ultralytics 8.3.54 New Streamlit inference Solution (#18316)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Signed-off-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: UltralyticsAssistant <web@ultralytics.com> Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Muhammad Rizwan Munawar
GitHub
parent
5b76bed7d0
commit
51026a9a4a
13 changed files with 251 additions and 188 deletions
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@ -55,7 +55,7 @@ See below for a quickstart install and usage examples, and see our [Docs](https:
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<details open>
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<summary>Install</summary>
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Pip install the ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
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Pip install the Ultralytics package including all [requirements](https://github.com/ultralytics/ultralytics/blob/main/pyproject.toml) in a [**Python>=3.8**](https://www.python.org/) environment with [**PyTorch>=1.8**](https://pytorch.org/get-started/locally/).
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[](https://pypi.org/project/ultralytics/) [](https://www.pepy.tech/projects/ultralytics) [](https://pypi.org/project/ultralytics/)
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@ -18,7 +18,7 @@
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[](https://www.pepy.tech/projects/ultralytics)
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[](https://pypi.org/project/ultralytics/)
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To install the ultralytics package in developer mode, ensure you have Git and Python 3 installed on your system. Then, follow these steps:
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To install the Ultralytics package in developer mode, ensure you have Git and Python 3 installed on your system. Then, follow these steps:
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1. Clone the ultralytics repository to your local machine using Git:
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@ -38,7 +38,7 @@ To install the ultralytics package in developer mode, ensure you have Git and Py
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pip install -e '.[dev]'
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```
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- This command installs the ultralytics package along with all development dependencies, allowing you to modify the package code and have the changes immediately reflected in your Python environment.
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- This command installs the Ultralytics package along with all development dependencies, allowing you to modify the package code and have the changes immediately reflected in your Python environment.
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## 🚀 Building and Serving Locally
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@ -43,7 +43,9 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
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=== "CLI"
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```bash
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yolo streamlit-predict
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yolo solutions inference
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yolo solutions inference model="path/to/model/file.pt"
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```
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=== "Python"
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@ -51,7 +53,11 @@ Streamlit makes it simple to build and deploy interactive web applications. Comb
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```python
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from ultralytics import solutions
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solutions.inference()
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inf = solutions.Inference(
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model="yolo11n.pt", # You can use any model that Ultralytics support, i.e. YOLO11, or custom trained model
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)
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inf.inference()
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### Make sure to run the file using command `streamlit run <file-name.py>`
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```
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@ -67,8 +73,11 @@ You can optionally supply a specific model in Python:
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```python
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from ultralytics import solutions
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# Pass a model as an argument
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solutions.inference(model="path/to/model.pt")
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inf = solutions.Inference(
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model="yolo11n.pt", # You can use any model that Ultralytics support, i.e. YOLO11, YOLOv10
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)
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inf.inference()
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### Make sure to run the file using command `streamlit run <file-name.py>`
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```
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@ -111,7 +120,11 @@ Then, you can create a basic Streamlit application to run live inference:
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```python
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from ultralytics import solutions
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solutions.inference()
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inf = solutions.Inference(
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model="yolo11n.pt", # You can use any model that Ultralytics support, i.e. YOLO11, YOLOv10
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)
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inf.inference()
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### Make sure to run the file using command `streamlit run <file-name.py>`
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```
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@ -119,7 +132,7 @@ Then, you can create a basic Streamlit application to run live inference:
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=== "CLI"
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```bash
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yolo streamlit-predict
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yolo solutions inference
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```
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For more details on the practical setup, refer to the [Streamlit Application Code section](#streamlit-application-code) of the documentation.
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@ -51,10 +51,6 @@ keywords: Ultralytics, YOLO, configuration, cfg2dict, get_cfg, check_cfg, save_d
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<br><br><hr><br>
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## ::: ultralytics.cfg.handle_streamlit_inference
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<br><br><hr><br>
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## ::: ultralytics.cfg.parse_key_value_pair
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<br><br><hr><br>
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@ -11,6 +11,6 @@ keywords: Ultralytics, YOLOv8, live inference, real-time object detection, Strea
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<br>
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## ::: ultralytics.solutions.streamlit_inference.inference
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## ::: ultralytics.solutions.streamlit_inference.Inference
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<br><br>
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@ -8,13 +8,14 @@ import torch
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from ultralytics.utils import ASSETS, yaml_load
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from ultralytics.utils.checks import check_requirements, check_yaml
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class RTDETR:
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"""RTDETR object detection model class for handling inference and visualization."""
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def __init__(self, model_path, img_path, conf_thres=0.5, iou_thres=0.5):
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"""
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Initializes the RTDETR object with the specified parameters.
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Args:
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model_path: Path to the ONNX model file.
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img_path: Path to the input image.
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@ -71,11 +72,17 @@ class RTDETR:
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# Draw a filled rectangle as the background for the label text
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cv2.rectangle(
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self.img, (int(label_x), int(label_y - label_height)), (int(label_x + label_width), int(label_y + label_height)), color, cv2.FILLED
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self.img,
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(int(label_x), int(label_y - label_height)),
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(int(label_x + label_width), int(label_y + label_height)),
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color,
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cv2.FILLED,
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)
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# Draw the label text on the image
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cv2.putText(self.img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
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cv2.putText(
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self.img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA
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)
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def preprocess(self):
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"""
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@ -110,8 +117,7 @@ class RTDETR:
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def bbox_cxcywh_to_xyxy(self, boxes):
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"""
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Converts bounding boxes from (center x, center y, width, height) format
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to (x_min, y_min, x_max, y_max) format.
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Converts bounding boxes from (center x, center y, width, height) format to (x_min, y_min, x_max, y_max) format.
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Args:
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boxes (numpy.ndarray): An array of shape (N, 4) where each row represents
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@ -176,7 +182,7 @@ class RTDETR:
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def main(self):
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"""
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Executes the detection on the input image using the ONNX model.
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Returns:
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np.array: Output image with annotations.
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"""
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@ -189,6 +195,7 @@ class RTDETR:
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# Process and return the model output
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return self.postprocess(model_output)
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if __name__ == "__main__":
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# Set up argument parser for command-line arguments
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parser = argparse.ArgumentParser()
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@ -210,4 +217,4 @@ if __name__ == "__main__":
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# Display the annotated output image
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cv2.namedWindow("Output", cv2.WINDOW_NORMAL)
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cv2.imshow("Output", output_image)
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cv2.waitKey(0)
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cv2.waitKey(0)
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@ -82,4 +82,4 @@ def test_instance_segmentation():
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@pytest.mark.slow
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def test_streamlit_predict():
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"""Test streamlit predict live inference solution."""
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solutions.inference()
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solutions.Inference().inference()
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = "8.3.53"
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__version__ = "8.3.54"
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import os
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@ -42,6 +42,7 @@ SOLUTION_MAP = {
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"workout": ("AIGym", "monitor"),
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"analytics": ("Analytics", "process_data"),
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"trackzone": ("TrackZone", "trackzone"),
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"inference": ("Inference", "inference"),
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"help": None,
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}
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@ -97,7 +98,10 @@ SOLUTIONS_HELP_MSG = f"""
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yolo solutions analytics analytics_type="pie"
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6. Track objects within specific zones
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yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
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yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
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7. Streamlit real-time webcam inference GUI
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yolo streamlit-predict
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"""
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CLI_HELP_MSG = f"""
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Arguments received: {str(['yolo'] + ARGV[1:])}. Ultralytics 'yolo' commands use the following syntax:
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@ -121,13 +125,10 @@ CLI_HELP_MSG = f"""
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4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
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yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
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5. Streamlit real-time webcam inference GUI
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yolo streamlit-predict
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6. Ultralytics solutions usage
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5. Ultralytics solutions usage
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yolo solutions count or in {list(SOLUTION_MAP.keys())[1:-1]} source="path/to/video/file.mp4"
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7. Run special commands:
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6. Run special commands:
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yolo help
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yolo checks
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yolo version
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@ -636,6 +637,9 @@ def handle_yolo_solutions(args: List[str]) -> None:
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Run analytics with custom configuration:
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>>> handle_yolo_solutions(["analytics", "conf=0.25", "source=path/to/video/file.mp4"])
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Run inference with custom configuration, requires Streamlit version 1.29.0 or higher.
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>>> handle_yolo_solutions(["inference", "model=yolo11n.pt"])
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Notes:
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- Default configurations are merged from DEFAULT_SOL_DICT and DEFAULT_CFG_DICT
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- Arguments can be provided in the format 'key=value' or as boolean flags
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@ -645,7 +649,9 @@ def handle_yolo_solutions(args: List[str]) -> None:
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- For 'analytics' solution, frame numbers are tracked for generating analytical graphs
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- Video processing can be interrupted by pressing 'q'
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- Processes video frames sequentially and saves output in .avi format
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- If no source is specified, downloads and uses a default sample video
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- If no source is specified, downloads and uses a default sample video\
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- The inference solution will be launched using the 'streamlit run' command.
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- The Streamlit app file is located in the Ultralytics package directory.
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"""
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full_args_dict = {**DEFAULT_SOL_DICT, **DEFAULT_CFG_DICT} # arguments dictionary
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overrides = {}
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if args and args[0] == "help": # Add check for return if user call `yolo solutions help`
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return
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cls, method = SOLUTION_MAP[s_n] # solution class name, method name and default source
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if s_n == "inference":
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checks.check_requirements("streamlit>=1.29.0")
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LOGGER.info("💡 Loading Ultralytics live inference app...")
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subprocess.run(
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[ # Run subprocess with Streamlit custom argument
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"streamlit",
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"run",
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str(ROOT / "solutions/streamlit_inference.py"),
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"--server.headless",
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"true",
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overrides["model"],
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]
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)
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else:
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cls, method = SOLUTION_MAP[s_n] # solution class name, method name and default source
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from ultralytics import solutions # import ultralytics solutions
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from ultralytics import solutions # import ultralytics solutions
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solution = getattr(solutions, cls)(IS_CLI=True, **overrides) # get solution class i.e ObjectCounter
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process = getattr(solution, method) # get specific function of class for processing i.e, count from ObjectCounter
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solution = getattr(solutions, cls)(IS_CLI=True, **overrides) # get solution class i.e ObjectCounter
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process = getattr(
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solution, method
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) # get specific function of class for processing i.e, count from ObjectCounter
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cap = cv2.VideoCapture(solution.CFG["source"]) # read the video file
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cap = cv2.VideoCapture(solution.CFG["source"]) # read the video file
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# extract width, height and fps of the video file, create save directory and initialize video writer
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import os # for directory creation
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from pathlib import Path
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# extract width, height and fps of the video file, create save directory and initialize video writer
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import os # for directory creation
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from pathlib import Path
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from ultralytics.utils.files import increment_path # for output directory path update
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from ultralytics.utils.files import increment_path # for output directory path update
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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if s_n == "analytics": # analytical graphs follow fixed shape for output i.e w=1920, h=1080
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w, h = 1920, 1080
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save_dir = increment_path(Path("runs") / "solutions" / "exp", exist_ok=False)
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save_dir.mkdir(parents=True, exist_ok=True) # create the output directory
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vw = cv2.VideoWriter(os.path.join(save_dir, "solution.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
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if s_n == "analytics": # analytical graphs follow fixed shape for output i.e w=1920, h=1080
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w, h = 1920, 1080
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save_dir = increment_path(Path("runs") / "solutions" / "exp", exist_ok=False)
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save_dir.mkdir(parents=True, exist_ok=True) # create the output directory
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vw = cv2.VideoWriter(os.path.join(save_dir, "solution.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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try: # Process video frames
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f_n = 0 # frame number, required for analytical graphs
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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break
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frame = process(frame, f_n := f_n + 1) if s_n == "analytics" else process(frame)
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vw.write(frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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finally:
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cap.release()
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def handle_streamlit_inference():
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"""
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Open the Ultralytics Live Inference Streamlit app for real-time object detection.
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This function initializes and runs a Streamlit application designed for performing live object detection using
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Ultralytics models. It checks for the required Streamlit package and launches the app.
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Examples:
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>>> handle_streamlit_inference()
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Notes:
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- Requires Streamlit version 1.29.0 or higher.
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- The app is launched using the 'streamlit run' command.
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- The Streamlit app file is located in the Ultralytics package directory.
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"""
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checks.check_requirements("streamlit>=1.29.0")
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LOGGER.info("💡 Loading Ultralytics Live Inference app...")
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subprocess.run(["streamlit", "run", ROOT / "solutions/streamlit_inference.py", "--server.headless", "true"])
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try: # Process video frames
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f_n = 0 # frame number, required for analytical graphs
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while cap.isOpened():
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success, frame = cap.read()
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if not success:
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break
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frame = process(frame, f_n := f_n + 1) if s_n == "analytics" else process(frame)
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vw.write(frame)
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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finally:
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cap.release()
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def parse_key_value_pair(pair: str = "key=value"):
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@ -853,7 +855,6 @@ def entrypoint(debug=""):
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"login": lambda: handle_yolo_hub(args),
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"logout": lambda: handle_yolo_hub(args),
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"copy-cfg": copy_default_cfg,
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"streamlit-predict": lambda: handle_streamlit_inference(),
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"solutions": lambda: handle_yolo_solutions(args[1:]),
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}
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full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
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@ -10,7 +10,7 @@ from .queue_management import QueueManager
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from .region_counter import RegionCounter
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from .security_alarm import SecurityAlarm
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from .speed_estimation import SpeedEstimator
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from .streamlit_inference import inference
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from .streamlit_inference import Inference
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from .trackzone import TrackZone
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__all__ = (
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@ -23,7 +23,7 @@ __all__ = (
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"QueueManager",
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"SpeedEstimator",
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"Analytics",
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"inference",
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"Inference",
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"RegionCounter",
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"TrackZone",
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"SecurityAlarm",
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@ -5,7 +5,9 @@ import json
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import cv2
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import numpy as np
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from ultralytics.solutions.solutions import LOGGER, BaseSolution, check_requirements
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from ultralytics.solutions.solutions import BaseSolution
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from ultralytics.utils import LOGGER
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from ultralytics.utils.checks import check_requirements
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from ultralytics.utils.plotting import Annotator
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@ -1,6 +1,7 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from ultralytics.solutions.solutions import LOGGER, BaseSolution
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from ultralytics.solutions.solutions import BaseSolution
|
||||
from ultralytics.utils import LOGGER
|
||||
from ultralytics.utils.plotting import Annotator, colors
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,145 +4,188 @@ import io
|
|||
import time
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
|
||||
from ultralytics import YOLO
|
||||
from ultralytics.utils import LOGGER
|
||||
from ultralytics.utils.checks import check_requirements
|
||||
from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
|
||||
|
||||
|
||||
def inference(model=None):
|
||||
"""Performs real-time object detection on video input using YOLO in a Streamlit web application."""
|
||||
check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds
|
||||
import streamlit as st
|
||||
class Inference:
|
||||
"""
|
||||
A class to perform object detection, image classification, image segmentation and pose estimation inference using
|
||||
Streamlit and Ultralytics YOLO models. It provides the functionalities such as loading models, configuring settings,
|
||||
uploading video files, and performing real-time inference.
|
||||
|
||||
from ultralytics import YOLO
|
||||
Attributes:
|
||||
st (module): Streamlit module for UI creation.
|
||||
temp_dict (dict): Temporary dictionary to store the model path.
|
||||
model_path (str): Path to the loaded model.
|
||||
model (YOLO): The YOLO model instance.
|
||||
source (str): Selected video source.
|
||||
enable_trk (str): Enable tracking option.
|
||||
conf (float): Confidence threshold.
|
||||
iou (float): IoU threshold for non-max suppression.
|
||||
vid_file_name (str): Name of the uploaded video file.
|
||||
selected_ind (list): List of selected class indices.
|
||||
|
||||
# Hide main menu style
|
||||
menu_style_cfg = """<style>MainMenu {visibility: hidden;}</style>"""
|
||||
Methods:
|
||||
web_ui: Sets up the Streamlit web interface with custom HTML elements.
|
||||
sidebar: Configures the Streamlit sidebar for model and inference settings.
|
||||
source_upload: Handles video file uploads through the Streamlit interface.
|
||||
configure: Configures the model and loads selected classes for inference.
|
||||
inference: Performs real-time object detection inference.
|
||||
|
||||
# Main title of streamlit application
|
||||
main_title_cfg = """<div><h1 style="color:#FF64DA; text-align:center; font-size:40px;
|
||||
font-family: 'Archivo', sans-serif; margin-top:-50px;margin-bottom:20px;">
|
||||
Ultralytics YOLO Streamlit Application
|
||||
</h1></div>"""
|
||||
Examples:
|
||||
>>> inf = solutions.Inference(model="path/to/model/file.pt") # Model is not necessary argument.
|
||||
>>> inf.inference()
|
||||
"""
|
||||
|
||||
# Subtitle of streamlit application
|
||||
sub_title_cfg = """<div><h4 style="color:#042AFF; text-align:center;
|
||||
font-family: 'Archivo', sans-serif; margin-top:-15px; margin-bottom:50px;">
|
||||
Experience real-time object detection on your webcam with the power of Ultralytics YOLO! 🚀</h4>
|
||||
</div>"""
|
||||
def __init__(self, **kwargs):
|
||||
"""
|
||||
Initializes the Inference class, checking Streamlit requirements and setting up the model path.
|
||||
|
||||
# Set html page configuration
|
||||
st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide", initial_sidebar_state="auto")
|
||||
Args:
|
||||
**kwargs (Dict): Additional keyword arguments for model configuration.
|
||||
"""
|
||||
check_requirements("streamlit>=1.29.0") # scope imports for faster ultralytics package load speeds
|
||||
import streamlit as st
|
||||
|
||||
# Append the custom HTML
|
||||
st.markdown(menu_style_cfg, unsafe_allow_html=True)
|
||||
st.markdown(main_title_cfg, unsafe_allow_html=True)
|
||||
st.markdown(sub_title_cfg, unsafe_allow_html=True)
|
||||
self.st = st
|
||||
|
||||
# Add ultralytics logo in sidebar
|
||||
with st.sidebar:
|
||||
logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg"
|
||||
st.image(logo, width=250)
|
||||
self.temp_dict = {"model": None} # Temporary dict to store the model path
|
||||
self.temp_dict.update(kwargs)
|
||||
|
||||
# Add elements to vertical setting menu
|
||||
st.sidebar.title("User Configuration")
|
||||
self.model_path = None # Store model file name with path
|
||||
if self.temp_dict["model"] is not None:
|
||||
self.model_path = self.temp_dict["model"]
|
||||
|
||||
# Add video source selection dropdown
|
||||
source = st.sidebar.selectbox(
|
||||
"Video",
|
||||
("webcam", "video"),
|
||||
)
|
||||
LOGGER.info(f"Ultralytics Solutions: ✅ {self.temp_dict}")
|
||||
|
||||
vid_file_name = ""
|
||||
if source == "video":
|
||||
vid_file = st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"])
|
||||
if vid_file is not None:
|
||||
g = io.BytesIO(vid_file.read()) # BytesIO Object
|
||||
vid_location = "ultralytics.mp4"
|
||||
with open(vid_location, "wb") as out: # Open temporary file as bytes
|
||||
out.write(g.read()) # Read bytes into file
|
||||
vid_file_name = "ultralytics.mp4"
|
||||
elif source == "webcam":
|
||||
vid_file_name = 0
|
||||
def web_ui(self):
|
||||
"""Sets up the Streamlit web interface with custom HTML elements."""
|
||||
menu_style_cfg = """<style>MainMenu {visibility: hidden;}</style>""" # Hide main menu style
|
||||
|
||||
# Add dropdown menu for model selection
|
||||
available_models = [x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolo11")]
|
||||
if model:
|
||||
available_models.insert(0, model.split(".pt")[0]) # insert model without suffix as *.pt is added later
|
||||
# Main title of streamlit application
|
||||
main_title_cfg = """<div><h1 style="color:#FF64DA; text-align:center; font-size:40px; margin-top:-50px;
|
||||
font-family: 'Archivo', sans-serif; margin-bottom:20px;">Ultralytics YOLO Streamlit Application</h1></div>"""
|
||||
|
||||
selected_model = st.sidebar.selectbox("Model", available_models)
|
||||
with st.spinner("Model is downloading..."):
|
||||
model = YOLO(f"{selected_model.lower()}.pt") # Load the YOLO model
|
||||
class_names = list(model.names.values()) # Convert dictionary to list of class names
|
||||
st.success("Model loaded successfully!")
|
||||
# Subtitle of streamlit application
|
||||
sub_title_cfg = """<div><h4 style="color:#042AFF; text-align:center; font-family: 'Archivo', sans-serif;
|
||||
margin-top:-15px; margin-bottom:50px;">Experience real-time object detection on your webcam with the power
|
||||
of Ultralytics YOLO! 🚀</h4></div>"""
|
||||
|
||||
# Multiselect box with class names and get indices of selected classes
|
||||
selected_classes = st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
|
||||
selected_ind = [class_names.index(option) for option in selected_classes]
|
||||
# Set html page configuration and append custom HTML
|
||||
self.st.set_page_config(page_title="Ultralytics Streamlit App", layout="wide", initial_sidebar_state="auto")
|
||||
self.st.markdown(menu_style_cfg, unsafe_allow_html=True)
|
||||
self.st.markdown(main_title_cfg, unsafe_allow_html=True)
|
||||
self.st.markdown(sub_title_cfg, unsafe_allow_html=True)
|
||||
|
||||
if not isinstance(selected_ind, list): # Ensure selected_options is a list
|
||||
selected_ind = list(selected_ind)
|
||||
def sidebar(self):
|
||||
"""Configures the Streamlit sidebar for model and inference settings."""
|
||||
with self.st.sidebar: # Add Ultralytics LOGO
|
||||
logo = "https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Original.svg"
|
||||
self.st.image(logo, width=250)
|
||||
|
||||
enable_trk = st.sidebar.radio("Enable Tracking", ("Yes", "No"))
|
||||
conf = float(st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01))
|
||||
iou = float(st.sidebar.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01))
|
||||
self.st.sidebar.title("User Configuration") # Add elements to vertical setting menu
|
||||
self.source = self.st.sidebar.selectbox(
|
||||
"Video",
|
||||
("webcam", "video"),
|
||||
) # Add source selection dropdown
|
||||
self.enable_trk = self.st.sidebar.radio("Enable Tracking", ("Yes", "No")) # Enable object tracking
|
||||
self.conf = float(self.st.sidebar.slider("Confidence Threshold", 0.0, 1.0, 0.25, 0.01)) # Slider for confidence
|
||||
self.iou = float(self.st.sidebar.slider("IoU Threshold", 0.0, 1.0, 0.45, 0.01)) # Slider for NMS threshold
|
||||
|
||||
col1, col2 = st.columns(2)
|
||||
org_frame = col1.empty()
|
||||
ann_frame = col2.empty()
|
||||
col1, col2 = self.st.columns(2)
|
||||
self.org_frame = col1.empty()
|
||||
self.ann_frame = col2.empty()
|
||||
self.fps_display = self.st.sidebar.empty() # Placeholder for FPS display
|
||||
|
||||
fps_display = st.sidebar.empty() # Placeholder for FPS display
|
||||
def source_upload(self):
|
||||
"""Handles video file uploads through the Streamlit interface."""
|
||||
self.vid_file_name = ""
|
||||
if self.source == "video":
|
||||
vid_file = self.st.sidebar.file_uploader("Upload Video File", type=["mp4", "mov", "avi", "mkv"])
|
||||
if vid_file is not None:
|
||||
g = io.BytesIO(vid_file.read()) # BytesIO Object
|
||||
with open("ultralytics.mp4", "wb") as out: # Open temporary file as bytes
|
||||
out.write(g.read()) # Read bytes into file
|
||||
self.vid_file_name = "ultralytics.mp4"
|
||||
elif self.source == "webcam":
|
||||
self.vid_file_name = 0
|
||||
|
||||
if st.sidebar.button("Start"):
|
||||
videocapture = cv2.VideoCapture(vid_file_name) # Capture the video
|
||||
def configure(self):
|
||||
"""Configures the model and loads selected classes for inference."""
|
||||
# Add dropdown menu for model selection
|
||||
available_models = [x.replace("yolo", "YOLO") for x in GITHUB_ASSETS_STEMS if x.startswith("yolo11")]
|
||||
if self.model_path: # If user provided the custom model, insert model without suffix as *.pt is added later
|
||||
available_models.insert(0, self.model_path.split(".pt")[0])
|
||||
selected_model = self.st.sidebar.selectbox("Model", available_models)
|
||||
|
||||
if not videocapture.isOpened():
|
||||
st.error("Could not open webcam.")
|
||||
with self.st.spinner("Model is downloading..."):
|
||||
self.model = YOLO(f"{selected_model.lower()}.pt") # Load the YOLO model
|
||||
class_names = list(self.model.names.values()) # Convert dictionary to list of class names
|
||||
self.st.success("Model loaded successfully!")
|
||||
|
||||
stop_button = st.button("Stop") # Button to stop the inference
|
||||
# Multiselect box with class names and get indices of selected classes
|
||||
selected_classes = self.st.sidebar.multiselect("Classes", class_names, default=class_names[:3])
|
||||
self.selected_ind = [class_names.index(option) for option in selected_classes]
|
||||
|
||||
while videocapture.isOpened():
|
||||
success, frame = videocapture.read()
|
||||
if not success:
|
||||
st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
|
||||
break
|
||||
if not isinstance(self.selected_ind, list): # Ensure selected_options is a list
|
||||
self.selected_ind = list(self.selected_ind)
|
||||
|
||||
prev_time = time.time() # Store initial time for FPS calculation
|
||||
def inference(self):
|
||||
"""Performs real-time object detection inference."""
|
||||
self.web_ui() # Initialize the web interface
|
||||
self.sidebar() # Create the sidebar
|
||||
self.source_upload() # Upload the video source
|
||||
self.configure() # Configure the app
|
||||
|
||||
# Store model predictions
|
||||
if enable_trk == "Yes":
|
||||
results = model.track(frame, conf=conf, iou=iou, classes=selected_ind, persist=True)
|
||||
else:
|
||||
results = model(frame, conf=conf, iou=iou, classes=selected_ind)
|
||||
annotated_frame = results[0].plot() # Add annotations on frame
|
||||
if self.st.sidebar.button("Start"):
|
||||
stop_button = self.st.button("Stop") # Button to stop the inference
|
||||
cap = cv2.VideoCapture(self.vid_file_name) # Capture the video
|
||||
if not cap.isOpened():
|
||||
self.st.error("Could not open webcam.")
|
||||
while cap.isOpened():
|
||||
success, frame = cap.read()
|
||||
if not success:
|
||||
st.warning("Failed to read frame from webcam. Please make sure the webcam is connected properly.")
|
||||
break
|
||||
|
||||
# Calculate model FPS
|
||||
curr_time = time.time()
|
||||
fps = 1 / (curr_time - prev_time)
|
||||
prev_time = time.time() # Store initial time for FPS calculation
|
||||
|
||||
# display frame
|
||||
org_frame.image(frame, channels="BGR")
|
||||
ann_frame.image(annotated_frame, channels="BGR")
|
||||
# Store model predictions
|
||||
if self.enable_trk == "Yes":
|
||||
results = self.model.track(
|
||||
frame, conf=self.conf, iou=self.iou, classes=self.selected_ind, persist=True
|
||||
)
|
||||
else:
|
||||
results = self.model(frame, conf=self.conf, iou=self.iou, classes=self.selected_ind)
|
||||
annotated_frame = results[0].plot() # Add annotations on frame
|
||||
|
||||
if stop_button:
|
||||
videocapture.release() # Release the capture
|
||||
torch.cuda.empty_cache() # Clear CUDA memory
|
||||
st.stop() # Stop streamlit app
|
||||
fps = 1 / (time.time() - prev_time) # Calculate model FPS
|
||||
|
||||
# Display FPS in sidebar
|
||||
fps_display.metric("FPS", f"{fps:.2f}")
|
||||
if stop_button:
|
||||
cap.release() # Release the capture
|
||||
self.st.stop() # Stop streamlit app
|
||||
|
||||
# Release the capture
|
||||
videocapture.release()
|
||||
self.fps_display.metric("FPS", f"{fps:.2f}") # Display FPS in sidebar
|
||||
self.org_frame.image(frame, channels="BGR") # Display original frame
|
||||
self.ann_frame.image(annotated_frame, channels="BGR") # Display processed frame
|
||||
|
||||
# Clear CUDA memory
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
# Destroy window
|
||||
cv2.destroyAllWindows()
|
||||
cap.release() # Release the capture
|
||||
cv2.destroyAllWindows() # Destroy window
|
||||
|
||||
|
||||
# Main function call
|
||||
if __name__ == "__main__":
|
||||
inference()
|
||||
import sys # Import the sys module for accessing command-line arguments
|
||||
|
||||
model = None # Initialize the model variable as None
|
||||
|
||||
# Check if a model name is provided as a command-line argument
|
||||
args = len(sys.argv)
|
||||
if args > 1:
|
||||
model = args # Assign the first argument as the model name
|
||||
|
||||
# Create an instance of the Inference class and run inference
|
||||
Inference(model=model).inference()
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue