portable version of mmap

DarknetImage move float conversion into c code

.gitignore

@@ -1,10 +1,14 @@
-example/main
-example/sample.jpg
-example/coco.names
-example/yolov3.cfg
-example/yolov3.weights
-example/yolov4.cfg
-example/yolov4.weights
+cmd/examples/main
+cmd/examples/base_example/main
+cmd/examples/coco.names
+cmd/examples/yolov3.cfg
+cmd/examples/yolov3.weights
+cmd/examples/yolov4.cfg
+cmd/examples/yolov4.weights
+cmd/examples/yolov4-tiny.cfg
+cmd/examples/yolov4-tiny.weights
+cmd/examples/yolov7-tiny.cfg
+cmd/examples/yolov7-tiny.weights
 darknet.h
 *.so
 predictions.png

Makefile

@@ -1,14 +1,54 @@
 .ONESHELL:
-.PHONY: download build clean
+.PHONY: prepare_cuda prepare_cudnn download_darknet build_darknet build_darknet_gpu clean clean_cuda clean_cudnn sudo_install
 
 # Latest battletested AlexeyAB version of Darknet commit
-LATEST_COMMIT?=d65909fbea471d06e52a2e4a41132380dc2edaa6
+# LATEST_COMMIT?=f056fc3b6a11528fa0522a468eca1e909b7004b7
+LATEST_COMMIT?=9d40b619756be9521bc2ccd81808f502daaa3e9a
 
 # 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:
+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
@@ -17,7 +57,7 @@ download:
 	cd -
 
 # Build AlexeyAB version of Darknet for usage with CPU only.
-build:
+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
@@ -27,7 +67,7 @@ build:
 	cd -
 
 # Build AlexeyAB version of Darknet for usage with both CPU and GPU (CUDA by NVIDIA).
-build_gpu:
+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
@@ -48,8 +88,17 @@ sudo_install:
 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: download build sudo_install clean
+install_darknet: download_darknet build_darknet sudo_install clean
 
 # Do every step for both CPU and GPU-based build.
-install_gpu: download build_gpu sudo_install clean
+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

@@ -3,38 +3,59 @@
 [![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].
+# go-darknet: Go bindings for Darknet (Yolo V4, Yolo V7-tiny, Yolo V3)
+### go-darknet is a Go package, which uses Cgo to enable Go applications to use V4/V7-tiny/V3 in [Darknet].
 
 #### Since this repository https://github.com/gyonluks/go-darknet  is no longer maintained I decided to move on and make little different bindings for Darknet.
 #### This bindings aren't for [official implementation](https://github.com/pjreddie/darknet) but for [AlexeyAB's fork](https://github.com/AlexeyAB/darknet).
 
+#### Paper Yolo v7: https://arxiv.org/abs/2207.02696 (WARNING: Only 'tiny' variation works currently)
 #### Paper Yolo v4: https://arxiv.org/abs/2004.10934
 #### Paper Yolo v3: https://arxiv.org/abs/1804.02767
 
 ## 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 Darknet 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
 
-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)
+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/9d40b619756be9521bc2ccd81808f502daaa3e9a). It corresponds last official [YOLOv4 release](https://github.com/AlexeyAB/darknet/releases/tag/yolov4)
 
 Use provided [Makefile](Makefile).
 
 * For CPU-based instalattion:
     ```shell
-    make install
+    make install_darknet
     ```
-* For both CPU and GPU-based instalattion:
+* For both CPU and GPU-based instalattion if you HAVE CUDA installed:
     ```shell
-    make install_gpu
+    make install_darknet_gpu
     ```
-    Note: If you want to have GPU-acceleration before running command above install [CUDA](https://developer.nvidia.com/cuda-downloads) and [cuDNN](https://developer.nvidia.com/cudnn) (Latest CUDA version I've tested is [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))
+    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))
+
+* 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.
+
 
 ## Installation
 
@@ -44,19 +65,23 @@ go get github.com/LdDl/go-darknet
 
 ## Usage
 
-Example Go program is provided in the [example] directory. Please refer to the code on how to use this Go package.
+Example Go program is provided in the [examples] directory. Please refer to the code on how to use this Go package.
 
 Building and running program:
 
-* Navigate to [example] folder
+* Navigate to [examples] folder
     ```shell
-    cd $GOPATH/github.com/LdDl/go-darknet/example/base_example
+    cd ${YOUR PATH}/github.com/LdDl/go-darknet/cmd/examples
     ```
 
 * Download dataset (sample of image, coco.names, yolov4.cfg (or v3), yolov4.weights(or v3)).
     ```shell
     #for yolo v4
     ./download_data.sh
+    #for yolo v4 tiny
+    ./download_data_v4_tiny.sh
+    #for yolo v7 tiny
+    ./download_data_v7_tiny.sh
     #for yolo v3
     ./download_data_v3.sh
     ```
@@ -86,29 +111,71 @@ Building and running program:
     It will reduce amount of VRAM used for detector test.
 
 
-* Build and run program
-    Yolo V4:
+* Build and run example program
+    
+    Yolo v7 tiny:
     ```shell
-    go build main.go && ./main --configFile=yolov4.cfg --weightsFile=yolov4.weights --imageFile=sample.jpg
+    go build -o base_example/main base_example/main.go && ./base_example/main --configFile=yolov7-tiny.cfg --weightsFile=yolov7-tiny.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): 53.2890% | start point: (0,143) | end point: (89, 328)
+    truck (7): 42.1364% | start point: (685,182) | end point: (800, 318)
+    truck (7): 26.9703% | start point: (437,170) | end point: (560, 217)
+    car (2): 87.7818% | start point: (509,189) | end point: (742, 329)
+    car (2): 87.5633% | start point: (262,191) | end point: (423, 322)
+    car (2): 85.4743% | start point: (427,198) | end point: (549, 309)
+    car (2): 71.3772% | start point: (0,147) | end point: (87, 327)
+    car (2): 62.5698% | start point: (98,151) | end point: (197, 286)
+    car (2): 61.5811% | start point: (693,186) | end point: (799, 316)
+    car (2): 49.6343% | start point: (386,206) | end point: (441, 286)
+    car (2): 28.2012% | start point: (386,205) | end point: (440, 236)
+    bicycle (1): 71.9609% | start point: (179,294) | end point: (249, 405)
+    person (0): 85.4390% | start point: (146,130) | end point: (269, 351)
+    ```
+
+    Yolo v4:
+    ```shell
+    go build -o base_example/main base_example/main.go && ./base_example/main --configFile=yolov4.cfg --weightsFile=yolov4.weights --imageFile=sample.jpg
+    ```
+
+    Output should be something like this:
+    ```shell
+    traffic light (9): 73.5040% | 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)
+    truck (7): 46.8694% | 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): 99.2532% | 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): 83.3920% | 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 v4 tiny:
+    ```shell
+    go build -o base_example/main base_example/main.go && ./base_example/main --configFile=yolov4-tiny.cfg --weightsFile=yolov4-tiny.weights --imageFile=sample.jpg
+    ```
+
+    Output should be something like this:
+    ```shell
+    truck (7): 77.7936% | start point: (0,138) | end point: (90, 332)
+    truck (7): 55.9773% | start point: (696,174) | end point: (799, 314)
+    car (2): 53.1286% | start point: (696,184) | end point: (799, 319)
+    car (2): 98.0222% | start point: (262,189) | end point: (424, 330)
+    car (2): 97.8773% | start point: (430,190) | end point: (542, 313)
+    car (2): 81.4099% | start point: (510,190) | end point: (743, 325)
+    car (2): 43.3935% | start point: (391,207) | end point: (435, 299)
+    car (2): 37.4221% | start point: (386,206) | end point: (429, 239)
+    car (2): 32.0724% | start point: (109,196) | end point: (157, 289)
+    person (0): 73.0868% | start point: (154,132) | end point: (284, 382)
+    ```
+
     Yolo V3:
     ```
     go build main.go && ./main --configFile=yolov3.cfg --weightsFile=yolov3.weights --imageFile=sample.jpg
@@ -116,19 +183,19 @@ Building and running program:
 
     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)
+    truck (7): 49.5123% | start point: (0,136) | end point: (85, 311)
+    car (2): 36.3694% | start point: (95,152) | end point: (186, 283)
+    truck (7): 48.4177% | start point: (95,152) | end point: (186, 283)
+    truck (7): 45.6520% | start point: (694,178) | end point: (798, 310)
+    car (2): 76.8402% | start point: (1,145) | end point: (84, 324)
+    truck (7): 25.5920% | start point: (107,89) | end point: (215, 263)
+    car (2): 99.8782% | start point: (511,185) | end point: (748, 328)
+    car (2): 99.8193% | start point: (261,189) | end point: (427, 322)
+    car (2): 99.6405% | start point: (426,197) | end point: (539, 311)
+    car (2): 74.5627% | start point: (692,186) | end point: (796, 316)
+    car (2): 72.7975% | start point: (388,206) | end point: (437, 276)
+    bicycle (1): 72.2760% | start point: (178,270) | end point: (268, 406)
+    person (0): 97.3007% | start point: (143,135) | end point: (268, 343)
     ```
 
 ## Documentation
@@ -145,5 +212,5 @@ 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/base_example
+[examples]: cmd/examples/base_example
 [GoDoc]: https://godoc.org/github.com/LdDl/go-darknet

cmd/examples/base_example/main.go

@@ -11,6 +11,7 @@ import (
 	"os"
 
 	darknet "github.com/LdDl/go-darknet"
+
 	"github.com/disintegration/imaging"
 )
 
@@ -61,13 +62,13 @@ 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 +93,8 @@ func main() {
 			// }
 		}
 	}
+
+	n.Close()
 }
 
 func imageToBytes(img image.Image) ([]byte, error) {

cmd/examples/download_data_v4_tiny.sh

@@ -0,0 +1,5 @@
+wget --output-document=sample.jpg https://cdn-images-1.medium.com/max/800/1*EYFejGUjvjPcc4PZTwoufw.jpeg
+wget --output-document=coco.names https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names
+wget --output-document=yolov4-tiny.cfg https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg
+sed -i -e "\$anames = coco.names" yolov4-tiny.cfg
+wget --output-document=yolov4-tiny.weights https://github.com/AlexeyAB/darknet/releases/download/yolov4/yolov4-tiny.weights

cmd/examples/download_data_v7_tiny.sh

@@ -0,0 +1,5 @@
+wget --output-document=sample.jpg https://cdn-images-1.medium.com/max/800/1*EYFejGUjvjPcc4PZTwoufw.jpeg
+wget --output-document=coco.names https://raw.githubusercontent.com/AlexeyAB/darknet/master/data/coco.names
+wget --output-document=yolov7-tiny.cfg https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov7-tiny.cfg
+sed -i -e "\$anames = coco.names" yolov7-tiny.cfg
+wget --output-document=yolov7-tiny.weights https://github.com/AlexeyAB/darknet/releases/download/yolov4/yolov7-tiny.weights

cmd/examples/rest_example/main.go

@@ -11,7 +11,7 @@ import (
 	"log"
 	"net/http"
 
-	"github.com/LdDl/go-darknet"
+	darknet "github.com/LdDl/go-darknet"
 )
 
 var configFile = flag.String("configFile", "",

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

go.mod

@@ -0,0 +1,14 @@
+module github.com/LdDl/go-darknet
+
+go 1.17
+
+require (
+	github.com/disintegration/imaging v1.6.2
+	github.com/pkg/errors v0.9.1
+	golang.org/x/image v0.0.0-20211028202545-6944b10bf410
+)
+
+require (
+	github.com/edsrzf/mmap-go v1.1.0 // indirect
+	golang.org/x/sys v0.0.0-20211216021012-1d35b9e2eb4e // indirect
+)

go.sum

@@ -0,0 +1,14 @@
+github.com/disintegration/imaging v1.6.2 h1:w1LecBlG2Lnp8B3jk5zSuNqd7b4DXhcjwek1ei82L+c=
+github.com/disintegration/imaging v1.6.2/go.mod h1:44/5580QXChDfwIclfc/PCwrr44amcmDAg8hxG0Ewe4=
+github.com/edsrzf/mmap-go v1.1.0 h1:6EUwBLQ/Mcr1EYLE4Tn1VdW1A4ckqCQWZBw8Hr0kjpQ=
+github.com/edsrzf/mmap-go v1.1.0/go.mod h1:19H/e8pUPLicwkyNgOykDXkJ9F0MHE+Z52B8EIth78Q=
+github.com/pkg/errors v0.9.1 h1:FEBLx1zS214owpjy7qsBeixbURkuhQAwrK5UwLGTwt4=
+github.com/pkg/errors v0.9.1/go.mod h1:bwawxfHBFNV+L2hUp1rHADufV3IMtnDRdf1r5NINEl0=
+golang.org/x/image v0.0.0-20191009234506-e7c1f5e7dbb8/go.mod h1:FeLwcggjj3mMvU+oOTbSwawSJRM1uh48EjtB4UJZlP0=
+golang.org/x/image v0.0.0-20211028202545-6944b10bf410 h1:hTftEOvwiOq2+O8k2D5/Q7COC7k5Qcrgc2TFURJYnvQ=
+golang.org/x/image v0.0.0-20211028202545-6944b10bf410/go.mod h1:023OzeP/+EPmXeapQh35lcL3II3LrY8Ic+EFFKVhULM=
+golang.org/x/sys v0.0.0-20211216021012-1d35b9e2eb4e h1:fLOSk5Q00efkSvAm+4xcoXD+RRmLmmulPn5I3Y9F2EM=
+golang.org/x/sys v0.0.0-20211216021012-1d35b9e2eb4e/go.mod h1:oPkhp1MJrh7nUepCBck5+mAzfO9JrbApNNgaTdGDITg=
+golang.org/x/text v0.3.0/go.mod h1:NqM8EUOU14njkJ3fqMW+pc6Ldnwhi/IjpwHt7yyuwOQ=
+golang.org/x/text v0.3.6/go.mod h1:5Zoc/QRtKVWzQhOtBMvqHzDpF6irO9z98xDceosuGiQ=
+golang.org/x/tools v0.0.0-20180917221912-90fa682c2a6e/go.mod h1:n7NCudcB/nEzxVGmLbDWY5pfWTLqBcC2KZ6jyYvM4mQ=

image.c

@@ -11,3 +11,18 @@ void set_data_f32_val(float* data, int index, float value) {
     data[index] = value;
 }
 
+void to_float_and_fill_image(image* im, int w, int h, uint8_t* data) {
+    int x, y, idx_source;
+    int pixel_count = w * h;
+    int idx = 0;
+
+    for (y = 0; y < h; y++) {
+        for (x = 0; x < w; x++) {
+            idx_source = (y*w + x) * 4;
+            im->data[(pixel_count*0) + idx] = (float)data[idx_source] / 255;
+            im->data[(pixel_count*1) + idx] = (float)data[idx_source+1] / 255;
+            im->data[(pixel_count*2) + idx] = (float)data[idx_source+2] / 255;
+            idx++;
+        }
+    }
+}

image.go

@@ -14,61 +14,33 @@ import (
 type DarknetImage struct {
 	Width  int
 	Height int
-	ans    []float32
 	image  C.image
 }
 
 // Close and release resources.
 func (img *DarknetImage) Close() error {
 	C.free_image(img.image)
-	img.ans = nil
 	return nil
 }
 
-// https://stackoverflow.com/questions/33186783/get-a-pixel-array-from-from-golang-image-image/59747737#59747737
-func imgTofloat32(src image.Image) []float32 {
+func Image2Float32(src image.Image) (*DarknetImage, error) {
 	bounds := src.Bounds()
-	width, height := bounds.Max.X, bounds.Max.Y
-	srcRGBA := image.NewRGBA(src.Bounds())
-	draw.Copy(srcRGBA, image.Point{}, src, src.Bounds(), draw.Src, nil)
+	srcRGBA := image.NewRGBA(bounds)
+	draw.Copy(srcRGBA, image.Point{}, src, bounds, draw.Src, nil)
 
-	red := make([]float32, 0, width*height)
-	green := make([]float32, 0, width*height)
-	blue := make([]float32, 0, width*height)
-	for y := 0; y < height; y++ {
-		for x := 0; x < width; x++ {
-			idxSource := (y*width + x) * 4
-			pix := srcRGBA.Pix[idxSource : idxSource+4]
-			rpix, gpix, bpix := float32(pix[0])/257.0, float32(pix[1])/257.0, float32(pix[2])/257.0
-			red = append(red, rpix)
-			green = append(green, gpix)
-			blue = append(blue, bpix)
-		}
-	}
-	srcRGBA = nil
-
-	ans := make([]float32, len(red)+len(green)+len(blue))
-	copy(ans[:len(red)], red)
-	copy(ans[len(red):len(red)+len(green)], green)
-	copy(ans[len(red)+len(green):], blue)
-	red = nil
-	green = nil
-	blue = nil
-	return ans
+	return ImageRGBA2Float32(srcRGBA)
 }
 
 // Image2Float32 Returns []float32 representation of image.Image
-func Image2Float32(img image.Image) (*DarknetImage, error) {
-	// ans := imgTofloat32(img)
+func ImageRGBA2Float32(img *image.RGBA) (*DarknetImage, error) {
 	width := img.Bounds().Dx()
 	height := img.Bounds().Dy()
 	imgDarknet := &DarknetImage{
 		Width:  width,
 		Height: height,
-		ans:    imgTofloat32(img),
 		image:  C.make_image(C.int(width), C.int(height), 3),
 	}
-	C.fill_image_f32(&imgDarknet.image, C.int(width), C.int(height), 3, (*C.float)(unsafe.Pointer(&imgDarknet.ans[0])))
+	C.to_float_and_fill_image(&imgDarknet.image, C.int(width), C.int(height), (*C.uint8_t)(unsafe.Pointer(&img.Pix[0])))
 	return imgDarknet, nil
 }
 

image.h

@@ -3,4 +3,5 @@
 #include <darknet.h>
 
 extern void fill_image_f32(image *im, int w, int h, int c, float* data);
-extern void set_data_f32_val(float* data, int index, float value);
+extern void set_data_f32_val(float* data, int index, float value);
+extern void to_float_and_fill_image(image *im, int w, int h, uint8_t* data);

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

@@ -7,9 +7,13 @@ package darknet
 // #include "network.h"
 import "C"
 import (
-	"errors"
+	"io/ioutil"
+	"os"
 	"time"
 	"unsafe"
+
+	"github.com/edsrzf/mmap-go"
+	"github.com/pkg/errors"
 )
 
 // YOLONetwork represents a neural network using YOLO.
@@ -53,12 +57,87 @@ func (n *YOLONetwork) Init() error {
 	return nil
 }
 
+/* EXPERIMENTAL */
+/*
+	By default AlexeyAB's Darknet doesn't export any functions in darknet.h to give ability to create network from scratch via code.
+	So I can't modify `parse_network_cfg_custom` to load `list *sections = read_cfg(filename);` from memory.
+
+	So, the point of this method is to be able create network configuration via Golang and then pass it to `C.load_network`
+
+	This code is portable for Windows/Linux/MacOS. See the ref.: https://github.com/edsrzf/mmap-go#mmap-go
+*/
+func (n *YOLONetwork) InitFromDefinedCfg() error {
+	wFile := C.CString(n.WeightsFile)
+	defer C.free(unsafe.Pointer(wFile))
+	/* Prepare network sections via Go */
+	/*
+		instead of using:
+			wFile := C.CString(n.WeightsFile)
+			defer C.free(unsafe.Pointer(wFile))
+		We call `load_network` that takes the first char* parameter (representing a file path to network configuration) with a Go function that takes an in-memory file
+	*/
+	cfgBytes, err := os.ReadFile(n.NetworkConfigurationFile)
+	if err != nil {
+		return errors.Wrap(err, "Can't read file bytes")
+	}
+
+	// Create a temporary file.
+	tmpFile, err := ioutil.TempFile("", "")
+	if err != nil {
+		return errors.Wrap(err, "Can't create temporary file")
+	}
+	defer os.Remove(tmpFile.Name())
+	// Write the file content to the temporary file.
+	if _, err := tmpFile.Write(cfgBytes); err != nil {
+		return errors.Wrap(err, "Can't write network's configuration into temporary file")
+	}
+	defer tmpFile.Close()
+
+	// Open the temporary file.
+	// fd, err := syscall.Open(tmpFile.Name(), syscall.O_RDWR, 0)
+	// if err != nil {
+	// 	return errors.Wrap(err, "Can't re-open temporary file")
+	// }
+	// defer syscall.Close(fd)
+	// Create a memory mapping of the file.
+	// addr, err := syscall.Mmap(fd, 0, len(cfgBytes), syscall.PROT_READ|syscall.PROT_WRITE, syscall.MAP_SHARED)
+	// if err != nil {
+	// 	return errors.Wrap(err, "Can't mmap on temporary file")
+	// }
+	// Unmap the memory-mapped file.
+	// defer syscall.Munmap(addr)
+
+	// Map the temporary file to memory.
+	mapping, err := mmap.Map(tmpFile, mmap.RDWR, 0)
+	if err != nil {
+		return errors.Wrap(err, "Can't mmap on temporary file")
+	}
+	defer mapping.Unmap() // Unmap the memory-mapped file.
+
+	// GPU device ID must be set before `load_network()` is invoked.
+	C.cuda_set_device(C.int(n.GPUDeviceIndex))
+	nCfg := C.CString(tmpFile.Name())
+	defer C.free(unsafe.Pointer(nCfg))
+	n.cNet = C.load_network(nCfg, wFile, 0)
+	if n.cNet == nil {
+		return errUnableToInitNetwork
+	}
+	C.srand(2222222)
+	n.hierarchalThreshold = 0.5
+	n.nms = 0.45
+	metadata := C.get_metadata(nCfg)
+	n.Classes = int(metadata.classes)
+	n.ClassNames = makeClassNames(metadata.names, n.Classes)
+
+	return nil
+}
+
 // Close and release resources.
 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
 }

section.go

@@ -0,0 +1,58 @@
+package darknet
+
+import (
+	"bufio"
+	"os"
+	"strings"
+)
+
+// Section represents a section in the configuration file.
+type Section struct {
+	Type    string
+	Options []string
+}
+
+// readCfg reads a configuration file and returns a list of sections.
+func readCfg(filename string) ([]Section, error) {
+	// Open the configuration file.
+	file, err := os.Open(filename)
+	if err != nil {
+		return nil, err
+	}
+	defer file.Close()
+
+	// Create a list of sections.
+	var sections []Section
+	var current *Section
+
+	// Read each line in the file.
+	scanner := bufio.NewScanner(file)
+	for scanner.Scan() {
+		line := strings.TrimSpace(scanner.Text())
+		if line == "" {
+			continue
+		}
+		switch line[0] {
+		case '[':
+			if current != nil {
+				sections = append(sections, *current)
+			}
+			current = &Section{Type: line}
+		case '#', ';', '\x00':
+			// Ignore comments and empty lines.
+		default:
+			if current == nil {
+				current = &Section{}
+			}
+			current.Options = append(current.Options, line)
+		}
+	}
+	if current != nil {
+		sections = append(sections, *current)
+	}
+	if err := scanner.Err(); err != nil {
+		return nil, err
+	}
+
+	return sections, nil
+}

section_test.go

@@ -0,0 +1,20 @@
+package darknet
+
+import (
+	"fmt"
+	"testing"
+)
+
+func TestReadSectionsFromCfg(t *testing.T) {
+	sections, err := readCfg("./cmd/examples/yolov7-tiny.cfg")
+	if err != nil {
+		fmt.Println(err)
+		return
+	}
+	for _, s := range sections {
+		fmt.Println(s.Type)
+		for _, o := range s.Options {
+			fmt.Println("\t", o)
+		}
+	}
+}