pt")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        results = model(frame)[0]
        detections = sv.Detections.from_ultralytics(results)
        sink.append(detections, {"frame_index": frame_index})

import torch
import supervision as sv
from transformers import DetrImageProcessor, DetrForObjectDetection

processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")
frames_generator = sv.get_video_frames_generator(<SOURCE_VIDEO_PATH>)

with sv.JSONSink(<TARGET_CSV_PATH>) as sink:
    for frame_index, frame in enumerate(frames_generator):

        frame = sv.cv2_to_pillow(frame)
        inputs = processor(images=frame, return_tensors="pt")

        with torch.no_grad():
            outputs = model(**inputs)

        width, height = frame.size
        target_size = torch.tensor([[height, width]])
        results = processor.post_process_object_detection(
            outputs=outputs, target_sizes=target_size)[0]
        detections = sv.Detections.from_transformers(results)
        sink.append(detections, {"frame_index": frame_index})

保存检测结果¶

执行检测¶

以 CSV 格式保存检测结果¶

自定义字段¶

以 JSON 格式保存检测结果¶

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