def _iterate(self, stims, validation='strict', *args, **kwargs): batches = batch_iterable(stims, self._batch_size) results = [] for batch in progress_bar_wrapper(batches): res = self._transform(batch, *args, **kwargs) for i, stim in enumerate(batch): res[i] = _log_transformation(stim, res[i], self) self._propagate_context(stim, res[i]) results.extend(res) return results
def transform(self, stims, validation='strict', *args, **kwargs): if isinstance(stims, string_types): stims = load_stims(stims) # If stims is a CompoundStim and the Transformer is expecting a single # input type, extract all matching stims if isinstance(stims, CompoundStim) and not isinstance( self._input_type, tuple): stims = stims.get_stim(self._input_type, return_all=True) if not stims: raise ValueError("No stims of class %s found in the provided" "CompoundStim instance." % self._input_type) # If stims is an iterable, naively loop over elements, removing # invalid results if needed if isiterable(stims): iters = self._iterate(stims, *args, **kwargs) if config.drop_bad_extractor_results: iters = (i for i in iters if i is not None) return progress_bar_wrapper(iters, desc='Stim') # Validate stim, and then either pass it directly to the Transformer # or, if a conversion occurred, recurse. else: try: validated_stim = self._validate(stims) except TypeError as err: if validation == 'strict': raise err elif validation == 'warn': logging.warn(str(err)) return elif validation == 'loose': return # If a conversion occurred during validation, we recurse if stims is not validated_stim: return self.transform(validated_stim, *args, **kwargs) else: result = self._transform(validated_stim, *args, **kwargs) result = _log_transformation(validated_stim, result, self) if isgenerator(result): result = list(result) return result
def _convert(self, video): if not hasattr(video, "frame_index"): frame_index = range(video.n_frames) else: frame_index = video.frame_index if self.every is not None: new_idx = range(video.n_frames)[::self.every] elif self.hertz is not None: interval = int(video.fps / self.hertz) new_idx = range(video.n_frames)[::interval] elif self.top_n is not None: import cv2 diffs = [] for i, img in enumerate(video.frames): if i == 0: last = img continue diffs.append(sum(cv2.sumElems(cv2.absdiff(last, img)))) last = img new_idx = sorted(range(len(diffs)), key=lambda i: diffs[i], reverse=True)[ :self.top_n] frame_index = sorted(list(set(frame_index).intersection(new_idx))) # Construct new VideoFrameStim for each frame index onsets = [frame_num * (1. / video.fps) for frame_num in frame_index] frames = [] for i, f in progress_bar_wrapper(enumerate(frame_index), desc='Video frame', total=len(frame_index)): if f != frame_index[-1]: dur = onsets[i+1] - onsets[i] else: dur = (video.n_frames / video.fps) - onsets[i] elem = VideoFrameStim(video=video, frame_num=f, duration=dur) frames.append(elem) return DerivedVideoStim(filename=video.filename, frames=frames, frame_index=frame_index)
def _iterate(self, stims, validation='strict', *args, **kwargs): batches = batch_iterable(stims, self._batch_size) results = [] for batch in progress_bar_wrapper(batches): use_cache = config.get_option('cache_transformers') target_inds = {} non_cached = [] for stim in batch: key = hash((hash(self), hash(stim))) # If using the cache, only transform stims that aren't in the # cache and haven't already appeared in the batch if not (use_cache and (key in _cache or key in target_inds)): target_inds[key] = len(non_cached) non_cached.append(stim) # _transform will likely fail if given an empty list if len(non_cached) > 0: batch_results = self._transform(non_cached, *args, **kwargs) else: batch_results = [] for i, stim in enumerate(batch): key = hash((hash(self), hash(stim))) # Use the target index to get the result from batch_results if key in target_inds: result = batch_results[target_inds[key]] result = _log_transformation(stim, result, self) self._propagate_context(stim, result) if use_cache: if isgenerator(result): result = list(result) _cache[key] = result results.append(result) # Otherwise, the result should be in the cache else: results.append(_cache[key]) return results
def transform(self, stims, validation='strict', *args, **kwargs): ''' Executes the transformation on the passed stim(s). Args: stims (str, Stim, list): One or more stimuli to process. Must be one of: - A string giving the path to a file that can be read in as a Stim (e.g., a .txt file, .jpg image, etc.) - A Stim instance of any type. - An iterable of stims, where each element is either a string or a Stim. validation (str): String specifying how validation errors should be handled. Must be one of: - 'strict': Raise an exception on any validation error - 'warn': Issue a warning for all validation errors - 'loose': Silently ignore all validation errors args: Optional positional arguments to pass onto the internal _transform call. kwargs: Optional positional arguments to pass onto the internal _transform call. ''' if isinstance(stims, str): stims = load_stims(stims) # If stims is a CompoundStim and the Transformer is expecting a single # input type, extract all matching stims if isinstance(stims, CompoundStim) and not isinstance( self._input_type, tuple): stims = stims.get_stim(self._input_type, return_all=True) if not stims: raise ValueError("No stims of class %s found in the provided" "CompoundStim instance." % self._input_type) # If stims is an iterable, naively loop over elements, removing # invalid results if needed if isiterable(stims): iters = self._iterate(stims, validation=validation, *args, **kwargs) if config.get_option('drop_bad_extractor_results'): iters = (i for i in iters if i is not None) iters = progress_bar_wrapper(iters, desc='Stim') return set_iterable_type(iters) # Validate stim, and then either pass it directly to the Transformer # or, if a conversion occurred, recurse. else: try: validated_stim = self._validate(stims) except TypeError as err: if validation == 'strict': raise err elif validation == 'warn': logging.warning(str(err)) return elif validation == 'loose': return # If a conversion occurred during validation, we recurse if stims is not validated_stim: return self.transform(validated_stim, *args, **kwargs) else: result = self._transform(validated_stim, *args, **kwargs) result = _log_transformation(validated_stim, result, self) if isgenerator(result): result = list(result) self._propagate_context(validated_stim, result) return result