## This program is for DAC HDC contest ###### ## 2017/11/22 ## [email protected] ## University of Notre Dame import procfunc import math import numpy as np import time #### !!!! you can import any package needed for your program ###### if __name__ == "__main__": teamName = 'USL-TAMU' DAC = './test2' [imgDir, resultDir, timeDir, xmlDir, myXmlDir, allTimeFile] = procfunc.setupDir(DAC, teamName) [allImageName, imageNum] = procfunc.getImageNames(imgDir) ### process all the images in batch batchNumDiskToDram = 128 ## the # of images read from disk to DRAM in one time batchNumDramToGPU = 16 ## the # of images read from DRAM to GPU in one time for batch processing on the GPU imageReadTime = math.ceil(float(imageNum)/float(batchNumDiskToDram)) imageProcTimeEachRead = math.ceil(float(batchNumDiskToDram)/float(batchNumDramToGPU)) resultRectangle = np.zeros((imageNum, 4)) ## store all the results about tracking accuracy resultRunTime = 0 if __name__=="__main__": for i in range(int(imageReadTime)): ImageDramBatch = procfunc.readImagesBatch(imgDir,allImageName, imageNum, i, batchNumDiskToDram) for j in range(int(imageProcTimeEachRead)): start = j*batchNumDramToGPU
from __future__ import print_function
import os
import math
import time
# third-party libraries
import numpy as np
# local packages
import procfunc
# set root path of the detector
detector_root = '..'
if __name__ == "__main__":
DAC = os.path.join(detector_root, 'data')
[imgDir, resultDir, timeDir, xmlDir, myXmlDir,
allTimeFile] = procfunc.setupDir(DAC)
# load all the images paths
[allImageName, imageNum] = procfunc.getImageNames(imgDir)
img_path_lst = [os.path.join(imgDir, imgName) for imgName in allImageName]
##############################################
############### detection part ###############
##############################################
# initialize
cfg_path = os.path.join(detector_root, 'cfg', 'valid.cfg')
model_path = os.path.join(detector_root, 'model',
'yolo_tiny_dacsdc_best.weights')
res_bboxes = np.zeros((imageNum, 4), dtype=int)
# inference
time_start = time.time()
## Folder structure:
## $DAC$|
## |images (all the test images are stored in this folder)
## |results-$teamName$|
## |time
## |xml
## !!!! Please specify your team name here
teamName = 'RandomSearch'
## !!!! please specify the dir here, and please put all the images for test in the folder "images".
## Important! You can specify the folder in your local test. But for the sumission, DAC folder is fixed as follows
#DAC = '/home/DACSDC_GPU' ## uncomment this line when submitting your code
DAC = '/home/nvidia/DAC/DAC18-Contest-GPU-v1'
# DAC = 'D:/DAC-dataset/test'
[imgDir, resultDir, timeDir, xmlDir, myXmlDir,
allTimeFile] = procfunc.setupDir(DAC, teamName)
############### processing for object detection and tracking ###########################################################
### load all the images names
[allImageName, imageNum] = procfunc.getImageNames(imgDir)
### process all the images in batch
batchNumDiskToDram = 5 ## the # of images read from disk to DRAM in one time
batchNumDramToGPU = 3 ## the # of images read from DRAM to GPU in one time for batch processing on the GPU
imageReadTime = int(math.ceil(imageNum / batchNumDiskToDram))
imageProcTimeEachRead = int(
math.ceil(batchNumDiskToDram / batchNumDramToGPU))
resultRectangle = np.zeros(
(imageNum, 4)) ## store all the results about tracking accuracy
time_start = time.time()