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Remove all AGPL licensed YOLO references from Frigate (#10717)

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* Remove yolov8 support from Frigate

* Remove yolov8 from dev

* Remove builds

* Formatting and remove yolov5

* Fix lint

* remove models download

---------

Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com>
This commit is contained in:
Blake Blackshear
2024-03-30 06:46:17 -04:00
committed by GitHub
parent 0223d6df60
commit 14235c42b9
16 changed files with 81 additions and 671 deletions
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94
frigate/detectors/plugins/rknn.py
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@@ -1,10 +1,7 @@
import logging
import os.path
import urllib.request
from typing import Literal
import numpy as np
try:
from hide_warnings import hide_warnings
except: # noqa: E722
@@ -24,14 +21,6 @@ DETECTOR_KEY = "rknn"
supported_socs = ["rk3562", "rk3566", "rk3568", "rk3588"]
yolov8_suffix = {
"default-yolov8n": "n",
"default-yolov8s": "s",
"default-yolov8m": "m",
"default-yolov8l": "l",
"default-yolov8x": "x",
}
class RknnDetectorConfig(BaseDetectorConfig):
type: Literal[DETECTOR_KEY]
@@ -68,35 +57,12 @@ class Rknn(DetectionApi):
elif "rk3588" in soc:
os.rename("/usr/lib/librknnrt_rk3588.so", "/usr/lib/librknnrt.so")
self.model_path = config.model.path or "default-yolov8n"
self.core_mask = config.core_mask
self.height = config.model.height
self.width = config.model.width
if self.model_path in yolov8_suffix:
if self.model_path == "default-yolov8n":
self.model_path = "/models/rknn/yolov8n-320x320-{soc}.rknn".format(
soc=soc
)
else:
model_suffix = yolov8_suffix[self.model_path]
self.model_path = (
"/config/model_cache/rknn/yolov8{suffix}-320x320-{soc}.rknn".format(
suffix=model_suffix, soc=soc
)
)
os.makedirs("/config/model_cache/rknn", exist_ok=True)
if not os.path.isfile(self.model_path):
logger.info(
"Downloading yolov8{suffix} model.".format(suffix=model_suffix)
)
urllib.request.urlretrieve(
"https://github.com/MarcA711/rknn-models/releases/download/v1.5.2-{soc}/yolov8{suffix}-320x320-{soc}.rknn".format(
soc=soc, suffix=model_suffix
),
self.model_path,
)
if True:
os.makedirs("/config/model_cache/rknn", exist_ok=True)
if (config.model.width != 320) or (config.model.height != 320):
logger.error(
@@ -132,60 +98,12 @@ class Rknn(DetectionApi):
"Error initializing rknn runtime. Do you run docker in privileged mode?"
)
def __del__(self):
self.rknn.release()
def postprocess(self, results):
"""
Processes yolov8 output.
Args:
results: array with shape: (1, 84, n, 1) where n depends on yolov8 model size (for 320x320 model n=2100)
Returns:
detections: array with shape (20, 6) with 20 rows of (class, confidence, y_min, x_min, y_max, x_max)
"""
results = np.transpose(results[0, :, :, 0]) # array shape (2100, 84)
scores = np.max(
results[:, 4:], axis=1
) # array shape (2100,); max confidence of each row
# remove lines with score scores < 0.4
filtered_arg = np.argwhere(scores > 0.4)
results = results[filtered_arg[:, 0]]
scores = scores[filtered_arg[:, 0]]
num_detections = len(scores)
if num_detections == 0:
return np.zeros((20, 6), np.float32)
if num_detections > 20:
top_arg = np.argpartition(scores, -20)[-20:]
results = results[top_arg]
scores = scores[top_arg]
num_detections = 20
classes = np.argmax(results[:, 4:], axis=1)
boxes = np.transpose(
np.vstack(
(
(results[:, 1] - 0.5 * results[:, 3]) / self.height,
(results[:, 0] - 0.5 * results[:, 2]) / self.width,
(results[:, 1] + 0.5 * results[:, 3]) / self.height,
(results[:, 0] + 0.5 * results[:, 2]) / self.width,
)
)
raise Exception(
"RKNN does not currently support any models. Please see the docs for more info."
)
detections = np.zeros((20, 6), np.float32)
detections[:num_detections, 0] = classes
detections[:num_detections, 1] = scores
detections[:num_detections, 2:] = boxes
return detections
def __del__(self):
self.rknn.release()
@hide_warnings
def inference(self, tensor_input):