diff --git a/machine-learning/app/models/facial_recognition/detection.py b/machine-learning/app/models/facial_recognition/detection.py index 18dd12214c..e790746e5d 100644 --- a/machine-learning/app/models/facial_recognition/detection.py +++ b/machine-learning/app/models/facial_recognition/detection.py @@ -1,44 +1,33 @@ -from pathlib import Path from typing import Any import numpy as np import onnxruntime as ort -from insightface.model_zoo import RetinaFace from numpy.typing import NDArray from app.models.base import InferenceModel -from app.models.session import ort_has_batch_dim, ort_squeeze_outputs -from app.models.transforms import decode_cv2 +from app.models.session import ort_has_batch_dim, ort_expand_outputs +from app.models.transforms import decode_pil from app.schemas import FaceDetectionOutput, ModelSession, ModelTask, ModelType - +from .scrfd import SCRFD +from PIL import Image +from PIL.ImageOps import pad class FaceDetector(InferenceModel): depends = [] identity = (ModelType.DETECTION, ModelTask.FACIAL_RECOGNITION) - def __init__( - self, - model_name: str, - min_score: float = 0.7, - cache_dir: Path | str | None = None, - **model_kwargs: Any, - ) -> None: - self.min_score = model_kwargs.pop("minScore", min_score) - super().__init__(model_name, cache_dir, **model_kwargs) - def _load(self) -> ModelSession: session = self._make_session(self.model_path) - if isinstance(session, ort.InferenceSession) and ort_has_batch_dim(session): - ort_squeeze_outputs(session) - self.model = RetinaFace(session=session) - self.model.prepare(ctx_id=0, det_thresh=self.min_score, input_size=(640, 640)) + if isinstance(session, ort.InferenceSession) and not ort_has_batch_dim(session): + ort_expand_outputs(session) + self.model = SCRFD(session=session) return session - def _predict(self, inputs: NDArray[np.uint8] | bytes, **kwargs: Any) -> FaceDetectionOutput: - inputs = decode_cv2(inputs) + def _predict(self, inputs: NDArray[np.uint8] | bytes | Image.Image, **kwargs: Any) -> FaceDetectionOutput: + inputs = self._transform(inputs) - bboxes, landmarks = self._detect(inputs) + [bboxes], [landmarks] = self.model.detect(inputs, threshold=kwargs.pop("minScore", 0.7)) return { "boxes": bboxes[:, :4].round(), "scores": bboxes[:, 4], @@ -48,5 +37,7 @@ class FaceDetector(InferenceModel): def _detect(self, inputs: NDArray[np.uint8] | bytes) -> tuple[NDArray[np.float32], NDArray[np.float32]]: return self.model.detect(inputs) # type: ignore - def configure(self, **kwargs: Any) -> None: - self.model.det_thresh = kwargs.pop("minScore", self.model.det_thresh) + def _transform(self, inputs: NDArray[np.uint8] | bytes | Image.Image) -> NDArray[np.uint8]: + image = decode_pil(inputs) + padded = pad(image, (640, 640), method=Image.Resampling.BICUBIC) + return np.array(padded, dtype=np.uint8)[None, ...] diff --git a/machine-learning/app/models/facial_recognition/scrfd.py b/machine-learning/app/models/facial_recognition/scrfd.py new file mode 100644 index 0000000000..7590ec1132 --- /dev/null +++ b/machine-learning/app/models/facial_recognition/scrfd.py @@ -0,0 +1,325 @@ +# Based on InsightFace-REST by SthPhoenix https://github.com/SthPhoenix/InsightFace-REST/blob/master/src/api_trt/modules/model_zoo/detectors/scrfd.py +# Primary changes made: +# 1. Removed CuPy-related code +# 2. Adapted proposal generation to be thread-safe +# 3. Added typing +# 4. Assume RGB input +# 5. Removed unused variables + +# Copyright 2021 SthPhoenix + +# Licensed 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 + +# http://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. + +# -*- coding: utf-8 -*- +# Based on Jia Guo reference implementation at +# https://github.com/deepinsight/insightface/blob/master/detection/scrfd/tools/scrfd.py + + +from __future__ import division + +import cv2 +import numpy as np +from numba import njit +from app.schemas import ModelSession +from numpy.typing import NDArray + + +@njit(cache=True, nogil=True) +def nms(dets, threshold: float = 0.4) -> NDArray[np.float32]: + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1 + 1) + h = np.maximum(0.0, yy2 - yy1 + 1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= threshold)[0] + order = order[inds + 1] + + return np.asarray(keep) + + +@njit(fastmath=True, cache=True, nogil=True) +def single_distance2bbox(point: NDArray[np.float32], distance: NDArray[np.float32], stride: int) -> NDArray[np.float32]: + """ + Fast conversion of single bbox distances to coordinates + + :param point: Anchor point + :param distance: Bbox distances from anchor point + :param stride: Current stride scale + :return: bbox + """ + distance[0] = point[0] - distance[0] * stride + distance[1] = point[1] - distance[1] * stride + distance[2] = point[0] + distance[2] * stride + distance[3] = point[1] + distance[3] * stride + return distance + + +@njit(fastmath=True, cache=True, nogil=True) +def single_distance2kps(point: NDArray[np.float32], distance: NDArray[np.float32], stride: int) -> NDArray[np.float32]: + """ + Fast conversion of single keypoint distances to coordinates + + :param point: Anchor point + :param distance: Keypoint distances from anchor point + :param stride: Current stride scale + :return: keypoint + """ + for ix in range(0, distance.shape[0], 2): + distance[ix] = distance[ix] * stride + point[0] + distance[ix + 1] = distance[ix + 1] * stride + point[1] + return distance + + +@njit(fastmath=True, cache=True, nogil=True) +def generate_proposals( + score_blob: NDArray[np.float32], + bbox_blob: NDArray[np.float32], + kpss_blob: NDArray[np.float32], + stride: int, + anchors: NDArray[np.float32], + threshold: float, +) -> tuple[NDArray[np.float32], NDArray[np.float32], NDArray[np.float32]]: + """ + Convert distances from anchors to actual coordinates on source image + and filter proposals by confidence threshold. + + :param score_blob: Raw scores for stride + :param bbox_blob: Raw bbox distances for stride + :param kpss_blob: Raw keypoints distances for stride + :param stride: Stride scale + :param anchors: Precomputed anchors for stride + :param threshold: Confidence threshold + :return: Filtered scores, bboxes and keypoints + """ + + idxs = [] + for ix in range(score_blob.shape[0]): + if score_blob[ix][0] > threshold: + idxs.append(ix) + + score_out = np.empty((len(idxs), 1), dtype="float32") + bbox_out = np.empty((len(idxs), 4), dtype="float32") + kpss_out = np.empty((len(idxs), 10), dtype="float32") + + for i in range(len(idxs)): + ix = idxs[i] + score_out[i] = score_blob[ix] + bbox_out[i] = single_distance2bbox(anchors[ix], bbox_blob[ix], stride) + kpss_out[i] = single_distance2kps(anchors[ix], kpss_blob[ix], stride) + + return score_out, bbox_out, kpss_out + + +@njit(fastmath=True, cache=True, nogil=True) +def filter( + bboxes_list: NDArray[np.float32], + kpss_list: NDArray[np.float32], + scores_list: NDArray[np.float32], + nms_threshold: float = 0.4, +) -> tuple[NDArray[np.float32], NDArray[np.float32]]: + """ + Filter postprocessed network outputs with NMS + + :param bboxes_list: List of bboxes (np.ndarray) + :param kpss_list: List of keypoints (np.ndarray) + :param scores_list: List of scores (np.ndarray) + :return: Face bboxes with scores [t,l,b,r,score], and key points + """ + + pre_det = np.hstack((bboxes_list, scores_list)) + keep = nms(pre_det, threshold=nms_threshold) + det = pre_det[keep, :] + kpss = kpss_list[keep, :] + kpss = kpss.reshape((kpss.shape[0], -1, 2)) + + return det, kpss + + +class SCRFD: + def __init__(self, session: ModelSession): + self.session = session + self.center_cache: dict[tuple[int, int], NDArray[np.float32]] = {} + self.nms_threshold = 0.4 + self.fmc = 3 + self._feat_stride_fpn = [8, 16, 32] + self._num_anchors = 2 + + def prepare(self, nms_threshold: float = 0.4) -> None: + """ + Populate class parameters + + :param nms_threshold: Threshold for NMS IoU + """ + + self.nms_threshold = nms_threshold + + def detect( + self, imgs: NDArray[np.uint8], threshold: float = 0.5 + ) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]]]: + """ + Run detection pipeline for provided images + + :param img: Raw image as nd.ndarray with HWC shape + :param threshold: Confidence threshold + :return: Face bboxes with scores [t,l,b,r,score], and key points + """ + + height, width = imgs.shape[1:3] + blob = self._preprocess(imgs) + net_outs = self._forward(blob) + + batch_bboxes, batch_kpss, batch_scores = self._postprocess(net_outs, height, width, threshold) + + dets_list = [] + kpss_list = [] + for e in range(imgs.shape[0]): + if len(batch_bboxes[e]) == 0: + det, kpss = np.zeros((0, 5), dtype="float32"), np.zeros((0, 10), dtype="float32") + else: + det, kpss = filter(batch_bboxes[e], batch_kpss[e], batch_scores[e], self.nms_threshold) + + dets_list.append(det) + kpss_list.append(kpss) + + return dets_list, kpss_list + + @staticmethod + def _build_anchors( + input_height: int, input_width: int, strides: list[int], num_anchors: int + ) -> NDArray[np.float32]: + """ + Precompute anchor points for provided image size + + :param input_height: Input image height + :param input_width: Input image width + :param strides: Model strides + :param num_anchors: Model num anchors + :return: box centers + """ + + centers = [] + for stride in strides: + height = input_height // stride + width = input_width // stride + + anchor_centers = np.stack(np.mgrid[:height, :width][::-1], axis=-1).astype(np.float32) + anchor_centers = (anchor_centers * stride).reshape((-1, 2)) + if num_anchors > 1: + anchor_centers = np.stack([anchor_centers] * num_anchors, axis=1).reshape((-1, 2)) + centers.append(anchor_centers) + return centers + + def _preprocess(self, images: NDArray[np.uint8]): + """ + Normalize image on CPU if backend can't provide CUDA stream, + otherwise preprocess image on GPU using CuPy + + :param img: Raw image as np.ndarray with HWC shape + :return: Preprocessed image or None if image was processed on device + """ + + input_size = tuple(images[0].shape[0:2][::-1]) + return cv2.dnn.blobFromImages(images, 1.0 / 128, input_size, (127.5, 127.5, 127.5), swapRB=False) + + def _forward(self, blob: NDArray[np.float32]) -> list[NDArray[np.float32]]: + """ + Send input data to inference backend. + + :param blob: Preprocessed image of shape NCHW or None + :return: network outputs + """ + + return self.session.run(None, {"input.1": blob}) + + def _postprocess( + self, net_outs: list[NDArray[np.float32]], height: int, width: int, threshold: float + ) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]], list[NDArray[np.float32]]]: + """ + Precompute anchor points for provided image size and process network outputs + + :param net_outs: Network outputs + :param input_height: Input image height + :param input_width: Input image width + :param threshold: Confidence threshold + :return: filtered bboxes, keypoints and scores + """ + + key = (height, width) + + if not self.center_cache.get(key): + self.center_cache[key] = self._build_anchors(height, width, self._feat_stride_fpn, self._num_anchors) + anchor_centers = self.center_cache[key] + bboxes, kpss, scores = self._process_strides(net_outs, threshold, anchor_centers) + return bboxes, kpss, scores + + def _process_strides( + self, net_outs: list[NDArray[np.float32]], threshold: float, anchors: NDArray[np.float32] + ) -> tuple[list[NDArray[np.float32]], list[NDArray[np.float32]], list[NDArray[np.float32]]]: + """ + Process network outputs by strides and return results proposals filtered by threshold + + :param net_outs: Network outputs + :param threshold: Confidence threshold + :param anchor_centers: Precomputed anchor centers for all strides + :return: filtered bboxes, keypoints and scores + """ + + batch_size = net_outs[0].shape[0] + bboxes_by_img = [] + kpss_by_img = [] + scores_by_img = [] + + for batch in range(batch_size): + scores_strided = [] + bboxes_strided = [] + kpss_strided = [] + for idx, stride in enumerate(self._feat_stride_fpn): + score_blob = net_outs[idx][batch] + bbox_blob = net_outs[idx + self.fmc][batch] + kpss_blob = net_outs[idx + self.fmc * 2][batch] + stride_anchors = anchors[idx] + score_list, bbox_list, kpss_list = generate_proposals( + score_blob, + bbox_blob, + kpss_blob, + stride, + stride_anchors, + threshold, + ) + + scores_strided.append(score_list) + bboxes_strided.append(bbox_list) + kpss_strided.append(kpss_list) + bboxes_by_img.append(np.concatenate(bboxes_strided, axis=0)) + kpss_by_img.append(np.concatenate(kpss_strided, axis=0)) + scores_by_img.append(np.concatenate(scores_strided, axis=0)) + + return bboxes_by_img, kpss_by_img, scores_by_img diff --git a/machine-learning/app/models/session.py b/machine-learning/app/models/session.py index ddd87a6a27..ecdbae9afe 100644 --- a/machine-learning/app/models/session.py +++ b/machine-learning/app/models/session.py @@ -12,12 +12,12 @@ def ort_has_batch_dim(session: ort.InferenceSession) -> bool: return session.get_inputs()[0].shape[0] == "batch" -def ort_squeeze_outputs(session: ort.InferenceSession) -> None: +def ort_expand_outputs(session: ort.InferenceSession) -> None: original_run = session.run def run(output_names: list[str], input_feed: dict[str, NDArray[np.float32]]) -> list[NDArray[np.float32]]: out: list[NDArray[np.float32]] = original_run(output_names, input_feed) - out = [o.squeeze(axis=0) for o in out] + out = [np.expand_dims(o, axis=0) for o in out] return out session.run = run diff --git a/machine-learning/app/models/transforms.py 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