128 lines
3.8 KiB
Python
128 lines
3.8 KiB
Python
import abc
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from pathlib import Path
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import pandas as pd
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import pandera as pa
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from dodgeballtracker.typing import VideoFrame, DetectedObject, Color, DetectedObject_
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import matplotlib
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import cv2
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class DetectedObjectBox:
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def __init__(self, min_threshold: float, color: Color):
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self.min_threshold = min_threshold
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self.color = color
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def __lt__(self, other):
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return self.min_threshold < other.min_threshold
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@classmethod
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def from_viridis(cls, min_threshold):
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cm = matplotlib.colormaps['viridis']
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color: Color = tuple((int(255 * cm(min_threshold)[i]) for i in range(3)))
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return cls(min_threshold, color)
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def create_video_writer(video_cap: cv2.VideoCapture, output_filename: Path) -> cv2.VideoWriter:
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# grab the width, height, and fps of the frames in the video stream.
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frame_width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fps = int(video_cap.get(cv2.CAP_PROP_FPS))
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# initialize the FourCC and a video writer object
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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writer = cv2.VideoWriter(str(output_filename.absolute()),
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fourcc,
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fps,
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(frame_width, frame_height))
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return writer
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class VideoWriter(abc.ABC):
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@abc.abstractmethod
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def write_frame(self, frame: VideoFrame) -> None:
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pass
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@abc.abstractmethod
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def plot_boxes_to_frame(self, data: pd.DataFrame, frame: VideoFrame) -> None:
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pass
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@abc.abstractmethod
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def close(self) -> None:
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pass
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@abc.abstractmethod
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def wait(self) -> bool:
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pass
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class NoopVideoWriter(VideoWriter):
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def plot_boxes_to_frame(self, boxes: pd.DataFrame, frame: VideoFrame) -> None:
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pass
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def write_frame(self, frame: VideoFrame) -> None:
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pass
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def close(self) -> None:
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pass
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def wait(self) -> bool:
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return False
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class MP4VVideoWriter(VideoWriter):
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def __init__(self, video_writer: cv2.VideoWriter, thresholds: list[DetectedObjectBox]) -> None:
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self._video_writer = video_writer
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self._thresholds = thresholds
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def write_frame(self, frame: VideoFrame) -> None:
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self._video_writer.write(frame)
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def plot_box_to_frame(self, data: pd.Series, frame: VideoFrame) -> None:
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confidence = float(data.score)
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# data = data.map(int)
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for threshold in self._thresholds:
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if threshold.min_threshold < confidence:
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start_point = data[['xmin', 'ymin']].array.astype('int32')
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end_point = data[['xmax', 'ymax']].array.astype('int32')
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cv2.rectangle(frame, start_point, end_point, threshold.color, 2)
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return
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@pa.decorators.check_io(boxes=DetectedObject_)
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def plot_boxes_to_frame(self, boxes: pd.DataFrame, frame: VideoFrame) -> None:
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boxes_that_passe_threshold = boxes[boxes['score'] > 0.5]
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for i, box in boxes_that_passe_threshold.iterrows():
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self.plot_box_to_frame(box, frame)
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def close(self) -> None:
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self._video_writer.release()
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cv2.destroyAllWindows()
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def wait(self) -> bool:
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return False
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@classmethod
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def from_frame(cls, input_: Path, output: Path, thresholds: list[float]) -> VideoWriter:
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object_drawers = [DetectedObjectBox.from_viridis(t) for t in thresholds]
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video_cap = cv2.VideoCapture(str(input_))
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try:
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return cls(create_video_writer(video_cap, output), object_drawers)
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finally:
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video_cap.release()
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class MP4VVideoWriterWithFrame(MP4VVideoWriter):
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def write_frame(self, frame: VideoFrame) -> None:
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super().write_frame(frame)
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cv2.imshow("Frame", frame)
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def wait(self) -> bool:
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return cv2.waitKey(1) == ord("q")
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