def get_YOLOv3_Predictor():
# has been trained with coco
file_yolov3_cfg = getDataFile("yolov3/yolov3.cfg")
yolov3_training_ctx = TrainingContext(
n_classes = 80,
trainfile = "/home/pjreddie/data/coco/trainvalno5k.txt",
validfile = "coco_testdev",
namefile = getDataFile("yolov3/coco.names"), # all the other values don't make any difference when predicting
backup_dir ="/home/pjreddie/backup/",
setname = "coco"
)
return Predictor(training_ctx = yolov3_training_ctx, weight_file = get_yolov3_weights_file(), config_file = file_yolov3_cfg)
import numpy
import imutils
import importlib
import cv2
import logging
from valkka.api2 import parameterInitCheck, typeCheck
from valkka.mvision.base import Analyzer
from valkka.live.multiprocess import MessageObject
from valkka.mvision.multiprocess import test_process, test_with_file, MVisionBaseProcess
from valkka.live import style
from valkka.live.tools import getLogger, setLogger, getFreeGPU_MB
# if the following works, then darknet is available and the weights file has been downloaded ok
from darknet.api2.constant import get_yolov2_weights_file, get_yolov3_weights_file, get_yolov3_tiny_weights_file
fname = get_yolov3_weights_file()
from valkka.live.version import MIN_DARKNET_VERSION_MAJOR, MIN_DARKNET_VERSION_MINOR, MIN_DARKNET_VERSION_PATCH
from darknet.core import VERSION_MAJOR, VERSION_MINOR, VERSION_PATCH
assert(VERSION_MAJOR >= MIN_DARKNET_VERSION_MAJOR)
assert(VERSION_MINOR >= MIN_DARKNET_VERSION_MINOR)
assert(VERSION_PATCH >= MIN_DARKNET_VERSION_PATCH)
class YoloV3Analyzer(Analyzer):
"""The celebrated Yolo v3 object detector
"""
parameter_defs = {
"verbose": (bool, False), # :param verbose: Verbose output or not? Default: False.
"debug": (bool, False)