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https://github.com/blakeblackshear/frigate.git
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44d8cdbba1
* ROCm AMD/GPU based build and detector, WIP * detectors/rocm: separate yolov8 postprocessing into own function; fix box scaling; use cv2.dnn.blobForImage for preprocessing; assert on required model parameters * AMD/ROCm: add couple of more ultralytics models; comments * docker/rocm: make imported model files readable by all * docker/rocm: readme about running on AMD GPUs * docker/rocm: updated README * docker/rocm: updated README * docker/rocm: updated README * detectors/rocm: separated preprocessing functions into yolo_utils.py * detector/plugins: added onnx cpu plugin * docker/rocm: updated container with limite label sets * example detectors view * docker/rocm: updated README.md * docker/rocm: update README.md * docker/rocm: do not set HSA_OVERRIDE_GFX_VERSION at all for the general version as the empty value broke rocm * detectors: simplified/optimized yolov8_postprocess * detector/yolo_utils: indentation, remove unused variable * detectors/rocm: default option to conserve cpu usage at the expense of latency * detectors/yolo_utils: use nms to prefilter overlapping boxes if too many detected * detectors/edgetpu_tfl: add support for yolov8 * util/download_models: script to download yolov8 model files * docker/main: add download-models overlay into s6 startup * detectors/rocm: assume models are in /config/model_cache/yolov8/ * docker/rocm: compile onnx files into mxr files at startup * switch model download into bash script * detectors/rocm: automatically override HSA_OVERRIDE_GFX_VERSION for couple of known chipsets * docs: rocm detector first notes * typos * describe builds (harakas temporary) * docker/rocm: also build a version for gfx1100 * docker/rocm: use cp instead of tar * docker.rocm: remove README as it is now in detector config * frigate/detectors: renamed yolov8_preprocess->preprocess, pass input tensor element type * docker/main: use newer openvino (2023.3.0) * detectors: implement class aggregation * update yolov8 model * add openvino/yolov8 support for label aggregation * docker: remove pointless s6/timeout-up files * Revert "detectors: implement class aggregation" This reverts commitdcfe6bbf6f. * detectors/openvino: remove class aggregation * detectors: increase yolov8 postprocessing score trershold to 0.5 * docker/rocm: separate rocm distributed files into its own build stage * Update object_detectors.md * updated CODEOWNERS file for rocm * updated build names for documentation * Revert "docker/main: use newer openvino (2023.3.0)" This reverts commitdee95de908. * reverrted openvino detector * reverted edgetpu detector * scratched rocm docs from any mention of edgetpu or openvino * Update docs/docs/configuration/object_detectors.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * renamed frigate.detectors.yolo_utils.py -> frigate.detectors.util.py * clarified rocm example performance * Improved wording and clarified text * Mentioned rocm detector for AMD GPUs * applied ruff formating * applied ruff suggested fixes * docker/rocm: fix missing argument resulting in larger docker image sizes * docs/configuration/object_detectors: fix links to yolov8 release files --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
import logging
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import cv2
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import numpy as np
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logger = logging.getLogger(__name__)
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def preprocess(tensor_input, model_input_shape, model_input_element_type):
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model_input_shape = tuple(model_input_shape)
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assert tensor_input.dtype == np.uint8, f"tensor_input.dtype: {tensor_input.dtype}"
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if len(tensor_input.shape) == 3:
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tensor_input = tensor_input[np.newaxis, :]
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if model_input_element_type == np.uint8:
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# nothing to do for uint8 model input
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assert (
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model_input_shape == tensor_input.shape
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), f"model_input_shape: {model_input_shape}, tensor_input.shape: {tensor_input.shape}"
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return tensor_input
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assert (
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model_input_element_type == np.float32
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), f"model_input_element_type: {model_input_element_type}"
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# tensor_input must be nhwc
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assert tensor_input.shape[3] == 3, f"tensor_input.shape: {tensor_input.shape}"
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if tensor_input.shape[1:3] != model_input_shape[2:4]:
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logger.warn(
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f"preprocess: tensor_input.shape {tensor_input.shape} and model_input_shape {model_input_shape} do not match!"
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)
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# cv2.dnn.blobFromImage is faster than numpying it
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return cv2.dnn.blobFromImage(
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tensor_input[0],
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1.0 / 255,
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(model_input_shape[3], model_input_shape[2]),
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None,
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swapRB=False,
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)
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def yolov8_postprocess(
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model_input_shape,
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tensor_output,
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box_count=20,
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score_threshold=0.5,
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nms_threshold=0.5,
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):
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model_box_count = tensor_output.shape[2]
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probs = tensor_output[0, 4:, :]
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all_ids = np.argmax(probs, axis=0)
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all_confidences = probs.T[np.arange(model_box_count), all_ids]
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all_boxes = tensor_output[0, 0:4, :].T
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mask = all_confidences > score_threshold
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class_ids = all_ids[mask]
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confidences = all_confidences[mask]
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cx, cy, w, h = all_boxes[mask].T
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if model_input_shape[3] == 3:
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scale_y, scale_x = 1 / model_input_shape[1], 1 / model_input_shape[2]
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else:
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scale_y, scale_x = 1 / model_input_shape[2], 1 / model_input_shape[3]
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detections = np.stack(
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(
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class_ids,
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confidences,
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scale_y * (cy - h / 2),
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scale_x * (cx - w / 2),
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scale_y * (cy + h / 2),
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scale_x * (cx + w / 2),
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),
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axis=1,
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)
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if detections.shape[0] > box_count:
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# if too many detections, do nms filtering to suppress overlapping boxes
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boxes = np.stack((cx - w / 2, cy - h / 2, w, h), axis=1)
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indexes = cv2.dnn.NMSBoxes(boxes, confidences, score_threshold, nms_threshold)
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detections = detections[indexes]
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# if still too many, trim the rest by confidence
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if detections.shape[0] > box_count:
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detections = detections[
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np.argpartition(detections[:, 1], -box_count)[-box_count:]
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]
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detections = detections.copy()
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detections.resize((box_count, 6))
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return detections
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