mirror of
https://github.com/pjreddie/darknet.git
synced 2023-08-10 21:13:14 +03:00
Added scripts
This commit is contained in:
AlexeyAB
66
scripts/install_OpenCV4.sh
Normal file
66
scripts/install_OpenCV4.sh
Normal file
@ -0,0 +1,66 @@
|
||||
#!/bin/bash
|
||||
|
||||
#based on https://devtalk.nvidia.com/default/topic/1042035/installing-opencv4-on-xavier/ & https://github.com/markste-in/OpenCV4XAVIER/blob/master/buildOpenCV4.sh
|
||||
|
||||
# Compute Capabilities can be found here https://developer.nvidia.com/cuda-gpus#compute
|
||||
ARCH_BIN=7.2 # AGX Xavier
|
||||
#ARCH_BIN=6.2 # Tx2
|
||||
|
||||
cd ~/Downloads
|
||||
sudo apt-get install -y build-essential \
|
||||
unzip \
|
||||
pkg-config \
|
||||
libjpeg-dev \
|
||||
libpng-dev \
|
||||
libtiff-dev \
|
||||
libavcodec-dev \
|
||||
libavformat-dev \
|
||||
libswscale-dev \
|
||||
libv4l-dev \
|
||||
libxvidcore-dev \
|
||||
libx264-dev \
|
||||
libgtk-3-dev \
|
||||
libatlas-base-dev \
|
||||
gfortran \
|
||||
python3-dev \
|
||||
python3-venv \
|
||||
libgstreamer1.0-dev \
|
||||
libgstreamer-plugins-base1.0-dev \
|
||||
libdc1394-22-dev \
|
||||
libavresample-dev
|
||||
|
||||
git clone https://github.com/opencv/opencv.git
|
||||
git clone https://github.com/opencv/opencv_contrib.git
|
||||
|
||||
python3 -m venv opencv4
|
||||
source opencv4/bin/activate
|
||||
pip install wheel
|
||||
pip install numpy
|
||||
|
||||
cd opencv && mkdir build && cd build
|
||||
|
||||
cmake -D CMAKE_BUILD_TYPE=RELEASE \
|
||||
-D CMAKE_INSTALL_PREFIX=/usr/local \
|
||||
-D INSTALL_PYTHON_EXAMPLES=ON \
|
||||
-D INSTALL_C_EXAMPLES=OFF \
|
||||
-D OPENCV_EXTRA_MODULES_PATH='~/Downloads/opencv_contrib/modules' \
|
||||
-D PYTHON_EXECUTABLE='~/Downloads/opencv4/bin/python' \
|
||||
-D BUILD_EXAMPLES=ON \
|
||||
-D WITH_CUDA=ON \
|
||||
-D CUDA_ARCH_BIN=${ARCH_BIN} \
|
||||
-D CUDA_ARCH_PTX="" \
|
||||
-D ENABLE_FAST_MATH=ON \
|
||||
-D CUDA_FAST_MATH=ON \
|
||||
-D WITH_CUBLAS=ON \
|
||||
-D WITH_LIBV4L=ON \
|
||||
-D WITH_GSTREAMER=ON \
|
||||
-D WITH_GSTREAMER_0_10=OFF \
|
||||
-D WITH_TBB=ON \
|
||||
../
|
||||
|
||||
make -j4
|
||||
sudo make install
|
||||
sudo ldconfig
|
||||
|
||||
cd ~/Downloads/opencv4/lib/python3.6/site-packages
|
||||
ln -s /usr/local/lib/python3.6/site-packages/cv2.cpython-36m-aarch64-linux-gnu.so cv2.so
|
||||
141
scripts/kitti2yolo.py
Normal file
141
scripts/kitti2yolo.py
Normal file
@ -0,0 +1,141 @@
|
||||
#!/usr/bin/env python
|
||||
from __future__ import print_function
|
||||
from __future__ import division
|
||||
#
|
||||
# This is a utility for converting ground truth data from the kitti format
|
||||
# to the YOLO format.
|
||||
#
|
||||
#
|
||||
#
|
||||
# YOLO FORMAT
|
||||
# .txt for each .jpg - in the same directory and with the same name
|
||||
# <object-class> <x> <y> <width> <height>
|
||||
#
|
||||
# Where:
|
||||
#
|
||||
# <object-class> - integer number of object from 0 to (classes-1)
|
||||
# <x> <y> <width> <height> - floats relative to image width/height 0.0 to 1.0
|
||||
# eg. <x> = <absolute_x> / <image_width>
|
||||
# Note: <x> <y> - are center of rectangle (not top-left corner)
|
||||
#
|
||||
# For example for img1.jpg you will be created img1.txt containing:
|
||||
#
|
||||
# 1 0.716797 0.395833 0.216406 0.147222
|
||||
# 0 0.687109 0.379167 0.255469 0.158333
|
||||
# 1 0.420312 0.395833 0.140625 0.166667
|
||||
#
|
||||
# KITTI FORMAT
|
||||
#
|
||||
# All images as .png in a separate folder to the .txt labels of the same name
|
||||
# One label line is as follows:
|
||||
#
|
||||
# 1 type Describes the type of object: Car, Van, Truck,
|
||||
# Pedestrian, Person_sitting, Cyclist, Tram,
|
||||
# Misc or DontCare
|
||||
# 1 truncated Float from 0 (non-truncated) to 1 (truncated), where
|
||||
# truncated refers to the object leaving image boundaries
|
||||
# 1 occluded Integer (0,1,2,3) indicating occlusion state:
|
||||
# 0 = fully visible, 1 = partly occluded
|
||||
# 2 = largely occluded, 3 = unknown
|
||||
# 1 alpha Observation angle of object, ranging [-pi..pi]
|
||||
# 4 bbox 2D bounding box of object in the image (0-based index):
|
||||
# contains left, top, right, bottom pixel coordinates
|
||||
# 3 dimensions 3D object dimensions: height, width, length (in meters)
|
||||
# 3 location 3D object location x,y,z in camera coordinates (in meters)
|
||||
# 1 rotation_y Rotation ry around Y-axis in camera coordinates [-pi..pi]
|
||||
# 1 score Only for results: Float, indicating confidence in
|
||||
# detection, needed for p/r curves, higher is better.
|
||||
#
|
||||
# Car 0.0 0 -1.5 57.0 17.3 614.1 200.12 1.65 1.67 3.64 -0.65 1.71 46.70 -1.59
|
||||
# Cyclist 0.0 0 -2.46 665.45 160.00 717.9 217.9 1.7 0.4 1.6 2.4 1.3 22.1 -2.35
|
||||
# Pedestrian 0.00 2 0.2 42.1 17.6 433.1 24.0 1.6 0.38 0.30 -5.8 1.6 23.1 -0.03
|
||||
# DontCare -1 -1 -10 650.19 175.02 668.98 210.48 -1 -1 -1 -1000 -1000 -1000 -10
|
||||
|
||||
# core imports
|
||||
import argparse
|
||||
import sys
|
||||
import os
|
||||
import shutil
|
||||
import cv2
|
||||
|
||||
|
||||
kitti2yolotype_dict = {'Car': '0',
|
||||
'Van': '0',
|
||||
'Pedestrian': '1',
|
||||
'Person_sitting': '1',
|
||||
'Cyclist': '2',
|
||||
'Truck': '3',
|
||||
'Tram': '6',
|
||||
'Misc': '6',
|
||||
'DontCare': '6'}
|
||||
|
||||
|
||||
def kitti2yolo(kitti_label, img_height, img_width):
|
||||
|
||||
kitti_label_arr = kitti_label.split(' ')
|
||||
x1 = float(kitti_label_arr[4])
|
||||
y1 = float(kitti_label_arr[5])
|
||||
x2 = float(kitti_label_arr[6])
|
||||
y2 = float(kitti_label_arr[7])
|
||||
|
||||
bb_width = x2 - x1
|
||||
bb_height = y2 - y1
|
||||
yolo_x = (x1 + 0.5*bb_width) / img_width
|
||||
yolo_y = (y1 + 0.5*bb_height) / img_height
|
||||
yolo_bb_width = bb_width / img_width
|
||||
yolo_bb_height = bb_height / img_height
|
||||
yolo_label = kitti2yolotype_dict[kitti_label_arr[0]]
|
||||
|
||||
return (yolo_label + ' '
|
||||
+ str(yolo_x) + ' '
|
||||
+ str(yolo_y) + ' '
|
||||
+ str(yolo_bb_width) + ' '
|
||||
+ str(yolo_bb_height))
|
||||
|
||||
|
||||
def main(args):
|
||||
|
||||
# parse command line arguments
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--kitti",
|
||||
help="path to kitti-format images and labels, images\
|
||||
should be under images_path/images and labels should\
|
||||
be under images_path/labels")
|
||||
parser.add_argument("--yolo",
|
||||
help="path to output yolo-ready training data")
|
||||
# kitti paths
|
||||
args = parser.parse_args()
|
||||
root_path = args.kitti
|
||||
yolo_path = args.yolo
|
||||
if root_path is None:
|
||||
root_path = os.getcwd()
|
||||
if (root_path[-1] != os.sep):
|
||||
root_path += os.sep
|
||||
kitti_images_path = root_path + 'image_2' + os.sep
|
||||
kitti_labels_path = root_path + 'label_2' + os.sep
|
||||
|
||||
# yolo paths
|
||||
if yolo_path is None:
|
||||
yolo_path = root_path + 'yolo_labels' + os.sep
|
||||
|
||||
if not os.path.exists(yolo_path):
|
||||
os.makedirs(yolo_path)
|
||||
|
||||
# load each kitti label, convert to yolo and save
|
||||
for labelfilename in os.listdir(kitti_labels_path):
|
||||
yolo_labels = []
|
||||
with open(kitti_labels_path + labelfilename, 'r') as kittilabelfile:
|
||||
cvimage = cv2.imread(kitti_images_path
|
||||
+ labelfilename.split('.txt')[0] + '.png')
|
||||
height, width, frame_depth = cvimage.shape
|
||||
for kitti_label in kittilabelfile:
|
||||
yolo_labels.append(kitti2yolo(kitti_label,
|
||||
img_height=height,
|
||||
img_width=width))
|
||||
with open(yolo_path + labelfilename, 'w+') as yololabelfile:
|
||||
for label in yolo_labels:
|
||||
yololabelfile.write(label + '\n')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main(sys.argv)
|
||||
Reference in New Issue
Block a user