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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,184 @@ | ||
| import os | ||
| import numpy as np | ||
| import cv2 | ||
| import multiprocessing | ||
| import functools | ||
| import pandas as pd | ||
| from typing import Union, Optional, Tuple, List | ||
| from simba.utils.read_write import find_files_of_filetypes_in_directory, get_fn_ext,read_df, find_video_of_file, write_df, get_video_meta_data, read_frm_of_video, find_core_cnt, remove_a_folder, concatenate_videos_in_folder | ||
| from simba.utils.checks import check_all_file_names_are_represented_in_video_log, check_valid_dataframe, check_int | ||
| from simba.utils.enums import Formats | ||
| from simba.mixins.config_reader import ConfigReader | ||
| from simba.utils.warnings import FrameRangeWarning | ||
| from simba.utils.printing import SimbaTimer | ||
|
|
||
| def _egocentric_aligner(frm_range: np.ndarray, | ||
| video_path: Union[str, os.PathLike], | ||
| temp_dir: Union[str, os.PathLike], | ||
| centers: List[Tuple[int, int]], | ||
| rotation_vectors: np.ndarray, | ||
| target: Tuple[int, int]): | ||
|
|
||
| video_meta = get_video_meta_data(video_path=video_path) | ||
| cap = cv2.VideoCapture(video_path) | ||
| batch, frm_range = frm_range[0], frm_range[1] | ||
| save_path = os.path.join(temp_dir, f'{batch}.mp4') | ||
| fourcc = cv2.VideoWriter_fourcc(*f'{Formats.MP4_CODEC.value}') | ||
| writer = cv2.VideoWriter(save_path, fourcc, video_meta['fps'], (video_meta['width'], video_meta['height'])) | ||
|
|
||
| for frm_cnt, frm_id in enumerate(frm_range): | ||
| img = read_frm_of_video(video_path=cap, frame_index=frm_id) | ||
| R, center = rotation_vectors[frm_id], centers[frm_id] | ||
| M_rotate = np.hstack([R, np.array([[-center[0] * R[0, 0] - center[1] * R[0, 1] + center[0]], [-center[0] * R[1, 0] - center[1] * R[1, 1] + center[1]]])]) | ||
| rotated_frame = cv2.warpAffine(img, M_rotate, (video_meta['width'], video_meta['height'])) | ||
| translation_x = target[0] - center[0] | ||
| translation_y = target[1] - center[1] | ||
| M_translate = np.float32([[1, 0, translation_x], [0, 1, translation_y]]) | ||
| final_frame = cv2.warpAffine(rotated_frame, M_translate, (video_meta['width'], video_meta['height'])) | ||
| writer.write(final_frame) | ||
| print(f'Creating frame {frm_id} (CPU core: {batch+1}).') | ||
|
|
||
| cap.release() | ||
| writer.release() | ||
| return batch+1 | ||
|
|
||
| class EgocentricalAligner(ConfigReader): | ||
|
|
||
| def __init__(self, | ||
| config_path: Union[str, os.PathLike], | ||
| save_dir: Union[str, os.PathLike], | ||
| data_dir: Optional[Union[str, os.PathLike]] = None, | ||
| anchor_1: Optional[str] = 'tail_base', | ||
| anchor_2: Optional[str] = 'nose', | ||
| direction: int = 0, | ||
| anchor_location: Optional[Tuple[int, int]] = (250, 250), | ||
| rotate_video: Optional[bool] = False, | ||
| cores: Optional[int] = -1): | ||
| """ | ||
| Aligns and rotates movement data and associated video frames based on specified anchor points to produce an egocentric view of the subject. The class aligns frames around a selected anchor point, optionally rotating the subject to a consistent direction and saving the output video. | ||
| .. video:: _static/img/EgocentricalAligner.webm | ||
| :width: 800 | ||
| :autoplay: | ||
| :loop: | ||
| :param Union[str, os.PathLike] config_path: Path to the configuration file. | ||
| :param Union[str, os.PathLike] save_dir: Directory where the processed output will be saved. | ||
| :param Optional[Union[str, os.PathLike]] data_dir: Directory containing CSV files with movement data. | ||
| :param Optional[str] anchor_1: Primary anchor point (e.g., 'tail_base') around which the alignment centers. | ||
| :param Optional[str] anchor_2: Secondary anchor point (e.g., 'nose') defining the alignment direction. | ||
| :param int direction: Target angle, in degrees, for alignment; e.g., `0` aligns along the x-axis. | ||
| :param Optional[Tuple[int, int]] anchor_location: Pixel location in the output where `anchor_1` should appear; default is `(250, 250)`. | ||
| :param Optional[bool] rotate_video: Whether to rotate the video to align with the specified direction. | ||
| :param Optional[int] cores: Number of CPU cores to use for video rotation; `-1` uses all available cores. | ||
| :example: | ||
| >>> aligner = EgocentricalAligner(config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini", rotate_video=True, anchor_1='tail_base', anchor_2='nose', data_dir=r'C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location\test', save_dir=r"C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location\test\bg_temp\rotated") | ||
| >>> aligner.run() | ||
| """ | ||
|
|
||
| ConfigReader.__init__(self, config_path=config_path, read_video_info=True, create_logger=False) | ||
| if data_dir is None: | ||
| self.data_paths = find_files_of_filetypes_in_directory(directory=self.outlier_corrected_dir, extensions=['.csv']) | ||
| else: | ||
| self.data_paths = find_files_of_filetypes_in_directory(directory=data_dir, extensions=['.csv']) | ||
| check_all_file_names_are_represented_in_video_log(video_info_df=self.video_info_df, data_paths=self.data_paths) | ||
| self.anchor_1_cols = [f'{anchor_1}_x'.lower(), f'{anchor_1}_y'.lower()] | ||
| self.anchor_2_cols = [f'{anchor_2}_x'.lower(), f'{anchor_2}_y'.lower()] | ||
| self.rotate_video, self.save_dir = rotate_video, save_dir | ||
| self.anchor_1, self.anchor_2 = anchor_1, anchor_2 | ||
| self.target_angle = np.deg2rad(direction) | ||
| self.anchor_location = anchor_location | ||
| check_int(name='cores', value=cores, min_value=-1, max_value=find_core_cnt()[0]) | ||
| if cores == -1: | ||
| self.cores = find_core_cnt()[0] | ||
| else: | ||
| self.cores = cores | ||
|
|
||
| def run(self): | ||
| for file_cnt, file_path in enumerate(self.data_paths): | ||
| _, self.video_name, _ = get_fn_ext(filepath=file_path) | ||
| save_path = os.path.join(self.save_dir, f'{self.video_name}.{self.file_type}') | ||
| df = read_df(file_path=file_path, file_type=self.file_type) | ||
| original_cols, self.file_path = list(df.columns), file_path | ||
| df.columns = [x.lower() for x in list(df.columns)] | ||
| check_valid_dataframe(df=df, source=self.__class__.__name__, valid_dtypes=Formats.NUMERIC_DTYPES.value, required_fields=self.anchor_1_cols + self.anchor_2_cols) | ||
| self.body_parts_lst = [x.lower() for x in self.body_parts_lst] | ||
| bp_cols = [x for x in df.columns if not x.endswith('_p')] | ||
| anchor_1_idx = self.body_parts_lst.index(self.anchor_1) | ||
| anchor_2_idx = self.body_parts_lst.index(self.anchor_2) | ||
| data_arr = df[bp_cols].values.reshape(len(df), len(self.body_parts_lst), 2).astype(np.int32) | ||
| results_arr = np.zeros_like(data_arr) | ||
| self.rotation_angles, self.rotation_vectors, self.centers, self.deltas = [], [], [], [] | ||
| for frame_index in range(data_arr.shape[0]): | ||
| frame_points = data_arr[frame_index] | ||
| frame_anchor_1 = frame_points[anchor_1_idx] | ||
| self.centers.append(tuple(frame_anchor_1)) | ||
| frame_anchor_2 = frame_points[anchor_2_idx] | ||
| delta_x, delta_y = frame_anchor_2[0] - frame_anchor_1[0], frame_anchor_2[1] - frame_anchor_1[1] | ||
| self.deltas.append((delta_x, delta_x)) | ||
| current_angle = np.arctan2(delta_y, delta_x) | ||
| rotate_angle = self.target_angle - current_angle | ||
| self.rotation_angles.append(rotate_angle) | ||
| cos_theta, sin_theta = np.cos(rotate_angle), np.sin(rotate_angle) | ||
| R = np.array([[cos_theta, -sin_theta], [sin_theta, cos_theta]]) | ||
| self.rotation_vectors.append(R) | ||
| keypoints_translated = frame_points - frame_anchor_1 | ||
| keypoints_rotated = np.dot(keypoints_translated, R.T) | ||
| anchor_1_position_after_rotation = keypoints_rotated[anchor_1_idx] | ||
| translation_to_target = np.array(self.anchor_location) - anchor_1_position_after_rotation | ||
| keypoints_aligned = keypoints_rotated + translation_to_target | ||
| results_arr[frame_index] = keypoints_aligned | ||
|
|
||
| results_arr = results_arr.reshape(len(df), len(bp_cols)) | ||
| self.out_df = pd.DataFrame(results_arr, columns=bp_cols) | ||
| df.update(self.out_df) | ||
| df.columns = original_cols | ||
| write_df(df=df, file_type=self.file_type, save_path=save_path) | ||
| if self.rotate_video: | ||
| self.run_video_rotation() | ||
|
|
||
| def run_video_rotation(self): | ||
| video_timer = SimbaTimer(start=True) | ||
| video_path = find_video_of_file(video_dir=self.video_dir, filename=self.video_name, raise_error=True) | ||
| video_meta = get_video_meta_data(video_path=video_path) | ||
| save_path = os.path.join(self.save_dir, f'{self.video_name}.mp4') | ||
| temp_dir = os.path.join(self.save_dir, 'temp') | ||
| if not os.path.isdir(temp_dir): | ||
| os.makedirs(temp_dir) | ||
| else: | ||
| remove_a_folder(folder_dir=temp_dir) | ||
| os.makedirs(temp_dir) | ||
| if video_meta['frame_count'] != len(self.out_df): | ||
| FrameRangeWarning(msg=f'The video {video_path} contains {video_meta["frame_count"]} frames while the file {self.file_path} contains {len(self.out_df)} frames', source=self.__class__.__name__) | ||
| frm_list = np.arange(0, video_meta['frame_count']) | ||
| frm_list = np.array_split(frm_list, self.cores) | ||
| frm_list = [(cnt, x) for cnt, x in enumerate(frm_list)] | ||
| print(f"Creating rotated videos, multiprocessing (chunksize: {self.multiprocess_chunksize}, cores: {self.cores})...") | ||
| with multiprocessing.Pool(self.cores, maxtasksperchild=self.maxtasksperchild) as pool: | ||
| constants = functools.partial(_egocentric_aligner, | ||
| temp_dir=temp_dir, | ||
| video_path=video_path, | ||
| centers=self.centers, | ||
| rotation_vectors=self.rotation_vectors, | ||
| target=self.anchor_location) | ||
| for cnt, result in enumerate(pool.imap(constants, frm_list, chunksize=self.multiprocess_chunksize)): | ||
| print(f"Rotate batch {result}/{self.cores} complete...") | ||
| pool.terminate() | ||
| pool.join() | ||
|
|
||
| concatenate_videos_in_folder(in_folder=temp_dir, save_path=save_path, remove_splits=True, gpu=False) | ||
| video_timer.stop_timer() | ||
| print(f"Egocentric rotation video {save_path} complete (elapsed time: {video_timer.elapsed_time_str}s) ...") | ||
|
|
||
|
|
||
| # if __name__ == "__main__": | ||
| # aligner = EgocentricalAligner(config_path=r"C:\troubleshooting\mitra\project_folder\project_config.ini", | ||
| # rotate_video=True, | ||
| # anchor_1='tail_base', | ||
| # anchor_2='nose', | ||
| # data_dir=r'C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location\test', | ||
| # save_dir=r"C:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location\test\bg_temp\rotated") | ||
| # aligner.run() | ||
| # | ||
|
|
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