From 2028686c23914a78b4ae75c3fba332d1d1327c37 Mon Sep 17 00:00:00 2001 From: sronilsson Date: Fri, 22 Nov 2024 11:35:06 -0500 Subject: [PATCH] gibbs --- docs/_static/custom.css | 2 +- docs/_static/img/get_video_slic.webm | Bin 0 -> 570508 bytes docs/simba.data_processors.rst | 17 + simba/data_processors/circling_detector.py | 122 ++++++ simba/data_processors/cuda/geometry.py | 2 +- simba/data_processors/cuda/image.py | 82 +++- simba/data_processors/freezing_detector.py | 130 ++++++ simba/mixins/circular_statistics.py | 4 +- simba/mixins/feature_extraction_mixin.py | 1 - .../feature_extraction_supplement_mixin.py | 3 +- simba/utils/read_write.py | 3 +- simba/video_processors/video_processing.py | 372 ++++++++++++------ 12 files changed, 608 insertions(+), 130 deletions(-) create mode 100644 docs/_static/img/get_video_slic.webm create mode 100644 simba/data_processors/circling_detector.py create mode 100644 simba/data_processors/freezing_detector.py diff --git a/docs/_static/custom.css 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simba.data_processors.circling_detector + :members: + :undoc-members: + + +Heuristic freezing detector +------------------------------------------------------------ + +.. automodule:: simba.data_processors.freezing_detector + :members: + :undoc-members: + diff --git a/simba/data_processors/circling_detector.py b/simba/data_processors/circling_detector.py new file mode 100644 index 000000000..25f652494 --- /dev/null +++ b/simba/data_processors/circling_detector.py @@ -0,0 +1,122 @@ +import os +import numpy as np +import pandas as pd +from numba import typed +from simba.utils.read_write import find_files_of_filetypes_in_directory, read_df, get_fn_ext, read_video_info +from simba.mixins.circular_statistics import CircularStatisticsMixin +from simba.mixins.feature_extraction_mixin import FeatureExtractionMixin +from simba.mixins.timeseries_features_mixin import TimeseriesFeatureMixin +from simba.mixins.config_reader import ConfigReader +from simba.utils.enums import Formats +from typing import Union, Optional +from simba.utils.checks import check_if_dir_exists, check_str, check_valid_dataframe, check_int, check_all_file_names_are_represented_in_video_log +from simba.utils.data import detect_bouts, plug_holes_shortest_bout +from simba.utils.printing import stdout_success + +CIRCLING = 'CIRCLING' + +class CirclingDetector(ConfigReader): + + """ + Detect circling using heuristic rules. + + .. important:: + + Circling is detected as :underline:`present` when **the circular range of the animal is above the ``circular_range_threshold`` within the preceding ``time_threshold``** AND + **the movement of the animal (defined as the sum of the center movement) is above the ``movement_threshold`` within the preceding ``time_threshold``.** + + Circling is detected as :underline:`absent` when not present. + + :param Union[str, os.PathLike] data_dir: Path to directory containing pose-estimated body-part data in CSV format. + :param Union[str, os.PathLike] config_path: Path to SimBA project config file. + :param Optional[str] nose_name: The name of the pose-estimated nose body-part. Defaults to 'nose'. + :param Optional[str] left_ear_name: The name of the pose-estimated left ear body-part. Defaults to 'left_ear'. + :param Optional[str] right_ear_name: The name of the pose-estimated right ear body-part. Defaults to 'right_ear'. + :param Optional[str] tail_base_name: The name of the pose-estimated tail base body-part. Defaults to 'tail_base'. + :param Optional[str] center_name: The name of the pose-estimated center body-part. Defaults to 'center'. + :param Optional[int] time_threshold: The time window in preceding seconds in which to evaluate the animals circular range. Default: 10. + :param Optional[int] circular_range_threshold: A value in degrees, between 0-360. + :param Optional[int] movement_threshold: A movement threshold in millimeters. + :param Optional[Union[str, os.PathLike]] save_dir: Directory where to store the results. If None, then results are stored in the ``logs`` directory of the SimBA project. + + References + ---------- + .. [1] Sabnis et al., Visual detection of seizures in mice using supervised machine learning, `biorxiv`, doi: https://doi.org/10.1101/2024.05.29.596520. + + :example: + >>> CirclingDetector(data_dir=r'D:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location', config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini") + """ + + def __init__(self, + data_dir: Union[str, os.PathLike], + config_path: Union[str, os.PathLike], + nose_name: Optional[str] = 'nose', + left_ear_name: Optional[str] = 'left_ear', + right_ear_name: Optional[str] = 'right_ear', + tail_base_name: Optional[str] = 'tail_base', + center_name: Optional[str] = 'center', + time_threshold: Optional[int] = 10, + circular_range_threshold: Optional[int] = 320, + movement_threshold: Optional[int] = 60, + save_dir: Optional[Union[str, os.PathLike]] = None): + + check_if_dir_exists(in_dir=data_dir) + for bp_name in [nose_name, left_ear_name, right_ear_name, tail_base_name]: check_str(name='body part name', value=bp_name, allow_blank=False) + self.data_paths = find_files_of_filetypes_in_directory(directory=data_dir, extensions=['.csv']) + ConfigReader.__init__(self, config_path=config_path, read_video_info=True, create_logger=False) + self.nose_heads = [f'{nose_name}_x'.lower(), f'{nose_name}_y'.lower()] + self.left_ear_heads = [f'{left_ear_name}_x'.lower(), f'{left_ear_name}_y'.lower()] + self.right_ear_heads = [f'{right_ear_name}_x'.lower(), f'{right_ear_name}_y'.lower()] + self.center_heads = [f'{center_name}_x'.lower(), f'{center_name}_y'.lower()] + self.required_field = self.nose_heads + self.left_ear_heads + self.right_ear_heads + self.save_dir = save_dir + if self.save_dir is None: + self.save_dir = os.path.join(self.logs_path, f'circling_data_{self.datetime}') + os.makedirs(self.save_dir) + else: + check_if_dir_exists(in_dir=self.save_dir) + self.time_threshold, self.circular_range_threshold, self.movement_threshold = time_threshold, circular_range_threshold, movement_threshold + + def run(self): + agg_results = pd.DataFrame(columns=['VIDEO', 'CIRCLING FRAMES', 'CIRCLING TIME (S)', 'CIRCLING BOUT COUNTS', 'CIRCLING PCT OF SESSION', 'VIDEO TOTAL FRAMES', 'VIDEO TOTAL TIME (S)']) + agg_results_path = os.path.join(self.save_dir, 'aggregate_circling_results.csv') + check_all_file_names_are_represented_in_video_log(video_info_df=self.video_info_df, data_paths=self.data_paths) + for file_cnt, file_path in enumerate(self.data_paths): + video_name = get_fn_ext(filepath=file_path)[1] + print(f'Analyzing {video_name} ({file_cnt+1}/{len(self.data_paths)})...') + save_file_path = os.path.join(self.save_dir, f'{video_name}.csv') + df = read_df(file_path=file_path, file_type='csv').reset_index(drop=True) + _, px_per_mm, fps = read_video_info(video_info_df=self.video_info_df, video_name=video_name) + df.columns = [str(x).lower() for x in df.columns] + check_valid_dataframe(df=df, valid_dtypes=Formats.NUMERIC_DTYPES.value, required_fields=self.required_field) + + nose_arr = df[self.nose_heads].values.astype(np.float32) + left_ear_arr = df[self.left_ear_heads].values.astype(np.float32) + right_ear_arr = df[self.right_ear_heads].values.astype(np.float32) + + center_shifted = FeatureExtractionMixin.create_shifted_df(df[self.center_heads]) + center_1, center_2 = center_shifted.iloc[:, 0:2].values, center_shifted.iloc[:, 2:4].values + + angle_degrees = CircularStatisticsMixin().direction_three_bps(nose_loc=nose_arr, left_ear_loc=left_ear_arr, right_ear_loc=right_ear_arr).astype(np.float32) + sliding_circular_range = CircularStatisticsMixin().sliding_circular_range(data=angle_degrees, time_windows=np.array([self.time_threshold], dtype=np.float64), fps=int(fps)).flatten() + movement = FeatureExtractionMixin.euclidean_distance(bp_1_x=center_1[:, 0].flatten(), bp_2_x=center_2[:, 0].flatten(), bp_1_y=center_1[:, 1].flatten(), bp_2_y=center_2[:, 1].flatten(), px_per_mm=2.15) + movement_sum = TimeseriesFeatureMixin.sliding_descriptive_statistics(data=movement.astype(np.float32), window_sizes=np.array([self.time_threshold], dtype=np.float64), sample_rate=fps, statistics=typed.List(["sum"])).astype(np.int32)[0].flatten() + + circling_idx = np.argwhere(sliding_circular_range >= self.circular_range_threshold).astype(np.int32).flatten() + movement_idx = np.argwhere(movement_sum >= self.movement_threshold).astype(np.int32).flatten() + circling_idx = [x for x in movement_idx if x in circling_idx] + df[CIRCLING] = 0 + df.loc[circling_idx, CIRCLING] = 1 + bouts = detect_bouts(data_df=df, target_lst=[CIRCLING], fps=fps) + df = plug_holes_shortest_bout(data_df=df, clf_name=CIRCLING, fps=fps, shortest_bout=100) + df.to_csv(save_file_path) + agg_results.loc[len(agg_results)] = [video_name, len(circling_idx), round(len(circling_idx) / fps, 4), len(bouts), round((len(circling_idx) / len(df)) * 100, 4), len(df), round(len(df)/fps, 2) ] + + agg_results.to_csv(agg_results_path) + stdout_success(msg=f'Results saved in {self.save_dir} directory.') + + + +# detector = CirclingDetector(data_dir=r'D:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location', config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini") +# detector.run() + diff --git a/simba/data_processors/cuda/geometry.py b/simba/data_processors/cuda/geometry.py index a1d3c44f6..e8994f5d1 100644 --- a/simba/data_processors/cuda/geometry.py +++ b/simba/data_processors/cuda/geometry.py @@ -7,7 +7,7 @@ import numpy as np from numba import cuda, njit -from simba.utils.checks import check_float, check_int, check_valid_array +from simba.utils.checks import check_float, check_valid_array from simba.utils.enums import Formats try: diff --git a/simba/data_processors/cuda/image.py b/simba/data_processors/cuda/image.py index f768a715e..7815884a7 100644 --- a/simba/data_processors/cuda/image.py +++ b/simba/data_processors/cuda/image.py @@ -20,6 +20,7 @@ import cv2 import numpy as np from numba import cuda +from cupyx.scipy.ndimage import rotate from simba.data_processors.cuda.utils import _cuda_mse from simba.mixins.image_mixin import ImageMixin @@ -170,13 +171,13 @@ def _average_3d_stack_cuda(image_stack: np.ndarray) -> np.ndarray: def create_average_frm_cuda(video_path: Union[str, os.PathLike], - start_frm: Optional[int] = None, - end_frm: Optional[int] = None, - start_time: Optional[str] = None, - end_time: Optional[str] = None, - save_path: Optional[Union[str, os.PathLike]] = None, - batch_size: Optional[int] = 6000, - verbose: Optional[bool] = False) -> Union[None, np.ndarray]: + start_frm: Optional[int] = None, + end_frm: Optional[int] = None, + start_time: Optional[str] = None, + end_time: Optional[str] = None, + save_path: Optional[Union[str, os.PathLike]] = None, + batch_size: Optional[int] = 6000, + verbose: Optional[bool] = False) -> Union[None, np.ndarray]: """ Computes the average frame using GPU acceleration from a specified range of frames or time interval in a video file. This average frame typically used for background substraction. @@ -908,5 +909,72 @@ def sliding_psnr(data: np.ndarray, _sliding_psnr[bpg, THREADS_PER_BLOCK](data_dev, stride_dev, results_dev) return results_dev.copy_to_host() +def rotate_img_stack_cupy(imgs: np.ndarray, + rotation_degrees: Optional[float] = 180, + batch_size: Optional[int] = 500) -> np.ndarray: + """ + Rotates a stack of images by a specified number of degrees using GPU acceleration with CuPy. + Accepts a 3D (single-channel images) or 4D (multichannel images) NumPy array, rotates each image in the stack by the specified degree around the center, and returns the result as a NumPy array. + :param np.ndarray imgs: The input stack of images to be rotated. Expected to be a NumPy array with 3 or 4 dimensions. 3D shape: (num_images, height, width) - 4D shape: (num_images, height, width, channels) + :param Optional[float] rotation_degrees: The angle by which the images should be rotated, in degrees. Must be between 1 and 359 degrees. Defaults to 180 degrees. + :param Optional[int] batch_size: Number of images to process on GPU in each batch. Decrease if data can't fit on GPU RAM. + :returns: A NumPy array containing the rotated images with the same shape as the input. + :rtype: np.ndarray + + :example: + >>> video_path = r"/mnt/c/troubleshooting/mitra/project_folder/videos/F0_gq_Saline_0626_clipped.mp4" + >>> imgs = read_img_batch_from_video_gpu(video_path=video_path) + >>> imgs = np.stack(np.array(list(imgs.values())), axis=0) + >>> imgs = rotate_img_stack_cupy(imgs=imgs, rotation=50) + """ + + check_valid_array(data=imgs, source=f'{rotate_img_stack_cupy.__name__} imgs', accepted_ndims=(3, 4)) + check_int(name=f'{rotate_img_stack_cupy.__name__} rotation', value=rotation_degrees, min_value=1, max_value=359) + results = cp.full_like(imgs, fill_value=np.nan, dtype=np.uint8) + for l in range(0, imgs.shape[0], batch_size): + r = l + batch_size + batch_imgs = cp.array(imgs[l:r]) + results[l:r] = rotate(input=batch_imgs, angle=rotation_degrees, axes=(2, 1), reshape=True) + return results.get() + +def rotate_video_cupy(video_path: Union[str, os.PathLike], + save_path: Optional[Union[str, os.PathLike]] = None, + rotation_degrees: Optional[float] = 180, + batch_cnt: Optional[int] = 1) -> None: + """ + Rotates a video by a specified angle using GPU acceleration and CuPy for image processing. + + :param Union[str, os.PathLike] video_path: Path to the input video file. + :param Optional[Union[str, os.PathLike]] save_path: Path to save the rotated video. If None, saves the video in the same directory as the input with '_rotated_' appended to the filename. + :param nptional[float] rotation_degrees: Degrees to rotate the video. Must be between 1 and 359 degrees. Default is 180. + :param Optional[int] batch_cnt: Number of batches to split the video frames into for processing. Higher values reduce memory usage. Default is 1. + :returns: None. + + :example: + >>> video_path = r"/mnt/c/troubleshooting/mitra/project_folder/videos/F0_gq_Saline_0626_clipped.mp4" + >>> rotate_video_cupy(video_path=video_path, rotation_degrees=45) + """ + + timer = SimbaTimer(start=True) + check_int(name=f'{rotate_img_stack_cupy.__name__} rotation', value=rotation_degrees, min_value=1, max_value=359) + check_int(name=f'{rotate_img_stack_cupy.__name__} batch_cnt', value=batch_cnt, min_value=1) + if save_path is None: + video_dir, video_name, _ = get_fn_ext(filepath=video_path) + save_path = os.path.join(video_dir, f'{video_name}_rotated_{rotation_degrees}.mp4') + video_meta_data = get_video_meta_data(video_path=video_path) + fourcc = cv2.VideoWriter_fourcc(*Formats.MP4_CODEC.value) + is_clr = ImageMixin.is_video_color(video=video_path) + frm_ranges = np.arange(0, video_meta_data['frame_count']) + frm_ranges = np.array_split(frm_ranges, batch_cnt) + for frm_batch, frm_range in enumerate(frm_ranges): + imgs = read_img_batch_from_video_gpu(video_path=video_path, start_frm=frm_range[0], end_frm=frm_range[-1]) + imgs = np.stack(np.array(list(imgs.values())), axis=0) + imgs = rotate_img_stack_cupy(imgs=imgs, rotation_degrees=rotation_degrees) + if frm_batch == 0: + writer = cv2.VideoWriter(save_path, fourcc, video_meta_data['fps'], (imgs.shape[2], imgs.shape[1]), isColor=is_clr) + for img in imgs: writer.write(img) + writer.release() + timer.stop_timer() + stdout_success(f'Rotated video saved at {save_path}', source=rotate_video_cupy.__name__) \ No newline at end of file diff --git a/simba/data_processors/freezing_detector.py b/simba/data_processors/freezing_detector.py new file mode 100644 index 000000000..e57578881 --- /dev/null +++ b/simba/data_processors/freezing_detector.py @@ -0,0 +1,130 @@ +import os +from typing import Union, Optional +import numpy as np +import pandas as pd +from numba import typed +from simba.utils.read_write import find_files_of_filetypes_in_directory, read_df, get_fn_ext, read_video_info +from simba.mixins.feature_extraction_mixin import FeatureExtractionMixin +from simba.mixins.timeseries_features_mixin import TimeseriesFeatureMixin +from simba.mixins.config_reader import ConfigReader +from simba.utils.checks import check_if_dir_exists, check_str, check_valid_dataframe, check_int, check_all_file_names_are_represented_in_video_log +from simba.utils.enums import Formats +from simba.utils.data import detect_bouts, plug_holes_shortest_bout +from simba.utils.printing import stdout_success + + +NAPE_X, NAPE_Y = 'nape_x', 'nape_y' +FREEZING = 'FREEZING' + +class FreezingDetector(ConfigReader): + + """ + Detect freezing behavior using heuristic rules. + + .. important:: + + Freezing is detected as :underline:`present` when ** the velocity (computed from the mean movement of the nape, nose, and tail-base body-parts) falls below + the movement threshold for the duration of the defined time-window or longer. + + Freezing is detected as :underline:`absent` when not present. + + :param Union[str, os.PathLike] data_dir: Path to directory containing pose-estimated body-part data in CSV format. + :param Union[str, os.PathLike] config_path: Path to SimBA project config file. + :param Optional[str] nose_name: The name of the pose-estimated nose body-part. Defaults to 'nose'. + :param Optional[str] left_ear_name: The name of the pose-estimated left ear body-part. Defaults to 'left_ear'. + :param Optional[str] right_ear_name: The name of the pose-estimated right ear body-part. Defaults to 'right_ear'. + :param Optional[str] tail_base_name: The name of the pose-estimated tail base body-part. Defaults to 'tail_base'. + :param Optional[int] time_window: The time window in preceding seconds in which to evaluate freezing. Default: 3. + :param Optional[int] movement_threshold: A movement threshold in millimeters per second. + :param Optional[Union[str, os.PathLike]] save_dir: Directory where to store the results. If None, then results are stored in the ``logs`` directory of the SimBA project. + + :example: + >>> FreezingDetector(data_dir=r'D:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location', config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini") + + References + ---------- + .. [1] Sabnis et al., Visual detection of seizures in mice using supervised machine learning, `biorxiv`, doi: https://doi.org/10.1101/2024.05.29.596520. + + """ + + def __init__(self, + data_dir: Union[str, os.PathLike], + config_path: Union[str, os.PathLike], + nose_name: Optional[str] = 'nose', + left_ear_name: Optional[str] = 'Left_ear', + right_ear_name: Optional[str] = 'right_ear', + tail_base_name: Optional[str] = 'tail_base', + time_window: Optional[int] = 3, + movement_threshold: Optional[int] = 5, + shortest_bout: Optional[int] = 100, + save_dir: Optional[Union[str, os.PathLike]] = None): + + check_if_dir_exists(in_dir=data_dir) + for bp_name in [nose_name, left_ear_name, right_ear_name, tail_base_name]: check_str(name='body part name', value=bp_name, allow_blank=False) + self.data_paths = find_files_of_filetypes_in_directory(directory=data_dir, extensions=['.csv']) + ConfigReader.__init__(self, config_path=config_path, read_video_info=True, create_logger=False) + self.nose_heads = [f'{nose_name}_x'.lower(), f'{nose_name}_y'.lower()] + self.left_ear_heads = [f'{left_ear_name}_x'.lower(), f'{left_ear_name}_y'.lower()] + self.right_ear_heads = [f'{right_ear_name}_x'.lower(), f'{right_ear_name}_y'.lower()] + self.tail_base_heads = [f'{tail_base_name}_x'.lower(), f'{tail_base_name}_y'.lower()] + self.required_field = self.nose_heads + self.left_ear_heads + self.right_ear_heads + self.tail_base_heads + check_int(name='time_window', value=time_window, min_value=1) + check_int(name='movement_threshold', value=movement_threshold, min_value=1) + self.save_dir = save_dir + if self.save_dir is None: + self.save_dir = os.path.join(self.logs_path, f'freezing_data_time_{time_window}s_{self.datetime}') + os.makedirs(self.save_dir) + else: + check_if_dir_exists(in_dir=self.save_dir) + self.time_window, self.movement_threshold = time_window, movement_threshold + self.movement_threshold, self.shortest_bout = movement_threshold, shortest_bout + self.run() + + def run(self): + agg_results = pd.DataFrame(columns=['VIDEO', 'FREEZING FRAMES', 'FREEZING TIME (S)', 'FREEZING BOUT COUNTS', 'FREEZING PCT OF SESSION', 'VIDEO TOTAL FRAMES', 'VIDEO TOTAL TIME (S)']) + agg_results_path = os.path.join(self.save_dir, 'aggregate_freezing_results.csv') + check_all_file_names_are_represented_in_video_log(video_info_df=self.video_info_df, data_paths=self.data_paths) + for file_cnt, file_path in enumerate(self.data_paths): + video_name = get_fn_ext(filepath=file_path)[1] + print(f'Analyzing {video_name}...') + save_file_path = os.path.join(self.save_dir, f'{video_name}.csv') + df = read_df(file_path=file_path, file_type='csv').reset_index(drop=True) + _, px_per_mm, fps = read_video_info(vid_info_df=self.video_info_df, video_name=video_name) + df.columns = [str(x).lower() for x in df.columns] + check_valid_dataframe(df=df, valid_dtypes=Formats.NUMERIC_DTYPES.value, required_fields=self.required_field) + nose_shifted = FeatureExtractionMixin.create_shifted_df(df[self.nose_heads]) + nose_1, nose_2 = nose_shifted.iloc[:, 0:2].values, nose_shifted.iloc[:, 2:4].values + nose_movement = FeatureExtractionMixin.euclidean_distance(bp_1_x=nose_1[:, 0].flatten(), bp_2_x=nose_2[:, 0].flatten(), bp_1_y=nose_1[:, 1].flatten(), bp_2_y=nose_2[:, 1].flatten(), px_per_mm=px_per_mm) + tail_base_shifted = FeatureExtractionMixin.create_shifted_df(df[self.tail_base_heads]) + tail_base_shifted_1, tail_base_shifted_2 = tail_base_shifted.iloc[:, 0:2].values, tail_base_shifted.iloc[:, 2:4].values + tail_base_movement = FeatureExtractionMixin.euclidean_distance(bp_1_x=tail_base_shifted_1[:, 0].flatten(), bp_2_x=tail_base_shifted_2[:, 0].flatten(), bp_1_y=tail_base_shifted_1[:, 1].flatten(), bp_2_y=tail_base_shifted_2[:, 1].flatten(), px_per_mm=px_per_mm) + left_ear_arr = df[self.left_ear_heads].values.astype(np.int64) + right_ear_arr = df[self.right_ear_heads].values.astype(np.int64) + nape_arr = pd.DataFrame(FeatureExtractionMixin.find_midpoints(bp_1=left_ear_arr, bp_2=right_ear_arr, percentile=np.float64(0.5)), columns=[NAPE_X, NAPE_Y]) + nape_shifted = FeatureExtractionMixin.create_shifted_df(nape_arr[[NAPE_X, NAPE_Y]]) + nape_shifted_1, nape_shifted_2 = nape_shifted.iloc[:, 0:2].values, nape_shifted.iloc[:, 2:4].values + nape_movement = FeatureExtractionMixin.euclidean_distance(bp_1_x=nape_shifted_1[:, 0].flatten(), bp_2_x=nape_shifted_2[:, 0].flatten(), bp_1_y=nape_shifted_1[:, 1].flatten(), bp_2_y=nape_shifted_2[:, 1].flatten(), px_per_mm=px_per_mm) + movement = np.hstack([nose_movement.reshape(-1, 1), nape_movement.reshape(-1, 1), tail_base_movement.reshape(-1, 1)]) + mean_movement = np.mean(movement, axis=1) + mm_s = TimeseriesFeatureMixin.sliding_descriptive_statistics(data=mean_movement.astype(np.float32), window_sizes=np.array([1], dtype=np.float64), sample_rate=int(fps), statistics=typed.List(["sum"]))[0].flatten() + freezing_idx = np.argwhere(mm_s <= self.movement_threshold).astype(np.int32).flatten() + df[FREEZING] = 0 + df.loc[freezing_idx, FREEZING] = 1 + df = plug_holes_shortest_bout(data_df=df, clf_name=FREEZING, fps=fps, shortest_bout=self.shortest_bout) + bouts = detect_bouts(data_df=df, target_lst=[FREEZING], fps=fps) + bouts = bouts[bouts['Bout_time'] >= self.time_window] + if len(bouts) > 0: + freezing_idx = list(bouts.apply(lambda x: list(range(int(x["Start_frame"]), int(x["End_frame"]) + 1)), 1)) + freezing_idx = [x for xs in freezing_idx for x in xs] + df.loc[freezing_idx, FREEZING] = 1 + else: + freezing_idx = [] + df.to_csv(save_file_path) + agg_results.loc[len(agg_results)] = [video_name, len(freezing_idx), round(len(freezing_idx) / fps, 4), len(bouts), round((len(freezing_idx) / len(df)) * 100, 4), len(df), round(len(df)/fps, 2) ] + + agg_results.to_csv(agg_results_path) + stdout_success(msg=f'Results saved in {self.save_dir} directory.') + +# +# FreezingDetector(data_dir=r'D:\troubleshooting\mitra\project_folder\csv\outlier_corrected_movement_location', +# config_path=r"D:\troubleshooting\mitra\project_folder\project_config.ini") \ No newline at end of file diff --git a/simba/mixins/circular_statistics.py b/simba/mixins/circular_statistics.py index fd0ec4307..e6b18b40d 100644 --- a/simba/mixins/circular_statistics.py +++ b/simba/mixins/circular_statistics.py @@ -1122,9 +1122,7 @@ def circular_hotspots(data: np.ndarray, bins: np.ndarray) -> np.ndarray: @staticmethod @njit("(float32[:], int64[:, :], float64, float64)") - def sliding_circular_hotspots( - data: np.ndarray, bins: np.ndarray, time_window: float, fps: float - ) -> np.ndarray: + def sliding_circular_hotspots(data: np.ndarray, bins: np.ndarray, time_window: float, fps: float) -> np.ndarray: """ Jitted compute of sliding circular hotspots in a dataset. Calculates circular hotspots in a time-series dataset by sliding a time window across the data and computing hotspot statistics for specified circular bins. diff --git a/simba/mixins/feature_extraction_mixin.py b/simba/mixins/feature_extraction_mixin.py index 223f80220..cbc118581 100644 --- a/simba/mixins/feature_extraction_mixin.py +++ b/simba/mixins/feature_extraction_mixin.py @@ -533,7 +533,6 @@ def create_shifted_df(df: pd.DataFrame, periods: int = 1, suffix: str = "_shifte >>> 2 89 41.0 """ - check data_df_shifted = df.shift(periods=periods) data_df_shifted = data_df_shifted.combine_first(df).add_suffix(suffix) return pd.concat([df, data_df_shifted], axis=1, join="inner").reset_index(drop=True) diff --git a/simba/mixins/feature_extraction_supplement_mixin.py b/simba/mixins/feature_extraction_supplement_mixin.py index f3c3fd120..fcbeb6ecb 100644 --- a/simba/mixins/feature_extraction_supplement_mixin.py +++ b/simba/mixins/feature_extraction_supplement_mixin.py @@ -179,8 +179,7 @@ def rolling_peak_count_ratio(data: np.ndarray, time_windows: np.ndarray, fps: in @staticmethod @jit(nopython=True) - def rolling_categorical_switches_ratio(data: np.ndarray, time_windows: np.ndarray, fps: int - ) -> np.ndarray: + def rolling_categorical_switches_ratio(data: np.ndarray, time_windows: np.ndarray, fps: int) -> np.ndarray: """ Compute the ratio of in categorical feature switches within rolling windows. diff --git a/simba/utils/read_write.py b/simba/utils/read_write.py index c3a8755aa..a5f26e399 100644 --- a/simba/utils/read_write.py +++ b/simba/utils/read_write.py @@ -434,11 +434,12 @@ def get_video_meta_data(video_path: Union[str, os.PathLike, cv2.VideoCapture], f if isinstance(video_path, str): check_file_exist_and_readable(file_path=video_path) cap = cv2.VideoCapture(video_path) + _, video_data["video_name"], _ = get_fn_ext(video_path) elif isinstance(video_path, cv2.VideoCapture): cap = video_path + video_data["video_name"] = '' else: raise InvalidInputError(msg=f'video_path is neither a file path or a cv2.VideoCapture: {type(video_path)}', source=get_video_meta_data.__name__) - _, video_data["video_name"], _ = get_fn_ext(video_path) video_data["fps"] = cap.get(cv2.CAP_PROP_FPS) if fps_as_int: video_data["fps"] = int(video_data["fps"]) diff --git a/simba/video_processors/video_processing.py b/simba/video_processors/video_processing.py index aa55a5689..92f2c487a 100644 --- a/simba/video_processors/video_processing.py +++ b/simba/video_processors/video_processing.py @@ -18,6 +18,8 @@ import numpy as np from PIL import Image, ImageTk from shapely.geometry import Polygon +from skimage.segmentation import slic +from skimage.color import label2rgb try: from typing import Literal @@ -35,7 +37,7 @@ check_nvidea_gpu_available, check_str, check_that_hhmmss_start_is_before_end, check_valid_boolean, check_valid_lst, - check_valid_tuple) + check_valid_tuple, check_if_valid_img) from simba.utils.data import find_frame_numbers_from_time_stamp from simba.utils.enums import OS, ConfigKey, Defaults, Formats, Options, Paths from simba.utils.errors import (CountError, DirectoryExistError, @@ -63,6 +65,7 @@ from simba.video_processors.roi_selector_circle import ROISelectorCircle from simba.video_processors.roi_selector_polygon import ROISelectorPolygon + MAX_FRM_SIZE = 1080, 650 def change_img_format(directory: Union[str, os.PathLike], @@ -114,10 +117,15 @@ def convert_to_jpeg(path: Union[str, os.PathLike, List[Union[str, os.PathLike]]] :width: 200 :align: center - :parameter Union[str, os.PathLike] directory: Path to directory holding image files, a single image file, or a list of paths to image files. - :parameter Optional[int] quality: The quality of the output images (0-100). + .. seealso:: + For BMP images, see :func:`~simba.video_processors.video_processing.convert_to_bmp`. For PNG, see :func:`~simba.video_processors.video_processing.convert_to_png`. + For TIFF, see :func:`~simba.video_processors.video_processing.convert_to_tiff`. For WEBP, see :func:`~simba.video_processors.video_processing.convert_to_webp`. + + :param Union[str, os.PathLike] directory: Path to directory holding image files, a single image file, or a list of paths to image files. + :param Optional[int] quality: The quality of the output images (0-100). :param Optional[bool] save_dir: If not None, then the directory where to store converted images. If None, then stores the images in a subdirectory within the first passed image directory. - :parameter Optional[bool] verbose: If True, prints progress. Default False. + :param Optional[bool] verbose: If True, prints progress. Default False. + :returns: None. New images are saved in ``save_dir``. :example: >>> convert_to_jpeg(path='/Users/simon/Desktop/imgs', verbose=False, quality=15) @@ -164,10 +172,14 @@ def convert_to_bmp(path: Union[str, os.PathLike, List[Union[str, os.PathLike]]], """ Convert images in a directory to BMP format. + .. seealso:: + For JEPG, see :func:`~simba.video_processors.video_processing.convert_to_jpeg`. For PNG, see :func:`~simba.video_processors.video_processing.convert_to_png`. + For TIFF, see :func:`~simba.video_processors.video_processing.convert_to_tiff`. For WEBP, see :func:`~simba.video_processors.video_processing.convert_to_webp`. + :param Union[str, os.PathLike] path: path to directory containing images, or path to a single image file, or a list of paths to image files. :param Optional[bool] save_dir: If not None, then the directory where to store converted images. If None, then stores the images in a subdirectory within the first passed image directory. :param Optional[bool] verbose: If True, print conversion progress. Default is False. - :return: None. + :returns: None. New images are saved in ``save_dir``. :example: >>> convert_to_bmp(path='/Users/simon/Desktop/test_jpg_/landing_40.jpeg', save_dir='/Users/simon/Desktop/test_jpg_/test__') @@ -214,10 +226,14 @@ def convert_to_png(path: Union[str, os.PathLike], """ Convert images to PNG format. + .. seealso:: + For BMP images, see :func:`~simba.video_processors.video_processing.convert_to_bmp`. For JEPG, see :func:`~simba.video_processors.video_processing.convert_to_jpeg`. + For TIFF, see :func:`~simba.video_processors.video_processing.convert_to_tiff`. For WEBP, see :func:`~simba.video_processors.video_processing.convert_to_webp`. + :param Union[str, os.PathLike] path: path to directory containing images, or path to a single image file, or a list of paths to image files. :param Optional[bool] save_dir: If not None, then the directory where to store converted images. If None, then stores the images in a subdirectory within the first passed image directory. :param Optional[bool] verbose: If True, print conversion progress. Default is False. - :return: None. + :returns: None. New images are saved in ``save_dir``. :example: >>> convert_to_png(path=['/Users/simon/Desktop/test_jpg_/test__/landing_40.bmp', '/Users/simon/Desktop/test_jpg_/landing_30.jpeg'], save_dir='/Users/simon/Desktop/test_jpg_/test__/HELLO_3') @@ -264,11 +280,15 @@ def convert_to_tiff(directory: Union[str, os.PathLike], """ Convert images in a directory to TIFF format. + .. seealso:: + For BMP images, see :func:`~simba.video_processors.video_processing.convert_to_bmp`. For JEPG, see :func:`~simba.video_processors.video_processing.convert_to_jpeg`. + For PNG, see :func:`~simba.video_processors.video_processing.convert_to_png`. For WEBP, see :func:`~simba.video_processors.video_processing.convert_to_webp`. + :param Union[str, os.PathLike] directory: The directory containing the images. :param Optional[bool] stack: If True, create a TIFF stack from the images. Default is False. :param Literal['raw', 'tiff_deflate', 'tiff_lzw'] compression: Compression method for the TIFF file. Options are 'raw' (no compression), 'tiff_deflate', and 'tiff_lzw'. Default is 'raw'. :param Optional[bool] verbose: If True, print conversion progress. Default is False. - :return: None. + :returns: None. New images are saved in ``save_dir``. :example: >>> convert_to_tiff('/Users/simon/Desktop/imgs', stack=True, compression='tiff_lzw') @@ -318,7 +338,7 @@ def convert_to_tiff(directory: Union[str, os.PathLike], def convert_to_webp(path: Union[str, os.PathLike], quality: Optional[int] = 95, save_dir: Optional[Union[str, os.PathLike]] = None, - verbose: Optional[bool] = True): + verbose: Optional[bool] = True) -> None: """ Convert the file type of all image files within a directory to WEBP format of passed quality. @@ -327,10 +347,15 @@ def convert_to_webp(path: Union[str, os.PathLike], :width: 300 :align: center - :parameter Union[str, os.PathLike] directory: Path to directory holding image files - :parameter Optional[int] quality: The quality of the output images (0-100). + .. seealso:: + For BMP images, see :func:`~simba.video_processors.video_processing.convert_to_bmp`. For JEPG, see :func:`~simba.video_processors.video_processing.convert_to_jpeg`. + For PNG, see :func:`~simba.video_processors.video_processing.convert_to_png`. For TIFF, see :func:`~simba.video_processors.video_processing.convert_to_tiff`. + + :param Union[str, os.PathLike] path: Path to directory holding image files + :param Optional[int] quality: The quality of the output images (0-100). :param Optional[bool] save_dir: If not None, then the directory where to store converted images. If None, then stores the images in a subdirectory within the first passed image directory. - :parameter Optional[bool] verbose: If True, prints progress. Default False. + :param Optional[bool] verbose: If True, prints progress. Default False. + :returns: None. New images are saved in ``save_dir``. :example: >>> convert_to_webp('/Users/simon/Desktop/imgs', quality=80) @@ -372,16 +397,17 @@ def clahe_enhance_video(file_path: Union[str, os.PathLike], out_path: Optional[Union[str, os.PathLike]] = None) -> None: """ - Convert a single video file to clahe-enhanced greyscale .avi file. The result is saved with prefix - ``CLAHE_`` in the same directory as in the input file if out_path is not passed. Else saved at the out_path. + Convert a single video file to clahe-enhanced greyscale .avi file. .. image:: _static/img/clahe_enhance_video.gif :width: 800 :align: center - :parameter Union[str, os.PathLike] file_path: Path to video file. - :parameter Optional[int] clip_limit: CLAHE amplification limit. Inccreased clip limit reduce noise in output. Default: 2. - :parameter Optional[Tuple[int]] tile_grid_size: The histogram kernel size. + :param Union[str, os.PathLike] file_path: Path to video file. + :param Optional[int] clip_limit: CLAHE amplification limit. Inccreased clip limit reduce noise in output. Default: 2. + :param Optional[Tuple[int]] tile_grid_size: The histogram kernel size. + :param Optional[Union[str, os.PathLike]] out_path: The result is saved with prefix``CLAHE_`` in the same directory as in the input file if out_path is not passed. Else saved at the out_path. + :returns: None. :example: >>> _ = clahe_enhance_video(file_path: 'project_folder/videos/Video_1.mp4') @@ -456,12 +482,14 @@ def extract_frame_range(file_path: Union[str, os.PathLike], save_dir: Optional[Union[str, os.PathLike]] = None, verbose: Optional[bool] = True) -> None: """ - Extract a user-defined range of frames from a video file to `png` format. Images - are saved in a folder with the suffix `_frames` within the same directory as the video file. + Extract a user-defined range of frames from a video file to `png` format. - :parameter Union[str, os.PathLike] file_path: Path to video file - :parameter int start_frame: First frame in range to extract - :parameter int end_frame: Last frame in range to extract. + :param Union[str, os.PathLike] file_path: Path to video file + :param int start_frame: First frame in range to extract + :param int end_frame: Last frame in range to extract. + :param Optional[Union[str, os.PathLike]] save_dir: Optional save directory. Images are saved in a folder with the suffix `_frames` within the same directory as the video file, if None is passed. + :param Optional[bool] verbose: Wether to print progress. Default False. + :returns: None :example: >>> _ = extract_frame_range(file_path='project_folder/videos/Video_1.mp4', start_frame=100, end_frame=500) @@ -510,6 +538,7 @@ def change_single_video_fps(file_path: Union[str, os.PathLike], :param Union[str, os.PathLike] file_path: Path to video file :param int fps: FPS of the new video file. :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None :example: >>> _ = change_single_video_fps(file_path='project_folder/videos/Video_1.mp4', fps=15) @@ -552,10 +581,11 @@ def change_fps_of_multiple_videos(path: Union[str, os.PathLike, List[Union[str, Change the fps of all video files in a folder. Results are stored in the same directory as in the input files with the suffix ``_fps_new_fps``. - :parameter Union[str, os.PathLike] path: Path to video file directory, or a list of video file paths. - :parameter int fps: Fps of the new video files. + :param Union[str, os.PathLike] path: Path to video file directory, or a list of video file paths. + :param int fps: Fps of the new video files. :param Optional[bool] save_dir: If not None, then the directory where to store converted videos. If None, then stores the new videos in the same directory as the input video with the ``_new_fps.file_extension`` suffix. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. :example: >>> _ = change_fps_of_multiple_videos(path='project_folder/videos/Video_1.mp4', fps=15) @@ -604,11 +634,11 @@ def change_fps_of_multiple_videos(path: Union[str, os.PathLike, List[Union[str, def convert_video_powerpoint_compatible_format(file_path: Union[str, os.PathLike], gpu: Optional[bool] = False) -> None: """ - Create MS PowerPoint compatible copy of a video file. The result is stored in the same directory as the - input file with the ``_powerpointready`` suffix. + Create MS PowerPoint compatible copy of a video file. - :parameter Union[str, os.PathLike] file_path: Path to video file. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Union[str, os.PathLike] file_path: Path to video file. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. The result is stored in the same directory as the input file with the ``_powerpointready`` suffix. :example: >>> _ = convert_video_powerpoint_compatible_format(file_path='project_folder/videos/Video_1.mp4') @@ -648,19 +678,21 @@ def convert_video_powerpoint_compatible_format(file_path: Union[str, os.PathLike def video_to_greyscale(file_path: Union[str, os.PathLike], gpu: Optional[bool] = False) -> None: """ - Convert a video file to greyscale mp4 format. The result is stored in the same directory as the - input file with the ``_grayscale.mp4`` suffix. + Convert a video file to greyscale mp4 format. .. image:: _static/img/to_greyscale.gif :width: 700 :align: center .. seealso:: - :func:`simba.data_processors.cuda.image.img_stack_to_grayscale_cupy`, :func:`simba.data_processors.cuda.image.img_stack_to_grayscale_cuda`, - :func:`simba.mixins.image_mixin.ImageMixin.img_stack_to_greyscale`, :func:`simba.video_processors.video_processing.batch_video_to_greyscale` + For GPU CuPY acceleration, see :func:`~simba.data_processors.cuda.image.img_stack_to_grayscale_cupy` + For GPU numba\ CUDA acceleration, see :func:`~simba.data_processors.cuda.image.img_stack_to_grayscale_cuda`, + For multicore CPU solutions, see :func:`~simba.mixins.image_mixin.ImageMixin.img_stack_to_greyscale` or :func:`~simba.video_processors.video_processing.batch_video_to_greyscale` + For single-core multivideo solution, see :func:`~simba.video_processors.video_processing.batch_video_to_greyscale` - :parameter Union[str, os.PathLike] file_path: Path to video file. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Union[str, os.PathLike] file_path: Path to video file. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: The result is stored in the same directory as the input file with the ``_grayscale.mp4`` suffix. :raise FFMPEGCodecGPUError: If no GPU is found and ``gpu == True``. :raise FileExistError: If video name with ``_grayscale`` suffix already exist. @@ -700,21 +732,23 @@ def batch_video_to_greyscale(path: Union[str, os.PathLike, List[Union[str, os.Pa save_dir: Optional[Union[str, os.PathLike]] = None, gpu: Optional[bool] = False) -> None: """ - Convert a directory of video file to greyscale mp4 format. The results are stored in the same directory as the - input files with the ``_grayscale.mp4`` suffix. + Convert a directory of video file to greyscale mp4 format. .. image:: _static/img/to_greyscale.gif :width: 700 :align: center .. seealso:: - :func:`simba.data_processors.cuda.image.img_stack_to_grayscale_cupy`, :func:`simba.data_processors.cuda.image.img_stack_to_grayscale_cuda`, - :func:`simba.mixins.image_mixin.ImageMixin.img_stack_to_greyscale`, :func:`simba.video_processors.video_processing.video_to_greyscale` + For GPU CuPY acceleration, see :func:`~simba.data_processors.cuda.image.img_stack_to_grayscale_cupy` + For GPU numba\ CUDA acceleration, see :func:`~simba.data_processors.cuda.image.img_stack_to_grayscale_cuda`, + For multicore CPU solutions, see :func:`~simba.mixins.image_mixin.ImageMixin.img_stack_to_greyscale` or :func:`~simba.video_processors.video_processing.batch_video_to_greyscale` + For single-core single video solution, see :func:`~simba.video_processors.video_processing.video_to_greyscale` :param Union[str, os.PathLike] path: Path to directory holding video files in color, or a list of file paths to videos in color. :param Optional[bool] save_dir: If not None, then the directory where to store converted videos. If None, then stores the new videos in the same directory as the input video with the ``_grayscale`` suffix. :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. :raise FFMPEGCodecGPUError: If no GPU is found and ``gpu == True``. + :returns: None. :example: >>> _ = batch_video_to_greyscale(path='/Users/simon/Desktop/envs/simba/troubleshooting/mouse_open_field/project_folder/videos/test_2') @@ -765,8 +799,7 @@ def superimpose_frame_count(file_path: Union[str, os.PathLike], fontsize: Optional[int] = 20) -> None: """ - Superimpose frame count on a video file. The result is stored in the same directory as the - input file with the ``_frame_no.mp4`` suffix if ``save_path`` is None. + Superimpose frame count on a video file. .. image:: _static/img/superimpose_frame_count.png :width: 700 @@ -784,6 +817,7 @@ def superimpose_frame_count(file_path: Union[str, os.PathLike], :parameter Optional[str] loc: The location of the font number text. Options: 'top_left', 'top_middle', 'top_right', 'bottom_left', 'bottom_middle', 'bottom_right'. Default: Bottom middle. :parameter Optional[str] bg_color: The color of the box which the frame number is printed in. Default: 'White'. :parameter Optional[str] font: The font to use for the frame number. Default Arial. + :returns: None. The result is stored in the same directory as the input file with the ``_frame_no.mp4`` suffix if ``save_path`` is None. :example: >>> _ = superimpose_frame_count(file_path='/Users/simon/Downloads/1_LH_0_3.mp4', fontsize=90) @@ -841,13 +875,13 @@ def remove_beginning_of_video(file_path: Union[str, os.PathLike], gpu: Optional[bool] = False) -> None: """ - Remove N seconds from the beginning of a video file. If save_path is not passed, - the result is stored in the same directory as the input file with the ``_shorten.mp4`` suffix. + Remove N seconds from the beginning of a video file. :param Union[str, os.PathLike] file_path: Path to video file :param int time: Number of seconds to remove from the beginning of the video. :param Optional[Union[str, os.PathLike]] save_path: Optional save location for the shortened video. If None, then the new video is saved in the same directory as the input video with the ``_shortened`` suffix. :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. If save_path is not passed, the result is stored in the same directory as the input file with the ``_shorten.mp4`` suffix. :example: >>> _ = remove_beginning_of_video(file_path='project_folder/videos/Video_1.avi', time=10) @@ -887,16 +921,16 @@ def clip_video_in_range(file_path: Union[str, os.PathLike], include_clip_time_in_filename: Optional[bool] = False, gpu: Optional[bool] = False) -> None: """ - Clip video within a specific range. The result is stored in the same directory as the - input file with the ``_clipped.mp4`` suffix. + Clip video within a specific range. - :parameter Union[str, os.PathLike] file_path: Path to video file - :parameter str start_time: Start time in HH:MM:SS format. - :parameter str end_time: End time in HH:MM:SS format. - :parameter Optional[Union[str, os.PathLike]] out_dir: If None, then the clip will be stored in the same dir as the input video. If directory, then the location of the output files. - :parameter Optional[bool] include_clip_time_in_filename: If True, include the clip start and end in HH-MM-SS format as suffix in the filename. If False, then use integer suffic representing the count. - :parameter Optional[bool] overwrite: If True, the overwrite output file if path already exist. If False, then raise FileExistError. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Union[str, os.PathLike] file_path: Path to video file + :param str start_time: Start time in HH:MM:SS format. + :param str end_time: End time in HH:MM:SS format. + :param Optional[Union[str, os.PathLike]] out_dir: If None, then the clip will be stored in the same dir as the input video. If directory, then the location of the output files. + :param Optional[bool] include_clip_time_in_filename: If True, include the clip start and end in HH-MM-SS format as suffix in the filename. If False, then use integer suffic representing the count. + :param Optional[bool] overwrite: If True, the overwrite output file if path already exist. If False, then raise FileExistError. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: If None, then the clip will be stored in the same dir as the input video with the ``_clipped.mp4`` suffix. If directory, then the location of the output files. :example: >>> _ = clip_video_in_range(file_path='project_folder/videos/Video_1.avi', start_time='00:00:05', end_time='00:00:10') @@ -942,13 +976,13 @@ def downsample_video(file_path: Union[str, os.PathLike], video_width: int, gpu: Optional[bool] = False) -> None: """ - Down-sample a video file. The result is stored in the same directory as the - input file with the ``_downsampled.mp4`` suffix. + Down-sample a video file. - :parameter Union[str, os.PathLike] file_path: Path to video file. - :parameter int video_height: height of new video. - :parameter int video_width: width of new video. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Union[str, os.PathLike] file_path: Path to video file. + :param int video_height: height of new video. + :param int video_width: width of new video. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. The result is stored in the same directory as the input file with the ``_downsampled.mp4`` suffix. :example: >>> _ = downsample_video(file_path='project_folder/videos/Video_1.avi', video_height=600, video_width=400) @@ -1053,20 +1087,18 @@ def gif_creator(file_path: Union[str, os.PathLike], stdout_success(msg=f"SIMBA COMPLETE: Video converted! {save_name} generated!", elapsed_time=timer.elapsed_time_str, source=gif_creator.__name__) -def batch_convert_video_format( - directory: Union[str, os.PathLike], - input_format: str, - output_format: str, - gpu: Optional[bool] = False, -) -> None: +def batch_convert_video_format(directory: Union[str, os.PathLike], + input_format: str, + output_format: str, + gpu: Optional[bool] = False) -> None: """ - Batch convert all videos in a folder of specific format into a different video format. The results are - stored in the same directory as the input files. + Batch convert all videos in a folder of specific format into a different video format. :parameter Union[str, os.PathLike] directory: Path to video file directory. :parameter str input_format: Format of the input files (e.g., avi). :parameter str output_format: Format of the output files (e.g., mp4). :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. The results are stored in the same directory as the input files. :example: >>> _ = batch_convert_video_format(directory='project_folder/videos', input_format='avi', output_format='mp4') @@ -1137,10 +1169,10 @@ def batch_convert_video_format( def batch_create_frames(directory: Union[str, os.PathLike]) -> None: """ - Extract all frames for all videos in a directory. Results are stored within sub-directories in the input - directory named according to the video files. + Extract all frames for all videos in a directory. :parameter str directory: Path to directory containing video files. + :returns: None. Results are stored within sub-directories in the input directory named according to the video files. :example: >>> _ = batch_create_frames(directory='project_folder/videos') @@ -1190,9 +1222,10 @@ def extract_frames_single_video(file_path: Union[str, os.PathLike], .. note:: Image frames are saved as PNG files named with integers in order of appearance, i.e., ``0.png, 1.png ...`` - :parameter Union[str, os.PathLike] file_path: Path to video file. - :parameter Optional[Union[str, os.PathLike]] save_dir: Optional directory where to save the frames. If ``save_dir`` is not passed, - results are stored within a subdirectory in the same directory as the input file. + :param Union[str, os.PathLike] file_path: Path to video file. + :param Optional[Union[str, os.PathLike]] save_dir: Optional directory where to save the frames. If ``save_dir`` is not passed, results are stored within a subdirectory in the same directory as the input file. + :returns: None + :example: >>> _ = extract_frames_single_video(file_path='project_folder/videos/Video_1.mp4') @@ -1217,23 +1250,22 @@ def extract_frames_single_video(file_path: Union[str, os.PathLike], #_ = extract_frames_single_video(file_path='/Users/simon/Desktop/video_test/Screen Recording 2024-05-06 at 1.23.31 PM_clipped.mp4') -def multi_split_video( - file_path: Union[str, os.PathLike], - start_times: List[str], - end_times: List[str], - out_dir: Optional[Union[str, os.PathLike]] = None, - include_clip_time_in_filename: Optional[bool] = False, - gpu: Optional[bool] = False, -) -> None: +def multi_split_video(file_path: Union[str, os.PathLike], + start_times: List[str], + end_times: List[str], + out_dir: Optional[Union[str, os.PathLike]] = None, + include_clip_time_in_filename: Optional[bool] = False, + gpu: Optional[bool] = False) -> None: """ Divide a video file into multiple video files from specified start and stop times. - :parameter str file_path: Path to input video file. - :parameter List[str] start_times: Start times in HH:MM:SS format. - :parameter List[str] end_times: End times in HH:MM:SS format. - :parameter Optional[Union[str, os.PathLike]] out_dir: If None, then the clips will be stored in the same dir as the input video. If directory, then the location of the output files. - :parameter Optional[bool] include_clip_time_in_filename: If True, include the clip start and end in HH-MM-SS format as suffix in the filename. If False, then use integer suffic representing the count. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param str file_path: Path to input video file. + :param List[str] start_times: Start times in HH:MM:SS format. + :param List[str] end_times: End times in HH:MM:SS format. + :param Optional[Union[str, os.PathLike]] out_dir: If None, then the clips will be stored in the same dir as the input video. If directory, then the location of the output files. + :param Optional[bool] include_clip_time_in_filename: If True, include the clip start and end in HH-MM-SS format as suffix in the filename. If False, then use integer suffic representing the count. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. :example: >>> _ = multi_split_video(file_path='project_folder/videos/Video_1.mp4', start_times=['00:00:05', '00:00:20'], end_times=['00:00:10', '00:00:25']) @@ -1306,18 +1338,20 @@ def multi_split_video( def crop_single_video(file_path: Union[str, os.PathLike], gpu: Optional[bool] = False) -> None: """ - Crop a single video using ``simba.video_processors.roi_selector.ROISelector`` interface. Results is saved in the same directory as input video with the - ``_cropped.mp4`` suffix`. + Crop a single video using :func:`~simba.video_processors.roi_selector.ROISelector` interface. + + Results is saved in the same directory as input video with the ``_cropped.mp4`` suffix`. .. image:: _static/img/crop_single_video.gif :width: 700 :align: center .. seealso:: - :func:`simba.video_processors.video_processing.crop_multiple_videos` + To crop multiple videos, see :func:`~simba.video_processors.video_processing.crop_multiple_videos` - :parameter str file_path: Path to video file. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param str file_path: Path to video file. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. Results is saved in the same directory as input video with the ``_cropped.mp4`` suffix`. :example: >>> _ = crop_single_video(file_path='project_folder/videos/Video_1.mp4') @@ -1369,23 +1403,20 @@ def crop_single_video(file_path: Union[str, os.PathLike], gpu: Optional[bool] = # crop_single_video(file_path=r'C:\Users\Nape_Computer_2\Desktop\test_videos\Box1_PM2_day_5_20211104T171021.mp4', gpu=False) -def crop_multiple_videos( - directory_path: Union[str, os.PathLike], - output_path: Union[str, os.PathLike], - gpu: Optional[bool] = False, -) -> None: +def crop_multiple_videos(directory_path: Union[str, os.PathLike], output_path: Union[str, os.PathLike], gpu: Optional[bool] = False) -> None: """ Crop multiple videos in a folder according to crop-coordinates defined in the **first** video. .. seealso:: - :func:`simba.video_processors.video_processing.crop_single_video` - - - :parameter str directory_path: Directory containing input videos. - :parameter str output_path: Directory where to save the cropped videos. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + To crop one video, see :func:`simba.video_processors.video_processing.crop_single_video` + .. note:: + Calls the :func:`~simba.video_processors.roi_selector.ROISelector` interface. + :param Union[str, os.PathLike] directory_path: Directory containing input videos. + :param Union[str, os.PathLike] output_path: Directory where to save the cropped videos. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. Results are stores in passed ``output_path``. :example: >>> _ = crop_multiple_videos(directory_path='project_folder/videos', output_path='project_folder/videos/my_new_folder') @@ -1486,11 +1517,12 @@ def frames_to_movie(directory: Union[str, os.PathLike], .. note:: The Image files have to have ordered numerical names e.g., ``1.png``, ``2.png`` etc... - :parameter str directory: Directory containing the images. - :parameter int fps: The frame rate of the output video. - :parameter int quality: Integer representing quatlity of the output video: 10, 20, 30.. 100. Higher values gives larger videos at higher quality. Higher values may negatively affect runtime. - :parameter Optional[Literal['mp4', 'avi', 'webm', 'mov']] out_format: The format of the output video: 'mp4', 'avi', 'webm', or 'mov'. Default: mp4. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param str directory: Directory containing the images. + :param int fps: The frame rate of the output video. + :param int quality: Integer representing quatlity of the output video: 10, 20, 30.. 100. Higher values gives larger videos at higher quality. Higher values may negatively affect runtime. + :param Optional[Literal['mp4', 'avi', 'webm', 'mov']] out_format: The format of the output video: 'mp4', 'avi', 'webm', or 'mov'. Default: mp4. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :results: None. The video is stored in the same directory as the frames, named after the directory. :example: >>> frames_to_movie(directory='/Users/simon/Desktop/blah', fps=60, quality=60, out_format='mp4') @@ -1549,7 +1581,8 @@ def video_concatenator(video_one_path: Union[str, os.PathLike], :param str video_two_path: Path to the second video in the concatenated video :param int or str resolution: If str, then the name of the video which resolution you want to retain. E.g., `Video_1`. Else int, representing the video width (if vertical concat) or height (if horizontal concat). Aspect raio will be retained. :param horizontal: If true, then horizontal concatenation. Else vertical concatenation. - :parameter Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :param Optional[bool] gpu: If True, use NVIDEA GPU codecs. Default False. + :returns: None. The video is stored in the same directory as the ``video_one_path`` using the video names concatenated as filename. :example: >>> video_concatenator(video_one_path='project_folder/videos/Video_1.mp4', video_two_path='project_folder/videos/Video_2.mp4', resolution=800, horizontal=True) @@ -1621,17 +1654,18 @@ def video_concatenator(video_one_path: Union[str, os.PathLike], class VideoRotator(ConfigReader): """ - GUI Tool for rotating video. Rotated video is saved with the ``_rotated_DATETIME.mp4`` suffix. + GUI Tool for rotating video. .. image:: _static/img/VideoRotator.gif :width: 700 :align: center - :parameter str input_path: Path to video to rotate. - :parameter str output_dir: Directory where to save the rotated video. - :parameter Optional[bool] gpu: If True, use FFMPEG NVIDEA GPU codecs. Else CPU codecs. - :parameter Optional[bool] gpu: If True, use FFPMPEG. Else, OpenCV. + :param str input_path: Path to video to rotate. + :param str output_dir: Directory where to save the rotated video. + :param Optional[bool] gpu: If True, use FFMPEG NVIDEA GPU codecs. Else CPU codecs. + :param Optional[bool] gpu: If True, use FFPMPEG. Else, OpenCV. + :returns: None. Rotated video is saved with the ``_rotated_DATETIME.mp4`` suffix. :example: >>> VideoRotator(input_path='project_folder/videos/Video_1.mp4', output_dir='project_folder/videos') @@ -1814,10 +1848,11 @@ def extract_frames_from_all_videos_in_directory(config_path: Union[str, os.PathL directory: Union[str, os.PathLike]) -> None: """ - Extract all frames from all videos in a directory. The results are saved in the project_folder/frames/input directory of the SimBA project + Extract all frames from all videos in a directory. - :parameter str config_path: path to SimBA project config file in Configparser format. - :parameter str directory: path to file or folder containing videos in mp4 and/or avi format. + :param str config_path: path to SimBA project config file in Configparser format. + :param str directory: path to file or folder containing videos in mp4 and/or avi format. + :returns: None. The results are saved in the ``project_folder/frames/input directory`` of the SimBA project :example: >>> extract_frames_from_all_videos_in_directory(config_path='project_folder/project_config.ini', source='/tests/test_data/video_tests') @@ -4431,6 +4466,115 @@ def temporal_concatenation(video_paths: List[Union[str, os.PathLike]], stdout_success(msg=f'{len(video_paths)} videos temporally concatenated and saved at {save_path}', elapsed_time=timer.elapsed_time_str) +def get_img_slic(img: np.ndarray, + n_segments: Optional[int] = 50, + compactness: Optional[int] = 50, + sigma: Optional[float] = 1) -> np.ndarray: + + """ + Simplify an image into superpixels using SLIC (Simple Linear Iterative Clustering). + + :param np.ndarray img: Image to segment. + :param n_segments: Number of segments to produce. + :param compactness: How compact ("square") the output segments are. + :param np.ndarray sigma: Amount of Gaussian smoothing. + :return: Smoothened version of the input image. + :rtype: np.ndarray + + :example: + >>> img = read_frm_of_video(video_path=r"C:\troubleshooting\mitra\project_folder\videos\FRR_gq_Saline_0626.mp4", frame_index=0) + >>> sliced_img = get_img_slic(img=img) + """ + + check_if_valid_img(data=img, source=f'{get_img_slic.__name__} img', raise_error=True) + check_int(name=f'{get_img_slic.__name__} n_segments', value=n_segments, min_value=2) + check_int(name=f'{get_img_slic.__name__} compactness', value=compactness, min_value=1) + check_int(name=f'{get_img_slic.__name__} sigma', value=compactness, min_value=0) + segments = slic(image=img, n_segments=n_segments, compactness=compactness, sigma=sigma, start_label=0) + segmented_image = label2rgb(segments, img, kind='avg', bg_label=0) + + return segmented_image + +def _slic_helper(frm_range: np.ndarray, + n_segments: int, + sigma: float, + compactness: int, + save_dir: Union[str, os.PathLike], + video_path: Union[str, os.PathLike]): + + """ SLIC multiprocess helper called by slic.get_video_slic""" + + video_cap = cv2.VideoCapture(video_path) + video_meta_data = get_video_meta_data(video_path=video_path) + batch, start_frm, end_frm = frm_range[0], frm_range[1][0], frm_range[1][-1] + save_path = os.path.join(save_dir, f'{batch}.mp4') + fourcc = cv2.VideoWriter_fourcc(*Formats.MP4_CODEC.value) + writer = cv2.VideoWriter(save_path, fourcc, video_meta_data["fps"], (video_meta_data["width"], video_meta_data["height"])) + for frm_idx in range(start_frm, end_frm): + print(f'Frame {frm_idx}/{end_frm}, Batch {batch}...') + img = read_frm_of_video(video_path=video_cap, frame_index=frm_idx) + img = get_img_slic(img=img, n_segments=n_segments, compactness=compactness, sigma=sigma) + writer.write(img) + writer.release() + return batch + + +def get_video_slic(video_path: Union[str, os.PathLike], + save_path: Union[str, os.PathLike], + n_segments: Optional[int] = 50, + compactness: Optional[int] = 50, + sigma: Optional[int] = 1, + core_cnt: Optional[int] = -1) -> None: + + """ + Apply SLIC superpixel segmentation to all frames of a video and save the output as a new video. + + .. video:: _static/img/get_video_slic.webm + :width: 800 + :autoplay: + :loop: + + .. seealso:: + To convert single image, see :func:`simba.video_processors.video_processing.get_img_slic` + + :param Union[str, os.PathLike] video_path: Path to the input video file. + :param Union[str, os.PathLike] save_path: Path to save the processed video with SLIC superpixel segmentation. + :param Optional[int] n_segments: Approximate number of superpixels for each frame. Defaults to 50. + :param Optional[int] compactness: Balance of color and spatial proximity. Higher values result in more uniformly shaped superpixels. Defaults to 50. + :param Optional[int] sigma: Standard deviation for Gaussian smoothing applied to each frame before segmentation. Defaults to 1. + :param Optional[int] core_cnt: Number of CPU cores to use for parallel processing. Set to -1 to use all available cores. Defaults to -1. + :return: None. The segmented video is saved to `save_path`. + + :example: + >>> #video_path = r"C:\troubleshooting\mitra\project_folder\videos\FRR_gq_Saline_0626.mp4" + """ + timer = SimbaTimer(start=True) + check_int(name=f'{get_img_slic.__name__} n_segments', value=n_segments, min_value=2) + check_int(name=f'{get_img_slic.__name__} compactness', value=compactness, min_value=1) + check_int(name=f'{get_img_slic.__name__} sigma', value=sigma, min_value=1) + check_int(name=f'{get_img_slic.__name__} core_cnt', value=core_cnt, min_value=-1, unaccepted_vals=[0]) + check_file_exist_and_readable(file_path=video_path) + video_meta_data = get_video_meta_data(video_path=video_path) + if core_cnt == -1 or core_cnt > find_core_cnt()[0]: core_cnt = find_core_cnt()[0] + frm_ranges = np.array_split(np.arange(0, video_meta_data['frame_count'] + 1), core_cnt) + frm_ranges = [(y, x) for y, x in enumerate(frm_ranges)] + out_dir, out_name, _= get_fn_ext(filepath=save_path) + temp_folder = os.path.join(out_dir, "temp") + if not os.path.isdir(temp_folder): os.makedirs(temp_folder) + with multiprocessing.Pool(core_cnt, maxtasksperchild=Defaults.MAX_TASK_PER_CHILD.value) as pool: + constants = functools.partial(_slic_helper, + video_path=video_path, + save_dir=temp_folder, + n_segments=n_segments, + compactness=compactness, + sigma=sigma) + for cnt, core_batch in enumerate(pool.map(constants, frm_ranges, chunksize=1)): + print(f'Core batch {core_batch} complete...') + pool.join() + pool.terminate() + timer.stop_timer() + concatenate_videos_in_folder(in_folder=temp_folder, save_path=save_path) + stdout_success(msg=f'SLIC video saved at {save_path}', elapsed_time=timer.elapsed_time_str) # video_paths = ['/Users/simon/Desktop/envs/simba/troubleshooting/beepboop174/project_folder/merge/Trial 10_clipped_gantt.mp4', # '/Users/simon/Desktop/envs/simba/troubleshooting/beepboop174/project_folder/merge/Trial 10_clipped.mp4',