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56994b0
replace MMDetection Vizualizer with Supervision Annotators (#4)
Browse files- replace MMDetection Visualizer with Supervision Annotators (bcf1bcb768d4c280c2519a4098dccc59ac3ce0e7)
Co-authored-by: Piotr Skalski <SkalskiP@users.noreply.huggingface.co>
- requirements.txt +1 -1
- tools/demo.py +25 -16
requirements.txt
CHANGED
@@ -9,7 +9,7 @@ addict
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yapf
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numpy
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opencv-python
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supervision==0.
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ftfy
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regex
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pot
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yapf
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numpy
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opencv-python
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supervision==0.18.0
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ftfy
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regex
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pot
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tools/demo.py
CHANGED
@@ -1,5 +1,6 @@
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# Copyright (c) Tencent Inc. All rights reserved.
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import os
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import argparse
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import os.path as osp
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from functools import partial
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@@ -11,6 +12,7 @@ import onnxsim
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import torch
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import gradio as gr
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import numpy as np
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from PIL import Image
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from torchvision.ops import nms
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from mmengine.config import Config, ConfigDict, DictAction
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@@ -23,6 +25,8 @@ from mmyolo.registry import RUNNERS
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from yolo_world.easydeploy.model import DeployModel, MMYOLOBackend
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def parse_args():
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parser = argparse.ArgumentParser(
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@@ -65,27 +69,32 @@ def run_image(runner,
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output = runner.model.test_step(data_batch)[0]
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pred_instances = output.pred_instances
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pred_instances = pred_instances[
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pred_instances.scores.float() > score_thr]
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if len(pred_instances.scores) > max_num_boxes:
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indices = pred_instances.scores.float().topk(max_num_boxes)[1]
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pred_instances = pred_instances[indices]
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image = np.array(image)
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draw_gt=False,
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out_file=image_path,
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pred_score_thr=score_thr)
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image = Image.open(image_path)
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return image
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# Copyright (c) Tencent Inc. All rights reserved.
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import os
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import cv2
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import argparse
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import os.path as osp
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from functools import partial
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import torch
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import gradio as gr
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import numpy as np
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import supervision as sv
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from PIL import Image
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from torchvision.ops import nms
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from mmengine.config import Config, ConfigDict, DictAction
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from yolo_world.easydeploy.model import DeployModel, MMYOLOBackend
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BOUNDING_BOX_ANNOTATOR = sv.BoundingBoxAnnotator()
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LABEL_ANNOTATOR = sv.LabelAnnotator(text_color=sv.Color.BLACK)
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def parse_args():
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parser = argparse.ArgumentParser(
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output = runner.model.test_step(data_batch)[0]
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pred_instances = output.pred_instances
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keep = nms(pred_instances.bboxes, pred_instances.scores, iou_threshold=nms_thr)
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pred_instances = pred_instances[keep]
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pred_instances = pred_instances[pred_instances.scores.float() > score_thr]
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if len(pred_instances.scores) > max_num_boxes:
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indices = pred_instances.scores.float().topk(max_num_boxes)[1]
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pred_instances = pred_instances[indices]
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pred_instances = pred_instances.cpu().numpy()
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detections = sv.Detections(
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xyxy=pred_instances['bboxes'],
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class_id=pred_instances['labels'],
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confidence=pred_instances['scores']
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)
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labels = [
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f"{texts[class_id][0]} {confidence:0.2f}"
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for class_id, confidence
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in zip(detections.class_id, detections.confidence)
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]
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image = np.array(image)
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image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
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image = BOUNDING_BOX_ANNOTATOR.annotate(image, detections)
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image = LABEL_ANNOTATOR.annotate(image, detections, labels=labels)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image = Image.fromarray(image)
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return image
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