martinzink commented on code in PR #2258: URL: https://github.com/apache/nifi-minifi-cpp/pull/2258#discussion_r4104057244
########## minifi_rust/extensions/minifi_tensor/src/low_level_processors/filter_bounding_boxes.rs: ########## @@ -0,0 +1,593 @@ +// Licensed to the Apache Software Foundation (ASF) under one +// or more contributor license agreements. See the NOTICE file +// distributed with this work for additional information +// regarding copyright ownership. The ASF licenses this file +// to you under the Apache License, Version 2.0 (the +// "License"); you may not use this file except in compliance +// with the License. You may obtain a copy of the License at +// +// https://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, +// software distributed under the License is distributed on an +// "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +// KIND, either express or implied. See the License for the +// specific language governing permissions and limitations +// under the License. + +mod filter_bounding_boxes_def; + +use crate::low_level_processors::image_to_tensor::ResizeMode; +use crate::utils::bounding_box::BoundingBox; +use crate::utils::dimensions::Dimensions; +use crate::utils::score_activation::{ScoreActivation, SoftmaxTerms}; +use crate::utils::tensor_helpers::{deserialize_tensors, tensor_as_f32}; +use filter_bounding_boxes_def::SUCCESS; +pub(crate) use filter_bounding_boxes_def::{ + BACKGROUND_CLASS_INDEX, BOX_FORMAT, BOX_OUTPUT_INDEX, CLASS_OUTPUT_INDEX, CONFIDENCE_THRESHOLD, + IOU_THRESHOLD, OUTPUT_ATTRIBUTE_NAME, SCORE_ACTIVATION, SCORE_OUTPUT_INDEX, +}; +use minifi_native::macros::{ComponentIdentifier, PropertyType}; +use minifi_native::{ + Content, FlowFileTransform, GetAttribute, GetId, GetProperty, InputStream, Logger, MinifiError, + ProcessError, RouteErrorExt, Schedule, TransformedFlowFile, debug, trace, +}; +use strum_macros::{Display, EnumString, IntoStaticStr, VariantNames}; +use tract::Tensor; + +#[derive( + Debug, Clone, Copy, PartialEq, Display, EnumString, VariantNames, IntoStaticStr, PropertyType, +)] +#[strum(serialize_all = "PascalCase", const_into_str)] +pub(crate) enum BoxFormat { + /// `[x_min, y_min, x_max, y_max]` — SSD, MobileNet-SSD, most PyTorch models. + Xyxy, + /// `[y_min, x_min, y_max, x_max]` — TensorFlow Object Detection API. + Yxyx, + /// `[cx, cy, w, h]` — YOLOv3/5/8 raw output (center + size). + Cxcywh, +} + +/// Convert the four floats at `box_floats[offset..offset+4]` into a canonical +/// `(x_min, y_min, x_max, y_max)` tuple, regardless of the source layout. +fn decode_box(box_floats: &[f32], offset: usize, format: BoxFormat) -> (f32, f32, f32, f32) { + let a = box_floats[offset]; + let b = box_floats[offset + 1]; + let c = box_floats[offset + 2]; + let d = box_floats[offset + 3]; + match format { + BoxFormat::Xyxy => (a, b, c, d), + BoxFormat::Yxyx => (b, a, d, c), + BoxFormat::Cxcywh => { + let (cx, cy, w, h) = (a, b, c, d); + (cx - w / 2.0, cy - h / 2.0, cx + w / 2.0, cy + h / 2.0) + } + } +} + +struct ScoredClass { + class_id: usize, + confidence: f32, +} + +fn score_box( + logits: &[f32], + activation: ScoreActivation, + background_class_index: Option<usize>, +) -> ScoredClass { + let num_classes = logits.len(); + + let best_valid = logits + .iter() + .enumerate() + .filter(|&(_, &logit)| logit.is_finite()) + .filter(|&(id, _)| match background_class_index { + Some(bg_idx) => !(num_classes > 1 && id == bg_idx), + None => true, + }) + .max_by(|a, b| a.1.total_cmp(b.1)); + + let (class_id, &best_logit) = match best_valid { + Some(val) => val, + None => { + return ScoredClass { + class_id: 0, + confidence: f32::NEG_INFINITY, + }; + } + }; + + let confidence = activation.confidence(best_logit, SoftmaxTerms::over(logits.iter().copied())); + + ScoredClass { + class_id, + confidence, + } +} + +#[derive(ComponentIdentifier)] +pub(crate) struct FilterBoundingBoxes { + confidence_threshold: f32, + iou_threshold: f32, + score_output_index: usize, + box_output_index: usize, + box_format: BoxFormat, + score_activation: ScoreActivation, + background_class_index: Option<usize>, + class_output_index: Option<usize>, +} + +impl Schedule for FilterBoundingBoxes { + fn schedule<Ctx: GetProperty, L: Logger>( + context: &Ctx, + _logger: &L, + ) -> Result<Self, MinifiError> { + let confidence_threshold = context.get_property(&CONFIDENCE_THRESHOLD)?; + let iou_threshold = context.get_property(&IOU_THRESHOLD)?; + let score_output_index = context.get_property(&SCORE_OUTPUT_INDEX)?; + let box_output_index = context.get_property(&BOX_OUTPUT_INDEX)?; + let box_format = context.get_property(&BOX_FORMAT)?; + let score_activation = context.get_property(&SCORE_ACTIVATION)?; + let background_class_index = context.get_property(&BACKGROUND_CLASS_INDEX)?; + let class_output_index = context.get_property(&CLASS_OUTPUT_INDEX)?; + + Ok(Self { + confidence_threshold, + iou_threshold, + score_output_index, + box_output_index, + box_format, + score_activation, + background_class_index, + class_output_index, + }) + } +} + +impl FilterBoundingBoxes { + fn result_via_output_attribute<'a, Context: GetProperty>( + &self, + context: &Context, + filtered_boxes: Vec<BoundingBox>, + ) -> Result<TransformedFlowFile<'a>, MinifiError> { + let output_attr = context.get_property(&OUTPUT_ATTRIBUTE_NAME)?; + let content = if output_attr.is_some() { + None + } else { + Some(Content::Buffer( + serde_json::to_vec(&filtered_boxes).map_err(MinifiError::other)?, + )) + }; + + let mut transformed = TransformedFlowFile::new(&SUCCESS, content) + .with_attribute("object.count", filtered_boxes.len().to_string()); + if let Some(attr) = output_attr { + transformed = transformed.with_attribute( + attr, + serde_json::to_string(&filtered_boxes).map_err(MinifiError::other)?, + ) + } else { + transformed = transformed.with_attribute("mime.type", "application/json"); + } + Ok(transformed) + } + + pub(crate) fn filter<'a, Context: GetProperty, LoggerImpl: Logger>( + &self, + context: &Context, + logger: &LoggerImpl, + tensors: Vec<Tensor>, + orig_dim: Dimensions, + target_dim: Dimensions, + resize_mode: ResizeMode, + ) -> Result<TransformedFlowFile<'a>, ProcessError> { + let score_floats = + tensor_as_f32(&tensors, self.score_output_index).route_err_to_failure()?; + let box_floats = tensor_as_f32(&tensors, self.box_output_index).route_err_to_failure()?; + + let (scale_x, scale_y, pad_x, pad_y) = match resize_mode { + ResizeMode::Letterbox => { + let geometry = orig_dim.letterbox_into(target_dim); + ( + geometry.scale, + geometry.scale, + geometry.pad_x as f32, + geometry.pad_y as f32, + ) + } + ResizeMode::Stretch => ( + target_dim.width / orig_dim.width, + target_dim.height / orig_dim.height, + 0.0, + 0.0, + ), + }; + + if !box_floats.len().is_multiple_of(4) { + return Err(MinifiError::custom( + "Box tensor byte length is not a multiple of 16 (4 f32 per box)", + ) + .into()); + } + let num_boxes = box_floats.len() / 4; + if num_boxes == 0 { + debug!(logger, "No boxes to filter; emitting empty array"); + return self + .result_via_output_attribute(context, vec![]) + .route_err_to_failure(); + } + + let make_box = |i: usize, class_id: usize, confidence: f32| -> BoundingBox { + let (raw_x_min, raw_y_min, raw_x_max, raw_y_max) = + decode_box(&box_floats, i * 4, self.box_format); + let true_x_min = (((raw_x_min * target_dim.width) - pad_x) / scale_x) / orig_dim.width; + let true_y_min = + (((raw_y_min * target_dim.height) - pad_y) / scale_y) / orig_dim.height; + let true_x_max = (((raw_x_max * target_dim.width) - pad_x) / scale_x) / orig_dim.width; + let true_y_max = + (((raw_y_max * target_dim.height) - pad_y) / scale_y) / orig_dim.height; + BoundingBox { + class_id, + confidence, + x_min: true_x_min.clamp(0.0, 1.0), + y_min: true_y_min.clamp(0.0, 1.0), + x_max: true_x_max.clamp(0.0, 1.0), + y_max: true_y_max.clamp(0.0, 1.0), + } + }; + + let mut valid_boxes = Vec::new(); + + match self.class_output_index { + // Separate class-id tensor: one score and one class id per box + Some(class_index) => { + let class_floats = tensor_as_f32(&tensors, class_index).route_err_to_failure()?; + if score_floats.len() != num_boxes || class_floats.len() != num_boxes { + return Err(MinifiError::custom(format!( + "'Class output index' mode expects one score and one class id per box \ + (num_boxes={}, scores={}, classes={})", + num_boxes, + score_floats.len(), + class_floats.len() + )) + .into()); + } + trace!( + logger, + "Filtering {} boxes with separate class-id tensor (activation={:?}, \ + box_format={:?})...", + num_boxes, + self.score_activation, + self.box_format + ); + for i in 0..num_boxes { + let confidence = self.score_activation.confidence_of_scalar(score_floats[i]); + if confidence < self.confidence_threshold { + continue; + } + let raw_class = class_floats[i]; + if raw_class < 0.0 { + continue; + } + if !confidence.is_finite() || !raw_class.is_finite() { + continue; + } + let class_id = raw_class.round() as usize; + if self.background_class_index == Some(class_id) { + continue; + } + valid_boxes.push(make_box(i, class_id, confidence)); + } + } + // Per-class score matrix: argmax over classes per box. + None => { + if !score_floats.len().is_multiple_of(num_boxes) { + return Err(MinifiError::custom(format!( + "Scores length ({}) not divisible by number of boxes ({})", + score_floats.len(), + num_boxes + )) + .into()); + } + let num_classes = score_floats.len() / num_boxes; + trace!( + logger, + "Filtering {} boxes across {} potential classes (activation={:?}, \ + box_format={:?})...", + num_boxes, + num_classes, + self.score_activation, + self.box_format + ); + for i in 0..num_boxes { + let logits = &score_floats[i * num_classes..(i + 1) * num_classes]; + let scored = + score_box(logits, self.score_activation, self.background_class_index); + if scored.confidence >= self.confidence_threshold { + valid_boxes.push(make_box(i, scored.class_id, scored.confidence)); + } + } + } + } + + trace!( + logger, + "Found {} boxes exceeding the {} threshold.", + valid_boxes.len(), + self.confidence_threshold + ); + + let filtered_boxes = + BoundingBox::apply_non_maximum_suppression(valid_boxes, self.iou_threshold); + + self.result_via_output_attribute(context, filtered_boxes) + .route_err_to_failure() + } +} + +fn resize_mode_from_attributes<Context: GetAttribute>(context: &Context) -> ResizeMode { + context + .get_attribute("image.resize.mode") + .ok() + .flatten() + .and_then(|raw| raw.parse::<ResizeMode>().ok()) + .unwrap_or(ResizeMode::Letterbox) Review Comment: 👍 it shouldnt matter because its should be present there, but changed it for consistencys sake 8446df5140037b345b2f7275444aeadcfb7d795a -- This is an automated message from the Apache Git Service. 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