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commit
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{
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||||
"nodes": {
|
||||
"darknet": {
|
||||
"flake": false,
|
||||
"locked": {
|
||||
"lastModified": 1658093200,
|
||||
"narHash": "sha256-Bhvbc06IeA4oNz93WiPmz9TXwxz7LQ6L8HPr8UEvzvE=",
|
||||
"owner": "pjreddie",
|
||||
"repo": "darknet",
|
||||
"rev": "f6afaabcdf85f77e7aff2ec55c020c0e297c77f9",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "pjreddie",
|
||||
"repo": "darknet",
|
||||
"type": "github"
|
||||
}
|
||||
},
|
||||
"nixpkgs": {
|
||||
"locked": {
|
||||
"lastModified": 1672781980,
|
||||
"narHash": "sha256-L+yqt2szcp+BFiWoMJCisDsNA5OrpYVW1QSbbS5U8RU=",
|
||||
"owner": "NixOS",
|
||||
"repo": "nixpkgs",
|
||||
"rev": "a9eedea7232f5d00f0aca7267efb69a54da1b8a1",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"id": "nixpkgs",
|
||||
"ref": "nixos-22.11",
|
||||
"type": "indirect"
|
||||
}
|
||||
},
|
||||
"root": {
|
||||
"inputs": {
|
||||
"darknet": "darknet",
|
||||
"nixpkgs": "nixpkgs",
|
||||
"utils": "utils"
|
||||
}
|
||||
},
|
||||
"utils": {
|
||||
"locked": {
|
||||
"lastModified": 1667395993,
|
||||
"narHash": "sha256-nuEHfE/LcWyuSWnS8t12N1wc105Qtau+/OdUAjtQ0rA=",
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"rev": "5aed5285a952e0b949eb3ba02c12fa4fcfef535f",
|
||||
"type": "github"
|
||||
},
|
||||
"original": {
|
||||
"owner": "numtide",
|
||||
"repo": "flake-utils",
|
||||
"type": "github"
|
||||
}
|
||||
}
|
||||
},
|
||||
"root": "root",
|
||||
"version": 7
|
||||
}
|
||||
|
|
@ -0,0 +1,62 @@
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|||
{
|
||||
inputs = {
|
||||
darknet = {
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||||
url = "github:pjreddie/darknet";
|
||||
flake = false;
|
||||
};
|
||||
nixpkgs.url = "nixpkgs/nixos-22.11";
|
||||
utils.url = "github:numtide/flake-utils";
|
||||
};
|
||||
|
||||
outputs = { self, nixpkgs, darknet, utils, ... }:
|
||||
utils.lib.eachDefaultSystem (system:
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||||
let
|
||||
pkgs = nixpkgs.legacyPackages."${system}";
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||||
pythonYolo = pkgs.python3.withPackages
|
||||
(pyPkgs: with pyPkgs; [ fastapi opencv4 python-multipart ]);
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||||
in {
|
||||
packages = rec {
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||||
objectifier = pkgs.callPackage ./objectifier.nix { };
|
||||
yolo-cli = pkgs.callPackage ./yolo-cli.nix { inherit yolov3-data; };
|
||||
yolov3-data = pkgs.callPackage ./yolo-data.nix { inherit darknet; };
|
||||
};
|
||||
|
||||
devShells = {
|
||||
default = pkgs.mkShell {
|
||||
buildInputs = let
|
||||
pythonYolo = pkgs.python3.withPackages (pyPkgs:
|
||||
with pyPkgs; [
|
||||
fastapi
|
||||
gunicorn
|
||||
opencv4
|
||||
python-multipart
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||||
uvicorn
|
||||
]);
|
||||
in [ pythonYolo ];
|
||||
};
|
||||
yolo-cli = pkgs.mkShell {
|
||||
buildInputs = [ self.packages."${system}".yolo-cli ];
|
||||
};
|
||||
};
|
||||
}) // {
|
||||
nixosModules = rec {
|
||||
default = objectifier;
|
||||
objectifier = {
|
||||
imports = [ ./objectifier-module.nix ];
|
||||
config.nixpkgs.settings.overlays = [ self.overlays.objectifier ];
|
||||
};
|
||||
};
|
||||
|
||||
overlays = rec {
|
||||
default = final: prev: {
|
||||
inherit (self.packages."${prev.system}") objectifier yolo-cli;
|
||||
};
|
||||
objectifier = final: prev: {
|
||||
inherit (self.packages."${prev.system}") objectifier;
|
||||
};
|
||||
yolo-cli = final: prev: {
|
||||
inherit (self.packages."${prev.system}") yolo-cli;
|
||||
};
|
||||
};
|
||||
};
|
||||
}
|
||||
|
|
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|
|||
{ config, lib, pkgs, ... }:
|
||||
|
||||
with lib;
|
||||
let
|
||||
cfg = config.services.objectifier;
|
||||
|
||||
pythonYolo = pkgs.python3.withPackages (pyPkgs:
|
||||
with pyPkgs; [
|
||||
fastapi
|
||||
gunicorn
|
||||
opencv4
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||||
python-multipart
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||||
uvicorn
|
||||
]);
|
||||
|
||||
in {
|
||||
options.services.objectifier = with types; {
|
||||
enable = mkEnableOption "Enable Objectifier object-detection web sevice.";
|
||||
|
||||
port = mkOption {
|
||||
type = port;
|
||||
description = "Port on which to run the Objectifier web service.";
|
||||
default = 5121;
|
||||
};
|
||||
|
||||
workers = mkOption {
|
||||
type = int;
|
||||
description = "Number of worker threads to launch.";
|
||||
default = 3;
|
||||
};
|
||||
|
||||
listen-addresses = mkOption {
|
||||
type = listOf str;
|
||||
description =
|
||||
"List of IP addresses on which to listen for incoming requests.";
|
||||
default = [ "127.0.0.1" ];
|
||||
};
|
||||
};
|
||||
|
||||
config = mkIf cfg.enable {
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||||
systemd.services.objectifier = {
|
||||
after = [ "network-online.target" ];
|
||||
wantedBy = [ "default.target" ];
|
||||
reloadIfChanged = true;
|
||||
path = with pkgs; [ pythonYolo ];
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||||
environment = {
|
||||
OBJECTIFIER_YOLOV3_CONFIG = "${yolo-data}/yolov3.cfg";
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||||
OBJECTIFIER_YOLOV3_WEIGHTS = "${yolo-data}/yolov3.weights";
|
||||
OBJECTIFIER_YOLOV3_LABELS = "${yolo-data}/labels";
|
||||
OBJECTIFIER_BUFFER_SIZE = 524288;
|
||||
};
|
||||
serviceConfig = {
|
||||
# PrivateUsers = true;
|
||||
# PrivateDevices = true;
|
||||
# PrivateTmp = true;
|
||||
# PrivateMounts = true;
|
||||
# ProtectControlGroups = true;
|
||||
# ProtectKernelTunables = true;
|
||||
# ProtectKernelModules = true;
|
||||
# ProtectSystem = true;
|
||||
# ProtectHostname = true;
|
||||
# ProtectHome = true;
|
||||
# ProtectClock = true;
|
||||
# ProtectKernelLogs = true;
|
||||
# DynamicUser = true;
|
||||
# MemoryDenyWriteExecute = true;
|
||||
# RestrictRealtime = true;
|
||||
# LockPersonality = true;
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||||
# PermissionsStartOnly = true;
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||||
WorkingDirectory = "${pkgs.objectifier}";
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||||
Restart = "on-failure";
|
||||
Type = "simple";
|
||||
PIDFile = "/run/objectifier.pid";
|
||||
ExecStart = let
|
||||
bindClause =
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map (addr: "--bind ${addr}:${cfg.port}") cfg.listen-addresses;
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||||
in concatStringsSep " " [
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||||
"gunicorn"
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||||
bindClause
|
||||
"--workers ${cfg.workers}"
|
||||
"-k uvicorn.workers.UvicornWorker"
|
||||
"objectifier:app"
|
||||
"--pid /run/objectifier.pid"
|
||||
];
|
||||
};
|
||||
};
|
||||
};
|
||||
}
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||||
|
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|||
{ pkgs, lib, ... }:
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||||
|
||||
pkgs.stdenv.mkDerivation {
|
||||
name = "objectifier";
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||||
src = ./src;
|
||||
phases = [ "installPhase" ];
|
||||
installPhase = ''
|
||||
mkdir -p $out
|
||||
cp $src/detector.py $out/detector.py
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||||
cp $src/yolo-cli.py $out/objectifier.py
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||||
'';
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||||
}
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||||
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#!/usr/bin/env python3
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||||
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||||
import cv2 as cv
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||||
import numpy as np
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||||
import sys
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||||
import shutil
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||||
from os import path
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||||
import hashlib
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||||
|
||||
import tempfile
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||||
from pathlib import Path
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||||
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||||
class Detection:
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||||
"""Represents an object dectected in an image."""
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||||
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||||
def __init__(self, label, confidence, box):
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||||
self.label = label
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||||
self.confidence = confidence
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||||
self.box = box
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||||
|
||||
class AnalyzedImage:
|
||||
"""The result of performing object detection on an image."""
|
||||
|
||||
def __init__(self, filename, detections, outfile):
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||||
self.detections = detections
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||||
self.outfile = outfile
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||||
|
||||
class Detector:
|
||||
"""Detects objects in images, returning an AnalyzedImage."""
|
||||
|
||||
def __init__(self, weights, cfg, classes, tempdir, confidence=0.6):
|
||||
self.net = cv.dnn.readNet(weights, cfg)
|
||||
self.classes = classes
|
||||
self.layer_names = self.net.getLayerNames()
|
||||
self.output_layer = [self.layer_names[i - 1] for i in self.net.getUnconnectedOutLayers()]
|
||||
self.tmpdir = tempdir
|
||||
self.minimum_confidence = confidence
|
||||
|
||||
def output_filename(self, filename):
|
||||
simple_name = path.splitext(path.basename(filename))[0]
|
||||
return str(self.tmpdir / (simple_name + ".png"))
|
||||
|
||||
def detect_objects(self, filename, output_filename=None):
|
||||
img = cv.imread(str(filename))
|
||||
height, width, channel = img.shape
|
||||
blob = cv.dnn.blobFromImage(img, 0.00392, (416, 416), (0,0,0), True, crop=False)
|
||||
self.net.setInput(blob)
|
||||
outs = self.net.forward(self.output_layer)
|
||||
|
||||
class_ids = []
|
||||
confidences = []
|
||||
boxes = []
|
||||
detections = []
|
||||
|
||||
for out in outs:
|
||||
for detection in out:
|
||||
scores = detection[5:]
|
||||
class_id = np.argmax(scores)
|
||||
confidence = scores[class_id]
|
||||
if confidence > self.minimum_confidence:
|
||||
center_x = int(detection[0] * width)
|
||||
center_y = int(detection[1] * height)
|
||||
w = int(detection[2] * width)
|
||||
h = int(detection[3] * height)
|
||||
x = int(center_x - w/2)
|
||||
y = int(center_y - h/2)
|
||||
boxes.append([x, y, w, h])
|
||||
confidences.append(float(confidence))
|
||||
class_ids.append(class_id)
|
||||
|
||||
indexes = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
|
||||
|
||||
for i in indexes:
|
||||
label = str(self.classes[class_ids[i]])
|
||||
box = [int(n) for n in boxes[i]]
|
||||
detections.append(Detection(label, confidences[i], box))
|
||||
|
||||
font = cv.FONT_HERSHEY_PLAIN
|
||||
marked = cv.imread(filename)
|
||||
for detection in detections:
|
||||
x, y, w, h = detection.box
|
||||
cv.rectangle(marked, (x,y), (x + w, y + h), (255,255,255,0), 2)
|
||||
cv.putText(marked, detection.label, (x,y+30), font, 3, (255,255,255,0), 1)
|
||||
|
||||
out_file = output_filename if output_filename else self.output_filename(filename)
|
||||
cv.imwrite(out_file, marked)
|
||||
return AnalyzedImage(filename, detections, str(out_file))
|
||||
|
||||
|
||||
|
||||
|
||||
# net = cv.dnn.readNet("/home/niten/Projects/yolo/yolov3.weights", "/home/niten/Projects/yolo/yolov3.cfg")
|
||||
|
||||
# classes = []
|
||||
|
||||
# with open("/home/niten/Projects/yolo/coco.names") as f:
|
||||
# classes = [line.strip() for line in f.readlines()]
|
||||
|
||||
# layer_names = net.getLayerNames()
|
||||
# output_layer = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]
|
||||
# colors = np.random.uniform(0, 255, size=(len(classes), 3))
|
||||
|
||||
# def scale_int(o, s, m):
|
||||
# return (m / s) * o
|
||||
|
||||
# def scale_box(orig, scaled, box):
|
||||
# o_h, o_w, _ = orig.shape
|
||||
# s_h, s_w, _ = scaled.shape
|
||||
# x, y, w, h = box
|
||||
# return [scale_int(o_w, s_w, x),
|
||||
# scale_int(o_h, s_h, y),
|
||||
# scale_int(o_w, o_h, w),
|
||||
# scale_int(o_h, s_h, h)]
|
||||
|
||||
# tmpdir = Path(tempfile.mkdtemp())
|
||||
|
||||
# def detect_objects(filename):
|
||||
# simplename = path.splitext(path.basename(filename))[0]
|
||||
# out_filename = tmpdir / ("processed_" + simplename + ".png")
|
||||
|
||||
# orig = cv.imread(str(filename))
|
||||
# img = cv.imread(str(filename))
|
||||
# # img = cv.resize(img, None, fx=0.4, fy=0.4)
|
||||
# height, width, channel = img.shape
|
||||
# # TODO: Change scale factor?
|
||||
# blob = cv.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)
|
||||
# net.setInput(blob)
|
||||
# outs = net.forward(output_layer)
|
||||
|
||||
# class_ids = []
|
||||
# confidences = []
|
||||
# boxes = []
|
||||
|
||||
# detections = []
|
||||
|
||||
# for out in outs:
|
||||
# for detection in out:
|
||||
# scores = detection[5:]
|
||||
# class_id = np.argmax(scores)
|
||||
# confidence = scores[class_id]
|
||||
# if confidence > 0.6:
|
||||
# center_x = int(detection[0] * width)
|
||||
# center_y = int(detection[1] * height)
|
||||
# w = int(detection[2] * width)
|
||||
# h = int(detection[3] * height)
|
||||
# x = int(center_x - w/2)
|
||||
# y = int(center_y - h/2)
|
||||
# boxes.append([x, y, w, h])
|
||||
# confidences.append(float(confidence))
|
||||
# class_ids.append(class_id)
|
||||
|
||||
# indexes = cv.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
|
||||
|
||||
# font = cv.FONT_HERSHEY_PLAIN
|
||||
# for i in range(len(boxes)):
|
||||
# if i in indexes:
|
||||
# label = str(classes[class_ids[i]])
|
||||
# color = colors[i]
|
||||
# scaled_box = scale_box(orig, img, boxes[i])
|
||||
# x, y, w, h = [int(n) for n in scaled_box]
|
||||
# detections.append(Detection(label, confidences[i], scaled_box))
|
||||
# # cv.rectangle(out, (x, y), (x + w, y + h), color, 2)
|
||||
# # cv.putText(out, label, (x, y + 30), font, 3, color, 3)
|
||||
|
||||
# #cv.imwrite(str(out_filename), out)
|
||||
# marked = cv.imread(filename)
|
||||
# for detection in detections:
|
||||
# x, y, w, h = [int(n) for n in detection.box]
|
||||
# cv.rectangle(marked, (x,y), (x + w, y + h), (255,255,255,0), 2)
|
||||
# cv.putText(marked, detection.label, (x, y + 30), font, 3, (255,255,255,0), 1)
|
||||
|
||||
# cv.imwrite(str(out_filename), marked)
|
||||
# return AnalyzedImage(filename, detections, str(out_filename))
|
||||
|
||||
# # cv.imshow("IMG", img)
|
||||
# # cv.waitKey(0)
|
||||
# # cv.destroyAllWindows()
|
||||
|
||||
# for filename in sys.argv[1:]:
|
||||
# print(filename + ":")
|
||||
# output = detect_objects(filename)
|
||||
# print(" OUTPUT: " + str(output.outfile))
|
||||
# for detection in output.detections:
|
||||
# print(" " + detection.label +
|
||||
# " (" + str(detection.confidence) + ")" +
|
||||
# " [" +
|
||||
# str(detection.box[0]) + ", " +
|
||||
# str(detection.box[1]) + ", " +
|
||||
# str(detection.box[2]) + ", " +
|
||||
# str(detection.box[3]) +
|
||||
# "]")
|
||||
|
||||
|
||||
# classes = []
|
||||
|
||||
# with open("/home/niten/Projects/yolo/coco.names") as f:
|
||||
# classes = [line.strip() for line in f.readlines()]
|
||||
|
||||
# detector = Detector("/home/niten/Projects/yolo/yolov3.weights", "/home/niten/Projects/yolo/yolov3.cfg", classes, Path(tempfile.mkdtemp()))
|
||||
|
||||
# for filename in sys.argv[1:]:
|
||||
# print(filename + ":")
|
||||
# output = detector.detect_objects(filename)
|
||||
# print(" OUTPUT: " + str(output.outfile))
|
||||
# for detection in output.detections:
|
||||
# print(" " + detection.label +
|
||||
# " (" + str(detection.confidence) + ")" +
|
||||
# " [" +
|
||||
# str(detection.box[0]) + ", " +
|
||||
# str(detection.box[1]) + ", " +
|
||||
# str(detection.box[2]) + ", " +
|
||||
# str(detection.box[3]) +
|
||||
# "]")
|
||||
|
|
@ -0,0 +1,78 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
from fastapi import FastAPI, HTTPException, Request, UploadFile
|
||||
from fastapi.responses import FileResponse
|
||||
from detector import Detector
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
import hashlib
|
||||
import os
|
||||
|
||||
incoming_dir = Path(tempfile.mkdtemp())
|
||||
outgoing_dir = Path(tempfile.mkdtemp())
|
||||
|
||||
def get_envvar(name):
|
||||
return os.environ.get(name)
|
||||
|
||||
def get_envvar_or_fail(name):
|
||||
result = get_envvar(name)
|
||||
if result:
|
||||
return result
|
||||
else:
|
||||
raise EnvironmentError('Missing required environment variable: ' + name)
|
||||
|
||||
yolo_config = get_envvar_or_fail('OBJECTIFIER_YOLOV3_CONFIG')
|
||||
yolo_weights = get_envvar_or_fail('OBJECTIFIER_YOLOV3_WEIGHTS')
|
||||
yolo_labels = get_envvar_or_fail('OBJECTIFIER_YOLOV3_LABELS')
|
||||
buffer_size = get_envvar('OBJECTIFIER_BUFFER_SIZE') or 524288
|
||||
|
||||
detector = Detector(
|
||||
yolo_weights,
|
||||
yolo_config,
|
||||
yolo_labels,
|
||||
outgoing_dir)
|
||||
|
||||
app = FastAPI()
|
||||
|
||||
analyzed_images = {}
|
||||
|
||||
def detection_to_dict(d):
|
||||
return {
|
||||
"label": d.label,
|
||||
"confidence": d.confidence,
|
||||
"box": {
|
||||
"x": d.box[0],
|
||||
"y": d.box[1],
|
||||
"width": d.box[2],
|
||||
"height": d.box[3],
|
||||
},
|
||||
}
|
||||
|
||||
def result_to_dict(res, base_url):
|
||||
return {
|
||||
"labels": map(lambda d: d.label, res.detections),
|
||||
"detections": map(detection_to_dict, res.detections),
|
||||
"output": base_url + d.outfile,
|
||||
}
|
||||
|
||||
@app.put("/images/")
|
||||
async def analyze_image(file: UploadFile, request: Request):
|
||||
base_url = re.sub(r'\/images\/$', '/analyzed_images/', str(request.url))
|
||||
infile = open(incoming_dir / file.filename)
|
||||
file_hash = hashlib.sha256()
|
||||
with open(infile, mode="wb") as f:
|
||||
chunk = f.read(buffer_size)
|
||||
while chunk:
|
||||
file_hash.update(chunk)
|
||||
infile.write(chunk)
|
||||
chunk=f.read(buffer_size)
|
||||
result = detector.detect_objects(infile, file_hash.hexdigest() + ".png")
|
||||
return result_to_dict(result, base_url)
|
||||
|
||||
@app.get("/analyzed_images/${image_name}", response_class=FileResponse)
|
||||
def get_analyzed_image(image_name):
|
||||
filename = str(outgoing_dir / image_name)
|
||||
if path.isfile(filename):
|
||||
return filename
|
||||
else:
|
||||
raise HTTPException(status_code=404, detail="file not found: " + filename)
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
#!/usr/bin/env python
|
||||
|
||||
import argparse
|
||||
from detector import Detector
|
||||
import tempfile
|
||||
from pathlib import Path
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
prog = 'YOLO CLI',
|
||||
description = 'YOLO Command Line Interface.')
|
||||
|
||||
parser.add_argument('-w', '--yolo_weights',
|
||||
help="Weight file for YOLOv3 object detection.")
|
||||
parser.add_argument('-c', '--yolo_config',
|
||||
help="Configuration file for YOLOv3 object detection")
|
||||
parser.add_argument('-l', '--yolo_labels',
|
||||
help="File containing list of object labels for YOLOv3 object detection")
|
||||
|
||||
parser.add_argument('files', metavar='FILE', type=str, nargs='*');
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
outgoing_dir = Path(tempfile.mkdtemp())
|
||||
|
||||
classes = []
|
||||
with open(args.yolo_labels) as f:
|
||||
classes = [line.strip() for line in f.readlines()]
|
||||
|
||||
detector = Detector(
|
||||
args.yolo_weights,
|
||||
args.yolo_config,
|
||||
classes,
|
||||
outgoing_dir)
|
||||
|
||||
for filename in args.files:
|
||||
print(filename + ":")
|
||||
result = detector.detect_objects(filename)
|
||||
print(" OUTPUT: " + str(result.outfile))
|
||||
for detection in result.detections:
|
||||
print(" " + detection.label + " (" + str(detection.confidence) + ")")
|
||||
|
|
@ -0,0 +1,28 @@
|
|||
{ pkgs, yolo-data, ... }:
|
||||
|
||||
let
|
||||
name = "yolo-cli";
|
||||
pythonYolo = pkgs.python3.withPackages (pyPkgs: with pyPkgs; [ opencv4 ]);
|
||||
yoloCliFiles = pkgs.stdenv.mkDerivation {
|
||||
name = "yolo-cli-src";
|
||||
src = ./src;
|
||||
phases = [ "installPhase" ];
|
||||
installPhase = ''
|
||||
mkdir -p $out
|
||||
cp $src/detector.py $out/detector.py
|
||||
cp $src/yolo-cli.py $out/yolo-cli.py
|
||||
chmod +x $out/yolo-cli.py
|
||||
'';
|
||||
};
|
||||
|
||||
in pkgs.writeShellApplication {
|
||||
inherit name;
|
||||
runtimeInputs = [ pythonYolo ];
|
||||
text = pkgs.lib.concatStringsSep " " [
|
||||
"${yoloCliFiles}/yolo-cli.py"
|
||||
"--yolo_weights=${yolo-data}/yolov3.weights"
|
||||
"--yolo_config=${yolo-data}/yolov3.cfg"
|
||||
"--yolo_labels=${yolo-data}/labels"
|
||||
''"$@"''
|
||||
];
|
||||
}
|
||||
|
|
@ -0,0 +1,37 @@
|
|||
{ pkgs, lib, buildEnv, stdenv, darknet, ... }:
|
||||
|
||||
with lib;
|
||||
buildEnv {
|
||||
name = "yolov3-data";
|
||||
paths = let
|
||||
cfg = stdenv.mkDerivation {
|
||||
name = "yolov3-cfg";
|
||||
src = darknet;
|
||||
phases = [ "installPhase" ];
|
||||
installPhase = ''
|
||||
mkdir -p $out
|
||||
cp $src/cfg/yolov3.cfg $out/yolov3.cfg
|
||||
'';
|
||||
};
|
||||
|
||||
labels = stdenv.mkDerivation {
|
||||
name = "yolov3-labels";
|
||||
src = darknet;
|
||||
phases = [ "installPhase" ];
|
||||
installPhase = ''
|
||||
mkdir -p $out
|
||||
cp $src/data/coco.names $out/labels
|
||||
'';
|
||||
};
|
||||
|
||||
weights = stdenv.mkDerivation {
|
||||
name = "yolov3-weights";
|
||||
src = ./data;
|
||||
phases = [ "installPhase" ];
|
||||
installPhase = ''
|
||||
mkdir -p $out
|
||||
cp $src/yolov3.weights $out/yolov3.weights
|
||||
'';
|
||||
};
|
||||
in [ cfg labels weights ];
|
||||
}
|
||||
Loading…
Reference in New Issue