update makefile

Issue 15

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,26 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: "[BUG]"
+labels: bug, help wanted
+assignees: LdDl
+
+---
+
+**Describe the bug**
+A clear and concise description of what the bug is.
+
+**To Reproduce**
+Steps to reproduce the behavior
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Expected behavior**
+A clear and concise description of what you expected to happen.
+
+**Describe the solution you'd like and provide pseudocode examples if you can**
+A clear and concise description of what you want to happen.
+
+**Additional context**
+Add any other context about the problem here.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: "[FEATURE REQUEST]"
+labels: enhancement
+assignees: LdDl
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+A clear and concise description of what the problem is.
+
+**Describe the solution you'd like and provide pseudocode examples if you can**
+A clear and concise description of what you want to happen.
+
+**Describe alternatives you've considered and provide pseudocode examples if you can**
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+Add any other context or screenshots about the feature request here.

.gitignore

@@ -1,5 +1,4 @@
 example/main
-example/sample.jpg
 example/coco.names
 example/yolov3.cfg
 example/yolov3.weights

Makefile

@@ -0,0 +1,103 @@
+.ONESHELL:
+.PHONY: prepare_cuda prepare_cudnn download_darknet build_darknet build_darknet_gpu clean clean_cuda clean_cudnn
+
+# Latest battletested AlexeyAB version of Darknet commit
+LATEST_COMMIT?=f056fc3b6a11528fa0522a468eca1e909b7004b7
+
+# Temporary folder for building Darknet
+TMP_DIR?=/tmp/
+
+# Manage cuda version
+CUDA_VERSION = 10.2
+CUDNN_VERSION = 7.6.5
+CUDNN_FULL_VERSION = 7.6.5.32
+OS_NAME_LOW_CASE = ubuntu
+OS_VERSION_CONCATENATED = 1804
+OS_ARCH = x86_64
+OS_ALTER_ARCH = linux-x64
+OS_FULLNAME = $(OS_NAME_LOW_CASE)$(OS_VERSION_CONCATENATED)
+# I guess *.pub is static for most of systems
+PUBNAME = 7fa2af80
+
+# Install CUDA
+prepare_cuda:
+	sudo apt-get install linux-headers-$(uname -r)
+	rm -rf $(TMP_DIR)install_cuda
+	mkdir $(TMP_DIR)install_cuda
+	wget -P $(TMP_DIR)install_cuda https://developer.download.nvidia.com/compute/cuda/repos/$(OS_FULLNAME)/$(OS_ARCH)/cuda-$(OS_FULLNAME).pin
+	cd $(TMP_DIR)install_cuda
+	sudo mv cuda-$(OS_FULLNAME).pin /etc/apt/preferences.d/cuda-repository-pin-600
+	sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/$(OS_FULLNAME)/$(OS_ARCH)/$(PUBNAME).pub
+	sudo add-apt-repository "deb http://developer.download.nvidia.com/compute/cuda/repos/$(OS_FULLNAME)/$(OS_ARCH)/ /"
+	sudo apt-get update
+	sudo apt-get -y install cuda-$(subst .,-,$(CUDA_VERSION))
+	cd -
+
+# Install cuDNN
+# Notice: this valid instruction for cuDNN version from v7.2.1 up to 8.1.0.77
+prepare_cudnn:
+	rm -rf $(TMP_DIR)install_cudnn
+	mkdir $(TMP_DIR)install_cudnn
+	wget -P $(TMP_DIR)install_cudnn https://developer.download.nvidia.com/compute/redist/cudnn/v${CUDNN_VERSION}/cudnn-${CUDA_VERSION}-${OS_ALTER_ARCH}-v${CUDNN_FULL_VERSION}.tgz
+	cd $(TMP_DIR)install_cudnn
+	tar -xzvf cudnn-${CUDA_VERSION}-${OS_ALTER_ARCH}-v${CUDNN_FULL_VERSION}.tgz
+	sudo cp cuda/include/cudnn*.h /usr/local/cuda/include 
+	sudo cp -P cuda/lib64/libcudnn* /usr/local/cuda/lib64 
+	sudo chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn*
+	cd -
+
+# Download AlexeyAB version of Darknet
+download_darknet:
+	rm -rf $(TMP_DIR)install_darknet
+	mkdir $(TMP_DIR)install_darknet
+	git clone https://github.com/AlexeyAB/darknet.git $(TMP_DIR)install_darknet
+	cd $(TMP_DIR)install_darknet
+	git checkout $(LATEST_COMMIT)
+	cd -
+
+# Build AlexeyAB version of Darknet for usage with CPU only.
+build_darknet:
+	cd $(TMP_DIR)install_darknet
+	sed -i -e 's/GPU=1/GPU=0/g' Makefile
+	sed -i -e 's/CUDNN=1/CUDNN=0/g' Makefile
+	sed -i -e 's/LIBSO=0/LIBSO=1/g' Makefile
+	$(MAKE) -j $(shell nproc --all)
+	$(MAKE) preinstall
+	cd -
+
+# Build AlexeyAB version of Darknet for usage with both CPU and GPU (CUDA by NVIDIA).
+build_darknet_gpu:
+	cd $(TMP_DIR)install_darknet
+	sed -i -e 's/GPU=0/GPU=1/g' Makefile
+	sed -i -e 's/CUDNN=0/CUDNN=1/g' Makefile
+	sed -i -e 's/LIBSO=0/LIBSO=1/g' Makefile
+	$(MAKE) -j $(shell nproc --all)
+	$(MAKE) preinstall
+	cd -
+
+# Install system wide.
+sudo_install:
+	cd $(TMP_DIR)install_darknet
+	sudo cp libdarknet.so /usr/lib/libdarknet.so
+	sudo cp include/darknet.h /usr/include/darknet.h
+	sudo ldconfig
+	cd -
+
+# Cleanup temporary files for building process
+clean:
+	rm -rf $(TMP_DIR)install_darknet
+
+clean_cuda:
+	rm -rf $(TMP_DIR)install_cuda
+
+clean_cudnn:
+	rm -rf $(TMP_DIR)install_cudnn
+
+# Do every step for CPU-based only build.
+install_darknet: download_darknet build_darknet sudo_install clean
+
+# Do every step for both CPU and GPU-based build.
+install_darknet_gpu: download_darknet build_darknet_gpu sudo_install clean
+
+# Do every step for both CPU and GPU-based build if you haven't installed CUDA.
+install_darknet_gpu_cuda: prepare_cuda prepare_cudnn download_darknet build_darknet_gpu sudo_install clean clean_cuda clean_cudnn

README.md

@@ -3,7 +3,6 @@
 [![Go Report Card](https://goreportcard.com/badge/github.com/LdDl/go-darknet)](https://goreportcard.com/report/github.com/LdDl/go-darknet)
 [![GitHub tag](https://img.shields.io/github/tag/LdDl/go-darknet.svg)](https://github.com/LdDl/go-darknet/releases)
 
-
 # go-darknet: Go bindings for Darknet (Yolo V4, Yolo V3)
 ### go-darknet is a Go package, which uses Cgo to enable Go applications to use YOLO V4/V3 in [Darknet].
 
@@ -15,37 +14,48 @@
 
 ## Table of Contents
 
+- [Why](#why)
 - [Requirements](#requirements)
 - [Installation](#installation)
 - [Usage](#usage)
 - [Documentation](#documentation)
 - [License](#license)
 
+## Why
+**Why does this repository exist?**
+
+Because this repository https://github.com/gyonluks/go-darknet is no longer maintained.
+
+**What is purpose of this bindings when you can have [GoCV](https://github.com/hybridgroup/gocv#gocv) (bindings to OpenCV) and it handle Darnet YOLO perfectly?**
+
+Well, you don't need bunch of OpenCV dependencies and OpenCV itself sometimes.
+
+Example of such project here: https://github.com/LdDl/license_plate_recognition#license-plate-recognition-with-go-darknet---- .
+
+
 ## Requirements
 
-For proper codebase please use fork of [darknet](https://github.com/AlexeyAB/darknet). Latest commit I've tested [here](https://github.com/AlexeyAB/darknet/commit/9dc897d2c77d5ef43a6b237b717437375765b527)
+You need to install fork of [darknet](https://github.com/AlexeyAB/darknet). Latest commit I've tested is [here](https://github.com/AlexeyAB/darknet/commit/d65909fbea471d06e52a2e4a41132380dc2edaa6)
 
-In order to use go-darknet, `libdarknet.so` should be available in one of
-the following locations:
+Use provided [Makefile](Makefile).
 
-* /usr/lib
-* /usr/local/lib
+* For CPU-based instalattion:
+    ```shell
+    make install_darknet
+    ```
+* For both CPU and GPU-based instalattion if you HAVE CUDA installed:
+    ```shell
+    make install_darknet_gpu
+    ```
+    Note: I've tested CUDA [10.2](https://developer.nvidia.com/cuda-10.2-download-archive) and cuDNN is [7.6.5](https://developer.nvidia.com/rdp/cudnn-archive#a-collapse765-102))
 
-Also, [darknet.h] should be available in one of the following locations:
+* For both CPU and GPU-based instalattion if you HAVE NOT CUDA installed:
+    ```shell
+    make install_darknet_gpu_cuda
+    ```
+    Note: There is some struggle in Makefile for cuDNN, but I hope it works in Ubuntu atleast. Do not forget provide proper CUDA and cuDNN versions.
 
-* /usr/include
-* /usr/local/include
 
-To achieve it, after Darknet compilation (via make) execute following command:
-```shell
-# Copy *.so to /usr/lib + /usr/include (or /usr/local/lib + /usr/local/include)
-sudo cp libdarknet.so /usr/lib/libdarknet.so && sudo cp include/darknet.h /usr/include/darknet.h
-# sudo cp libdarknet.so /usr/local/lib/libdarknet.so && sudo cp include/darknet.h /usr/local/include/darknet.h
-```
-Note: do not forget to set LIBSO=1 in Makefile before executing 'make':
-```Makefile
-LIBSO=1
-```
 ## Installation
 
 ```shell
@@ -58,87 +68,88 @@ Example Go program is provided in the [example] directory. Please refer to the c
 
 Building and running program:
 
-Navigate to [example] folder
-```shell
-cd $GOPATH/github.com/LdDl/go-darknet/example
-```
+* Navigate to [example] folder
+    ```shell
+    cd $GOPATH/github.com/LdDl/go-darknet/example/base_example
+    ```
 
-Download dataset (sample of image, coco.names, yolov4.cfg (or v3), yolov4.weights(or v3)).
-```shell
-#for yolo v4
-./download_data.sh
-#for yolo v3
-./download_data_v3.sh
-```
-Note: you don't need *coco.data* file anymore, because sh-script above does insert *coco.names* into 'names' field in *yolov4.cfg* file (so AlexeyAB's fork can deal with it properly)
-So last rows in yolov4.cfg file will look like:
-```bash
-......
-[yolo]
-.....
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
+* Download dataset (sample of image, coco.names, yolov4.cfg (or v3), yolov4.weights(or v3)).
+    ```shell
+    #for yolo v4
+    ./download_data.sh
+    #for yolo v3
+    ./download_data_v3.sh
+    ```
+* Note: you don't need *coco.data* file anymore, because sh-script above does insert *coco.names* into 'names' field in *yolov4.cfg* file (so AlexeyAB's fork can deal with it properly)
+    So last rows in yolov4.cfg file will look like:
+    ```bash
+    ......
+    [yolo]
+    .....
+    iou_loss=ciou
+    nms_kind=greedynms
+    beta_nms=0.6
 
-names = coco.names # this is path to coco.names file
-```
-Also do not forget change batch and subdivisions sizes from:
-```shell
-batch=64
-subdivisions=8
-```
-to
-```shell
-batch=1
-subdivisions=1
-```
-It will reduce amount of VRAM used for detector test.
+    names = coco.names # this is path to coco.names file
+    ```
+
+* Also do not forget change batch and subdivisions sizes from:
+    ```shell
+    batch=64
+    subdivisions=8
+    ```
+    to
+    ```shell
+    batch=1
+    subdivisions=1
+    ```
+    It will reduce amount of VRAM used for detector test.
 
 
-Build and run program
-Yolo V4:
-```shell
-go build main.go && ./main --configFile=yolov4.cfg --weightsFile=yolov4.weights --imageFile=sample.jpg
-```
+* Build and run program
+    Yolo V4:
+    ```shell
+    go build main.go && ./main --configFile=yolov4.cfg --weightsFile=yolov4.weights --imageFile=sample.jpg
+    ```
 
-Output should be something like this:
-```shell
-traffic light (9): 73.5039% | start point: (238,73) | end point: (251, 106)
-truck (7): 96.6401% | start point: (95,79) | end point: (233, 287)
-truck (7): 96.4774% | start point: (662,158) | end point: (800, 321)
-truck (7): 96.1841% | start point: (0,77) | end point: (86, 333)
-truck (7): 46.8695% | start point: (434,173) | end point: (559, 216)
-car (2): 99.7370% | start point: (512,188) | end point: (741, 329)
-car (2): 99.2533% | start point: (260,191) | end point: (422, 322)
-car (2): 99.0333% | start point: (425,201) | end point: (547, 309)
-car (2): 83.3919% | start point: (386,210) | end point: (437, 287)
-car (2): 75.8621% | start point: (73,199) | end point: (102, 274)
-car (2): 39.1925% | start point: (386,206) | end point: (442, 240)
-bicycle (1): 76.3121% | start point: (189,298) | end point: (253, 402)
-person (0): 97.7213% | start point: (141,129) | end point: (283, 362)
-```
+    Output should be something like this:
+    ```shell
+    traffic light (9): 73.5039% | start point: (238,73) | end point: (251, 106)
+    truck (7): 96.6401% | start point: (95,79) | end point: (233, 287)
+    truck (7): 96.4774% | start point: (662,158) | end point: (800, 321)
+    truck (7): 96.1841% | start point: (0,77) | end point: (86, 333)
+    truck (7): 46.8695% | start point: (434,173) | end point: (559, 216)
+    car (2): 99.7370% | start point: (512,188) | end point: (741, 329)
+    car (2): 99.2533% | start point: (260,191) | end point: (422, 322)
+    car (2): 99.0333% | start point: (425,201) | end point: (547, 309)
+    car (2): 83.3919% | start point: (386,210) | end point: (437, 287)
+    car (2): 75.8621% | start point: (73,199) | end point: (102, 274)
+    car (2): 39.1925% | start point: (386,206) | end point: (442, 240)
+    bicycle (1): 76.3121% | start point: (189,298) | end point: (253, 402)
+    person (0): 97.7213% | start point: (141,129) | end point: (283, 362)
+    ```
 
-Yolo V3:
-```
-go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --imageFile=sample.jpg
-```
+    Yolo V3:
+    ```
+    go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --imageFile=sample.jpg
+    ```
 
-Output should be something like this:
-```shell
-truck (7): 49.5197% | start point: (0,136) | end point: (85, 311)
-car (2): 36.3747% | start point: (95,152) | end point: (186, 283)
-truck (7): 48.4384% | start point: (95,152) | end point: (186, 283)
-truck (7): 45.6590% | start point: (694,178) | end point: (798, 310)
-car (2): 76.8379% | start point: (1,145) | end point: (84, 324)
-truck (7): 25.5731% | start point: (107,89) | end point: (215, 263)
-car (2): 99.8783% | start point: (511,185) | end point: (748, 328)
-car (2): 99.8194% | start point: (261,189) | end point: (427, 322)
-car (2): 99.6408% | start point: (426,197) | end point: (539, 311)
-car (2): 74.5610% | start point: (692,186) | end point: (796, 316)
-car (2): 72.8053% | start point: (388,206) | end point: (437, 276)
-bicycle (1): 72.2932% | start point: (178,270) | end point: (268, 406)
-person (0): 97.3026% | start point: (143,135) | end point: (268, 343)
-```
+    Output should be something like this:
+    ```shell
+    truck (7): 49.5197% | start point: (0,136) | end point: (85, 311)
+    car (2): 36.3747% | start point: (95,152) | end point: (186, 283)
+    truck (7): 48.4384% | start point: (95,152) | end point: (186, 283)
+    truck (7): 45.6590% | start point: (694,178) | end point: (798, 310)
+    car (2): 76.8379% | start point: (1,145) | end point: (84, 324)
+    truck (7): 25.5731% | start point: (107,89) | end point: (215, 263)
+    car (2): 99.8783% | start point: (511,185) | end point: (748, 328)
+    car (2): 99.8194% | start point: (261,189) | end point: (427, 322)
+    car (2): 99.6408% | start point: (426,197) | end point: (539, 311)
+    car (2): 74.5610% | start point: (692,186) | end point: (796, 316)
+    car (2): 72.8053% | start point: (388,206) | end point: (437, 276)
+    bicycle (1): 72.2932% | start point: (178,270) | end point: (268, 406)
+    person (0): 97.3026% | start point: (143,135) | end point: (268, 343)
+    ```
 
 ## Documentation
 
@@ -154,5 +165,5 @@ go-darknet follows [Darknet]'s [license].
 [darknet.h]: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h
 [include/darknet.h]: https://github.com/AlexeyAB/darknet/blob/master/include/darknet.h
 [Makefile]: https://github.com/alexeyab/darknet/blob/master/Makefile
-[example]: /example
+[example]: /example/base_example
 [GoDoc]: https://godoc.org/github.com/LdDl/go-darknet

docker/.env

@@ -1 +0,0 @@
-NAMESPACE=darknet

docker/Dockerfile

@@ -1,40 +0,0 @@
-# Build phase
-FROM ubuntu:18.04 as builder
-
-ENV darknet_commit=a234a5022333c930de08f2470184ef4e0c68356e
-
-WORKDIR /root/build
-COPY Makefile.cpu .
-RUN apt-get -y update && \
-	apt-get -y install --no-install-recommends git build-essential ca-certificates && \
-	git clone https://github.com/AlexeyAB/darknet && \
-	cd darknet && \
-	git checkout $darknet_commit && \
-	cp -f /root/build/Makefile.cpu Makefile && \
-	make
-
-# Final Image
-FROM golang:1.14
-
-RUN apt-get -y update && \
-        apt-get -y install --no-install-recommends nano bash jq
-
-WORKDIR /root
-COPY --from=builder /root/build/darknet/darknet \
-	/root/build/darknet/libdarknet.so \
-	/root/build/darknet/include/darknet.h \
-	./staging/
-
-RUN mv staging/darknet /usr/local/bin && \
-	mv staging/darknet.h /usr/include && \
-	mv staging/libdarknet.so /usr/lib && \
-	rm -rf staging
-
-RUN go get -u github.com/LdDl/go-darknet \
-    && go get -u github.com/disintegration/imaging
-
-WORKDIR /darknet
-
-COPY download_data.sh .
-
-CMD ["/bin/bash"]

docker/Dockerfile.gpu

@@ -1,52 +0,0 @@
-# Build phase
-FROM nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04 as builder
-
-ENV darknet_commit=a234a5022333c930de08f2470184ef4e0c68356e
-
-WORKDIR /root/build
-COPY Makefile.gpu .
-RUN apt-get -y update && \
-	apt-get -y install git build-essential && \
-	git clone https://github.com/AlexeyAB/darknet.git && \
-	cd darknet && \
-	git checkout $darknet_commit && \
-        cp -f /root/build/Makefile.gpu Makefile && \
-	make
-
-# Final Image
-FROM nvidia/cuda:10.0-cudnn7-runtime-ubuntu18.04
-
-WORKDIR /root
-COPY --from=builder /root/build/darknet/darknet \
-	/root/build/darknet/libdarknet.so \
-	/root/build/darknet/include/darknet.h \
-	./staging/
-
-RUN mv staging/darknet /usr/local/bin && \
-	mv staging/darknet.h /usr/include && \
-	mv staging/libdarknet.so /usr/lib && \
-	rm -rf staging
-
-WORKDIR /tmp
-RUN cd /tmp \
-    && apt-get -y update \
-    && apt-get install -y wget git gcc \ 
-    && wget https://dl.google.com/go/go1.14.linux-amd64.tar.gz \
-    && tar -xvf go1.14.linux-amd64.tar.gz \
-    && mv go /usr/local
-
-RUN cp /usr/local/cuda-10.0/compat/* /usr/local/cuda-10.0/targets/x86_64-linux/lib/
-
-ENV GOROOT=/usr/local/go
-ENV GOPATH=/go
-ENV PATH=$GOPATH/bin:$GOROOT/bin:$PATH
-ENV LIBRARY_PATH=$LIBRARY_PATH:/usr/local/cuda-10.0/compat/
-
-RUN go get -u github.com/LdDl/go-darknet \
-    && go get -u github.com/disintegration/imaging
-
-WORKDIR /darknet
-
-COPY download_data.sh .
-
-CMD ["/bin/bash"]

docker/Makefile.cpu

@@ -1,185 +0,0 @@
-GPU=0
-CUDNN=0
-CUDNN_HALF=0
-OPENCV=0
-AVX=1
-OPENMP=1
-LIBSO=1
-ZED_CAMERA=0 # ZED SDK 3.0 and above
-ZED_CAMERA_v2_8=0 # ZED SDK 2.X
-
-# set GPU=1 and CUDNN=1 to speedup on GPU
-# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher
-# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)
-
-USE_CPP=0
-DEBUG=0
-
-ARCH= -gencode arch=compute_30,code=sm_30 \
-      -gencode arch=compute_35,code=sm_35 \
-      -gencode arch=compute_50,code=[sm_50,compute_50] \
-      -gencode arch=compute_52,code=[sm_52,compute_52] \
-	  -gencode arch=compute_61,code=[sm_61,compute_61]
-
-OS := $(shell uname)
-
-# Tesla V100
-# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]
-
-# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores
-# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]
-
-# Jetson XAVIER
-# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]
-
-# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
-# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
-
-# GP100/Tesla P100 - DGX-1
-# ARCH= -gencode arch=compute_60,code=sm_60
-
-# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:
-# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]
-
-# For Jetson Tx2 or Drive-PX2 uncomment:
-# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]
-
-
-# VPATH=./src/
-VPATH=./src/:./examples
-SLIB=libdarknet.so
-
-EXEC=darknet
-OBJDIR=./obj/
-
-ifeq ($(LIBSO), 1)
-LIBNAMESO=libdarknet.so
-APPNAMESO=uselib
-endif
-
-ifeq ($(USE_CPP), 1)
-CC=g++
-else
-CC=gcc
-endif
-
-CPP=g++ -std=c++11
-NVCC=nvcc
-OPTS=-Ofast
-LDFLAGS= -lm -pthread
-COMMON= -Iinclude/ -I3rdparty/stb/include
-CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC
-
-ifeq ($(DEBUG), 1)
-#OPTS= -O0 -g
-#OPTS= -Og -g
-COMMON+= -DDEBUG
-CFLAGS+= -DDEBUG
-else
-ifeq ($(AVX), 1)
-CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a
-endif
-endif
-
-CFLAGS+=$(OPTS)
-
-ifneq (,$(findstring MSYS_NT,$(OS)))
-LDFLAGS+=-lws2_32
-endif
-
-ifeq ($(OPENCV), 1)
-COMMON+= -DOPENCV
-CFLAGS+= -DOPENCV
-LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`
-COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`
-endif
-
-ifeq ($(OPENMP), 1)
-CFLAGS+= -fopenmp
-LDFLAGS+= -lgomp
-endif
-
-ifeq ($(GPU), 1)
-COMMON+= -DGPU -I/usr/local/cuda/include/
-CFLAGS+= -DGPU
-ifeq ($(OS),Darwin) #MAC
-LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand
-else
-LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
-endif
-endif
-
-ifeq ($(CUDNN), 1)
-COMMON+= -DCUDNN
-ifeq ($(OS),Darwin) #MAC
-CFLAGS+= -DCUDNN -I/usr/local/cuda/include
-LDFLAGS+= -L/usr/local/cuda/lib -lcudnn
-else
-CFLAGS+= -DCUDNN -I/usr/local/cudnn/include
-LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn
-endif
-endif
-
-ifeq ($(CUDNN_HALF), 1)
-COMMON+= -DCUDNN_HALF
-CFLAGS+= -DCUDNN_HALF
-ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]
-endif
-
-ifeq ($(ZED_CAMERA), 1)
-CFLAGS+= -DZED_STEREO -I/usr/local/zed/include
-ifeq ($(ZED_CAMERA_v2_8), 1)
-LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed
-#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 
-else
-LDFLAGS+= -L/usr/local/zed/lib -lsl_zed
-#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 
-endif
-endif
-
-OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o
-ifeq ($(GPU), 1) 
-LDFLAGS+= -lstdc++ 
-OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
-endif
-
-OBJS = $(addprefix $(OBJDIR), $(OBJ))
-DEPS = $(wildcard src/*.h) Makefile include/darknet.h
-
-all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)
-
-ifeq ($(LIBSO), 1)
-CFLAGS+= -fPIC
-
-$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp
-	$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)
-
-$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)
-endif
-
-$(EXEC): $(OBJS)
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)
-
-$(OBJDIR)%.o: %.c $(DEPS)
-	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
-
-$(OBJDIR)%.o: %.cpp $(DEPS)
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@
-
-$(OBJDIR)%.o: %.cu $(DEPS)
-	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
-
-$(OBJDIR):
-	mkdir -p $(OBJDIR)
-backup:
-	mkdir -p backup
-results:
-	mkdir -p results
-setchmod:
-	chmod +x *.sh
-
-.PHONY: clean
-
-clean:
-	rm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)

docker/Makefile.gpu

@@ -1,186 +0,0 @@
-
-GPU=1
-CUDNN=1
-CUDNN_HALF=0
-OPENCV=0
-AVX=0
-OPENMP=0
-LIBSO=1
-ZED_CAMERA=0 # ZED SDK 3.0 and above
-ZED_CAMERA_v2_8=0 # ZED SDK 2.X
-
-# set GPU=1 and CUDNN=1 to speedup on GPU
-# set CUDNN_HALF=1 to further speedup 3 x times (Mixed-precision on Tensor Cores) GPU: Volta, Xavier, Turing and higher
-# set AVX=1 and OPENMP=1 to speedup on CPU (if error occurs then set AVX=0)
-
-USE_CPP=0
-DEBUG=0
-
-ARCH= -gencode arch=compute_30,code=sm_30 \
-      -gencode arch=compute_35,code=sm_35 \
-      -gencode arch=compute_50,code=[sm_50,compute_50] \
-      -gencode arch=compute_52,code=[sm_52,compute_52] \
-	  -gencode arch=compute_61,code=[sm_61,compute_61]
-
-OS := $(shell uname)
-
-# Tesla V100
-# ARCH= -gencode arch=compute_70,code=[sm_70,compute_70]
-
-# GeForce RTX 2080 Ti, RTX 2080, RTX 2070, Quadro RTX 8000, Quadro RTX 6000, Quadro RTX 5000, Tesla T4, XNOR Tensor Cores
-# ARCH= -gencode arch=compute_75,code=[sm_75,compute_75]
-
-# Jetson XAVIER
-# ARCH= -gencode arch=compute_72,code=[sm_72,compute_72]
-
-# GTX 1080, GTX 1070, GTX 1060, GTX 1050, GTX 1030, Titan Xp, Tesla P40, Tesla P4
-# ARCH= -gencode arch=compute_61,code=sm_61 -gencode arch=compute_61,code=compute_61
-
-# GP100/Tesla P100 - DGX-1
-# ARCH= -gencode arch=compute_60,code=sm_60
-
-# For Jetson TX1, Tegra X1, DRIVE CX, DRIVE PX - uncomment:
-# ARCH= -gencode arch=compute_53,code=[sm_53,compute_53]
-
-# For Jetson Tx2 or Drive-PX2 uncomment:
-# ARCH= -gencode arch=compute_62,code=[sm_62,compute_62]
-
-
-# VPATH=./src/
-VPATH=./src/:./examples
-SLIB=libdarknet.so
-
-EXEC=darknet
-OBJDIR=./obj/
-
-ifeq ($(LIBSO), 1)
-LIBNAMESO=libdarknet.so
-APPNAMESO=uselib
-endif
-
-ifeq ($(USE_CPP), 1)
-CC=g++
-else
-CC=gcc
-endif
-
-CPP=g++ -std=c++11
-NVCC=nvcc
-OPTS=-Ofast
-LDFLAGS= -lm -pthread
-COMMON= -Iinclude/ -I3rdparty/stb/include
-CFLAGS=-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC
-
-ifeq ($(DEBUG), 1)
-#OPTS= -O0 -g
-#OPTS= -Og -g
-COMMON+= -DDEBUG
-CFLAGS+= -DDEBUG
-else
-ifeq ($(AVX), 1)
-CFLAGS+= -ffp-contract=fast -mavx -mavx2 -msse3 -msse4.1 -msse4.2 -msse4a
-endif
-endif
-
-CFLAGS+=$(OPTS)
-
-ifneq (,$(findstring MSYS_NT,$(OS)))
-LDFLAGS+=-lws2_32
-endif
-
-ifeq ($(OPENCV), 1)
-COMMON+= -DOPENCV
-CFLAGS+= -DOPENCV
-LDFLAGS+= `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv`
-COMMON+= `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv`
-endif
-
-ifeq ($(OPENMP), 1)
-CFLAGS+= -fopenmp
-LDFLAGS+= -lgomp
-endif
-
-ifeq ($(GPU), 1)
-COMMON+= -DGPU -I/usr/local/cuda/include/
-CFLAGS+= -DGPU
-ifeq ($(OS),Darwin) #MAC
-LDFLAGS+= -L/usr/local/cuda/lib -lcuda -lcudart -lcublas -lcurand
-else
-LDFLAGS+= -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand
-endif
-endif
-
-ifeq ($(CUDNN), 1)
-COMMON+= -DCUDNN
-ifeq ($(OS),Darwin) #MAC
-CFLAGS+= -DCUDNN -I/usr/local/cuda/include
-LDFLAGS+= -L/usr/local/cuda/lib -lcudnn
-else
-CFLAGS+= -DCUDNN -I/usr/local/cudnn/include
-LDFLAGS+= -L/usr/local/cudnn/lib64 -lcudnn
-endif
-endif
-
-ifeq ($(CUDNN_HALF), 1)
-COMMON+= -DCUDNN_HALF
-CFLAGS+= -DCUDNN_HALF
-ARCH+= -gencode arch=compute_70,code=[sm_70,compute_70]
-endif
-
-ifeq ($(ZED_CAMERA), 1)
-CFLAGS+= -DZED_STEREO -I/usr/local/zed/include
-ifeq ($(ZED_CAMERA_v2_8), 1)
-LDFLAGS+= -L/usr/local/zed/lib -lsl_core -lsl_input -lsl_zed
-#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 
-else
-LDFLAGS+= -L/usr/local/zed/lib -lsl_zed
-#-lstdc++ -D_GLIBCXX_USE_CXX11_ABI=0 
-endif
-endif
-
-OBJ=image_opencv.o http_stream.o gemm.o utils.o dark_cuda.o convolutional_layer.o list.o image.o activations.o im2col.o col2im.o blas.o crop_layer.o dropout_layer.o maxpool_layer.o softmax_layer.o data.o matrix.o network.o connected_layer.o cost_layer.o parser.o option_list.o darknet.o detection_layer.o captcha.o route_layer.o writing.o box.o nightmare.o normalization_layer.o avgpool_layer.o coco.o dice.o yolo.o detector.o layer.o compare.o classifier.o local_layer.o swag.o shortcut_layer.o activation_layer.o rnn_layer.o gru_layer.o rnn.o rnn_vid.o crnn_layer.o demo.o tag.o cifar.o go.o batchnorm_layer.o art.o region_layer.o reorg_layer.o reorg_old_layer.o super.o voxel.o tree.o yolo_layer.o gaussian_yolo_layer.o upsample_layer.o lstm_layer.o conv_lstm_layer.o scale_channels_layer.o sam_layer.o
-ifeq ($(GPU), 1) 
-LDFLAGS+= -lstdc++ 
-OBJ+=convolutional_kernels.o activation_kernels.o im2col_kernels.o col2im_kernels.o blas_kernels.o crop_layer_kernels.o dropout_layer_kernels.o maxpool_layer_kernels.o network_kernels.o avgpool_layer_kernels.o
-endif
-
-OBJS = $(addprefix $(OBJDIR), $(OBJ))
-DEPS = $(wildcard src/*.h) Makefile include/darknet.h
-
-all: $(OBJDIR) backup results setchmod $(EXEC) $(LIBNAMESO) $(APPNAMESO)
-
-ifeq ($(LIBSO), 1)
-CFLAGS+= -fPIC
-
-$(LIBNAMESO): $(OBJDIR) $(OBJS) include/yolo_v2_class.hpp src/yolo_v2_class.cpp
-	$(CPP) -shared -std=c++11 -fvisibility=hidden -DLIB_EXPORTS $(COMMON) $(CFLAGS) $(OBJS) src/yolo_v2_class.cpp -o $@ $(LDFLAGS)
-
-$(APPNAMESO): $(LIBNAMESO) include/yolo_v2_class.hpp src/yolo_console_dll.cpp
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -o $@ src/yolo_console_dll.cpp $(LDFLAGS) -L ./ -l:$(LIBNAMESO)
-endif
-
-$(EXEC): $(OBJS)
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) $^ -o $@ $(LDFLAGS)
-
-$(OBJDIR)%.o: %.c $(DEPS)
-	$(CC) $(COMMON) $(CFLAGS) -c $< -o $@
-
-$(OBJDIR)%.o: %.cpp $(DEPS)
-	$(CPP) -std=c++11 $(COMMON) $(CFLAGS) -c $< -o $@
-
-$(OBJDIR)%.o: %.cu $(DEPS)
-	$(NVCC) $(ARCH) $(COMMON) --compiler-options "$(CFLAGS)" -c $< -o $@
-
-$(OBJDIR):
-	mkdir -p $(OBJDIR)
-backup:
-	mkdir -p backup
-results:
-	mkdir -p results
-setchmod:
-	chmod +x *.sh
-
-.PHONY: clean
-
-clean:
-	rm -rf $(OBJS) $(EXEC) $(LIBNAMESO) $(APPNAMESO)

docker/docker-compose.yml

@@ -1,32 +0,0 @@
----
-version: '3.7'
-services:
-
-  sidekiq: &darknet_base 
-    container_name: ${NAMESPACE}-sidekiq
-    build:
-      context: .
-      dockerfile: Dockerfile
-    image: go-darknet:latest
-    working_dir: /darknet
-    volumes:
-    - darknet-data:/darknet/models
-    command: /darknet/download_data.sh
-
-  darknet:
-    <<: *darknet_base
-    container_name: ${NAMESPACE}-api
-    ports:
-    - "9003:9003"
-    restart: unless-stopped
-    depends_on:
-    - sidekiq
-    command: ["/bin/bash"]
-    # command: ["darknet-server"]
-
-volumes:
-  darknet-data:
-    driver_opts:
-      type: none
-      o: bind
-      device: ${PWD}/models

docker/download_data.sh

@@ -1,10 +0,0 @@
-#!/bin/sh
-
-# set -x
-# set -e
-
-wget -nc --output-document=sample.jpg https://cdn-images-1.medium.com/max/800/1*EYFejGUjvjPcc4PZTwoufw.jpeg
-wget -nc --output-document=./models/coco.names https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names
-wget -nc --output-document=./models/yolov3.cfg https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg
-sed -i -e "\$anames = coco.names" ./models/yolov3.cfg
-wget -nc --output-document=./models/yolov3.weights https://pjreddie.com/media/files/yolov3.weights

docker/models/.gitignore

@@ -1,2 +0,0 @@
-*
-!.gitignore

example/base_example/README.md

@@ -8,12 +8,12 @@ This is an example Go application which uses go-darknet.
 Navigate to example folder:
 
 ```shell
-cd $GOPATH/github.com/LdDl/go-darknet/example
+cd $GOPATH/github.com/LdDl/go-darknet/example/base_example
 ```
 
 Download dataset (sample of image, coco.names, yolov3.cfg, yolov3.weights).
 ```shell
-./download_data.sh
+./download_data_v3.sh
 ```
 Note: you don't need *coco.data* file anymore, because script below does insert *coco.names* into 'names' filed in *yolov3.cfg* file (so AlexeyAB's fork can deal with it properly)
 So last rows in yolov3.cfg file will look like:

example/base_example/main.go

@@ -9,7 +9,6 @@ import (
 	"log"
 	"math"
 	"os"
-
 	darknet "github.com/LdDl/go-darknet"
 	"github.com/disintegration/imaging"
 )
@@ -61,13 +60,13 @@ func main() {
 	if err != nil {
 		panic(err.Error())
 	}
-	defer imgDarknet.Close()
-
+	
 	dr, err := n.Detect(imgDarknet)
 	if err != nil {
 		printError(err)
 		return
 	}
+	imgDarknet.Close()
 
 	log.Println("Network-only time taken:", dr.NetworkOnlyTimeTaken)
 	log.Println("Overall time taken:", dr.OverallTimeTaken, len(dr.Detections))
@@ -92,6 +91,8 @@ func main() {
 			// }
 		}
 	}
+
+	n.Close()
 }
 
 func imageToBytes(img image.Image) ([]byte, error) {

example/rest_example/README.md

@@ -0,0 +1,221 @@
+# Example Go application using go-darknet and REST
+
+This is an example Go server application (in terms of REST) which uses go-darknet.
+
+## Run
+
+Navigate to example folder:
+
+```shell
+cd $GOPATH/github.com/LdDl/go-darknet/example/rest_example
+```
+
+Download dataset (sample of image, coco.names, yolov3.cfg, yolov3.weights).
+```shell
+./download_data_v3.sh
+```
+Note: you don't need *coco.data* file anymore, because script below does insert *coco.names* into 'names' filed in *yolov3.cfg* file (so AlexeyAB's fork can deal with it properly)
+So last rows in yolov3.cfg file will look like:
+```bash
+......
+[yolo]
+mask = 0,1,2
+anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
+classes=80
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+names = coco.names # this is path to coco.names file
+```
+
+Build and run program
+```
+go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --port 8090
+```
+
+After server started check if REST-requests works. We provide cURL-based example
+```shell
+curl -F 'image=@sample.jpg' 'http://localhost:8090/detect_objects'
+```
+
+Servers response should be something like this:
+```json
+{
+    "net_time": "43.269289ms",
+    "overall_time": "43.551604ms",
+    "num_detections": 44,
+    "detections": [
+        {
+            "class_id": 7,
+            "class_name": "truck",
+            "probability": 49.51231,
+            "start_point": {
+                "x": 0,
+                "y": 136
+            },
+            "end_point": {
+                "x": 85,
+                "y": 311
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 36.36933,
+            "start_point": {
+                "x": 95,
+                "y": 152
+            },
+            "end_point": {
+                "x": 186,
+                "y": 283
+            }
+        },
+        {
+            "class_id": 7,
+            "class_name": "truck",
+            "probability": 48.417683,
+            "start_point": {
+                "x": 95,
+                "y": 152
+            },
+            "end_point": {
+                "x": 186,
+                "y": 283
+            }
+        },
+        {
+            "class_id": 7,
+            "class_name": "truck",
+            "probability": 45.652023,
+            "start_point": {
+                "x": 694,
+                "y": 178
+            },
+            "end_point": {
+                "x": 798,
+                "y": 310
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 76.8402,
+            "start_point": {
+                "x": 1,
+                "y": 145
+            },
+            "end_point": {
+                "x": 84,
+                "y": 324
+            }
+        },
+        {
+            "class_id": 7,
+            "class_name": "truck",
+            "probability": 25.592052,
+            "start_point": {
+                "x": 107,
+                "y": 89
+            },
+            "end_point": {
+                "x": 215,
+                "y": 263
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 99.87823,
+            "start_point": {
+                "x": 511,
+                "y": 185
+            },
+            "end_point": {
+                "x": 748,
+                "y": 328
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 99.819336,
+            "start_point": {
+                "x": 261,
+                "y": 189
+            },
+            "end_point": {
+                "x": 427,
+                "y": 322
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 99.64055,
+            "start_point": {
+                "x": 426,
+                "y": 197
+            },
+            "end_point": {
+                "x": 539,
+                "y": 311
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 74.56263,
+            "start_point": {
+                "x": 692,
+                "y": 186
+            },
+            "end_point": {
+                "x": 796,
+                "y": 316
+            }
+        },
+        {
+            "class_id": 2,
+            "class_name": "car",
+            "probability": 72.79756,
+            "start_point": {
+                "x": 388,
+                "y": 206
+            },
+            "end_point": {
+                "x": 437,
+                "y": 276
+            }
+        },
+        {
+            "class_id": 1,
+            "class_name": "bicycle",
+            "probability": 72.27595,
+            "start_point": {
+                "x": 178,
+                "y": 270
+            },
+            "end_point": {
+                "x": 268,
+                "y": 406
+            }
+        },
+        {
+            "class_id": 0,
+            "class_name": "person",
+            "probability": 97.30075,
+            "start_point": {
+                "x": 143,
+                "y": 135
+            },
+            "end_point": {
+                "x": 268,
+                "y": 343
+            }
+        }
+    ]
+}
+```

example/rest_example/main.go

@@ -0,0 +1,140 @@
+package main
+
+import (
+	"bytes"
+	"encoding/json"
+	"flag"
+	"fmt"
+	"image"
+	_ "image/jpeg"
+	"io/ioutil"
+	"log"
+	"net/http"
+
+	"github.com/LdDl/go-darknet"
+)
+
+var configFile = flag.String("configFile", "",
+	"Path to network layer configuration file. Example: cfg/yolov3.cfg")
+var weightsFile = flag.String("weightsFile", "",
+	"Path to weights file. Example: yolov3.weights")
+var serverPort = flag.Int("port", 8090,
+	"Listening port")
+
+func main() {
+	flag.Parse()
+
+	if *configFile == "" || *weightsFile == "" {
+		flag.Usage()
+		return
+	}
+
+	n := darknet.YOLONetwork{
+		GPUDeviceIndex:           0,
+		NetworkConfigurationFile: *configFile,
+		WeightsFile:              *weightsFile,
+		Threshold:                .25,
+	}
+	if err := n.Init(); err != nil {
+		log.Println(err)
+		return
+	}
+	defer n.Close()
+
+	http.HandleFunc("/detect_objects", detectObjects(&n))
+	http.ListenAndServe(fmt.Sprintf(":%d", *serverPort), nil)
+}
+
+// DarknetResp Response
+type DarknetResp struct {
+	NetTime     string              `json:"net_time"`
+	OverallTime string              `json:"overall_time"`
+	Detections  []*DarknetDetection `json:"detections"`
+}
+
+// DarknetDetection Information about single detection
+type DarknetDetection struct {
+	ClassID     int           `json:"class_id"`
+	ClassName   string        `json:"class_name"`
+	Probability float32       `json:"probability"`
+	StartPoint  *DarknetPoint `json:"start_point"`
+	EndPoint    *DarknetPoint `json:"end_point"`
+}
+
+// DarknetPoint image.Image point
+type DarknetPoint struct {
+	X int `json:"x"`
+	Y int `json:"y"`
+}
+
+func detectObjects(n *darknet.YOLONetwork) func(w http.ResponseWriter, req *http.Request) {
+	return func(w http.ResponseWriter, req *http.Request) {
+		// Restrict file size up to 10mb
+		req.ParseMultipartForm(10 << 20)
+
+		file, _, err := req.FormFile("image")
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error reading FormFile: %s", err.Error()))
+			return
+		}
+		defer file.Close()
+
+		fileBytes, err := ioutil.ReadAll(file)
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error reading bytes: %s", err.Error()))
+			return
+		}
+
+		imgSrc, _, err := image.Decode(bytes.NewReader(fileBytes))
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error decoding bytes to image: %s", err.Error()))
+			return
+		}
+
+		imgDarknet, err := darknet.Image2Float32(imgSrc)
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error converting image.Image to darknet.DarknetImage: %s", err.Error()))
+			return
+		}
+		defer imgDarknet.Close()
+
+		dr, err := n.Detect(imgDarknet)
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error detecting objects: %s", err.Error()))
+			return
+		}
+
+		resp := DarknetResp{
+			NetTime:     fmt.Sprintf("%v", dr.NetworkOnlyTimeTaken),
+			OverallTime: fmt.Sprintf("%v", dr.OverallTimeTaken),
+			Detections:  []*DarknetDetection{},
+		}
+
+		for _, d := range dr.Detections {
+			for i := range d.ClassIDs {
+				bBox := d.BoundingBox
+				resp.Detections = append(resp.Detections, &DarknetDetection{
+					ClassID:     d.ClassIDs[i],
+					ClassName:   d.ClassNames[i],
+					Probability: d.Probabilities[i],
+					StartPoint: &DarknetPoint{
+						X: bBox.StartPoint.X,
+						Y: bBox.StartPoint.Y,
+					},
+					EndPoint: &DarknetPoint{
+						X: bBox.EndPoint.X,
+						Y: bBox.EndPoint.Y,
+					},
+				})
+			}
+		}
+
+		respBytes, err := json.Marshal(resp)
+		if err != nil {
+			fmt.Fprintf(w, fmt.Sprintf("Error encoding response: %s", err.Error()))
+			return
+		}
+
+		fmt.Fprintf(w, string(respBytes))
+	}
+}

network.c

@@ -14,7 +14,8 @@ struct network_box_result perform_network_detect(network *n, image *img, int cla
         sized = resize_image(*img, n->w, n->h);
     }
     struct network_box_result result = { NULL };
-    network_predict(*n, sized.data);
+    // mleak at network_predict(), get_network_boxes() and network_predict_ptr()?
+    network_predict_ptr(n, sized.data);
     int nboxes = 0;
     result.detections = get_network_boxes(n, img->w, img->h, thresh, hier_thresh, 0, 1, &result.detections_len, letter_box);
     if (nms) {

network.go

@@ -58,7 +58,7 @@ func (n *YOLONetwork) Close() error {
 	if n.cNet == nil {
 		return errNetworkNotInit
 	}
-	C.free_network(*n.cNet)
+	C.free_network_ptr(n.cNet)
 	n.cNet = nil
 	return nil
 }
@@ -80,4 +80,4 @@ func (n *YOLONetwork) Detect(img *DarknetImage) (*DetectionResult, error) {
 		OverallTimeTaken:     endTimeOverall.Sub(startTime),
 	}
 	return &out, nil
-}
+}