93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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import os
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from pathlib import Path
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from typing import Any
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from ultralytics.solutions.solutions import BaseSolution, SolutionResults
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from ultralytics.utils.plotting import save_one_box
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class ObjectCropper(BaseSolution):
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"""
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A class to manage the cropping of detected objects in a real-time video stream or images.
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This class extends the BaseSolution class and provides functionality for cropping objects based on detected bounding
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boxes. The cropped images are saved to a specified directory for further analysis or usage.
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Attributes:
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crop_dir (str): Directory where cropped object images are stored.
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crop_idx (int): Counter for the total number of cropped objects.
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iou (float): IoU (Intersection over Union) threshold for non-maximum suppression.
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conf (float): Confidence threshold for filtering detections.
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Methods:
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process: Crop detected objects from the input image and save them to the output directory.
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Examples:
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>>> cropper = ObjectCropper()
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>>> frame = cv2.imread("frame.jpg")
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>>> processed_results = cropper.process(frame)
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>>> print(f"Total cropped objects: {cropper.crop_idx}")
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"""
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def __init__(self, **kwargs: Any) -> None:
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"""
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Initialize the ObjectCropper class for cropping objects from detected bounding boxes.
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Args:
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**kwargs (Any): Keyword arguments passed to the parent class and used for configuration.
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crop_dir (str): Path to the directory for saving cropped object images.
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"""
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super().__init__(**kwargs)
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self.crop_dir = self.CFG["crop_dir"] # Directory for storing cropped detections
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if not os.path.exists(self.crop_dir):
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os.mkdir(self.crop_dir) # Create directory if it does not exist
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if self.CFG["show"]:
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self.LOGGER.warning(
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f"show=True disabled for crop solution, results will be saved in the directory named: {self.crop_dir}"
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)
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self.crop_idx = 0 # Initialize counter for total cropped objects
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self.iou = self.CFG["iou"]
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self.conf = self.CFG["conf"]
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def process(self, im0) -> SolutionResults:
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"""
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Crop detected objects from the input image and save them as separate images.
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Args:
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im0 (numpy.ndarray): The input image containing detected objects.
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Returns:
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(SolutionResults): A SolutionResults object containing the total number of cropped objects and processed
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image.
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Examples:
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>>> cropper = ObjectCropper()
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>>> frame = cv2.imread("image.jpg")
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>>> results = cropper.process(frame)
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>>> print(f"Total cropped objects: {results.total_crop_objects}")
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"""
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with self.profilers[0]:
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results = self.model.predict(
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im0,
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classes=self.classes,
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conf=self.conf,
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iou=self.iou,
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device=self.CFG["device"],
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verbose=False,
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)[0]
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for box in results.boxes:
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self.crop_idx += 1
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save_one_box(
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box.xyxy,
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im0,
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file=Path(self.crop_dir) / f"crop_{self.crop_idx}.jpg",
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BGR=True,
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)
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# Return SolutionResults
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return SolutionResults(plot_im=im0, total_crop_objects=self.crop_idx)
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