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DarkNet源码中提供的接口，用Python处理一张图片的时候，只能传入图片路径，见python/darknet.py的demo。
if __name__ == "__main__":
net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
meta = load_meta("cfg/coco.data")
r = detect(net, meta, "data/dog.jpg") // 传入图片路径
print r
然后：
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
im = load_image(image, 0, 0) // 调load_image函数
...
即就是调load_image函数加载图片得到IMAGE对象（这个IMAGE是Darknet中自定义的结构体类型）：
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
class IMAGE(Structure):
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]
下面来修改DarkNet源码，来支持加载numpy图片。
1.修改image.c文件
找个位置添加加下面代码（我放到了1042行）：
#ifdef NUMPY
image ndarray_to_image(unsigned char* src, long* shape, long* strides)
{
int h = shape[0];
int w = shape[1];
int c = shape[2];
int step_h = strides[0];
int step_w = strides[1];
int step_c = strides[2];
image im = make_image(w, h, c);
int i, j, k;
int index1, index2 = 0;
for(i = 0; i < h; ++i){
for(k= 0; k < c; ++k){
for(j = 0; j < w; ++j){
index1 = k*w*h + i*w + j;
index2 = step_h*i + step_w*j + step_c*k;
// fprintf(stderr, "w=%d h=%d c=%d step_w=%d step_h=%d step_c=%d \n", w, h, c, step_w, step_h, step_c);
// fprintf(stderr, "im.data[%d]=%u data[%d]=%f \n", index1, src[index2], index2, src[index2]/255.);
im.data[index1] = src[index2]/255.;
}
}
}
rgbgr_image(im);
return im;
}
#endif
2.修改image.h文件
添加下面代码（我放在了47行）：
#ifdef NUMPY
image ndarray_to_image(unsigned char* src, long* shape, long* strides);
#endif
3.修改Makefile文件
（1）最前面添加NUMPY=1
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
NUMPY=1
DEBUG=0
（2）在大概50行找个地方添加：
ifeq ($(NUMPY), 1)
COMMON+= -DNUMPY -I/usr/include/python2.7/ -I/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/
CFLAGS+= -DNUMPY
endif
4.重新编译DarkNet
make clean
make all -j16
5.修改demo
（1）添加函数：
def nparray_to_image(img):
data = img.ctypes.data_as(POINTER(c_ubyte))
image = ndarray_image(data, img.ctypes.shape, img.ctypes.strides)
return image
（2）添加：
ndarray_image = lib.ndarray_to_image
ndarray_image.argtypes = [POINTER(c_ubyte), POINTER(c_long), POINTER(c_long)]
ndarray_image.restype = IMAGE
完整demo示例：
#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys, os
sys.path.append(os.path.join(os.getcwd(),'python/'))
# import pdb
from ctypes import *
import math
import random
import numpy as np
from PIL import Image
class IMAGE(Structure):
_fields_ = [("w", c_int),
("h", c_int),
("c", c_int),
("data", POINTER(c_float))]
def nparray_to_image(img):
data = img.ctypes.data_as(POINTER(c_ubyte))
image = ndarray_image(data, img.ctypes.shape, img.ctypes.strides)
return image
lib = CDLL("libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
ndarray_image = lib.ndarray_to_image
ndarray_image.argtypes = [POINTER(c_ubyte), POINTER(c_long), POINTER(c_long)]
ndarray_image.restype = IMAGE
predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
free_image = lib.free_image
free_image.argtypes = [IMAGE]
def MGN(net, image_path):
# PIL Image
image = Image.open(image_path) # RGB
image = image.resize((128, 384))
image = np.array(image)
image = image / 255.0
# image = load_image(image_path, 0, 0) # 由路径加载得到IMAGE
im = nparray_to_image(image) # 由numpy转换得到IMAGE
out = predict_image(net, im)
res = []
for i in range(2048):
res.append(out[i])
print(i, out[i])
free_image(im)
return res
if __name__ == '__main__':
net = load_net("cfg/MGN.cfg", "backup/MGN.weights", 0)
test_img = "data/test.jpg"
res = MGN(net, test_img)
print(len(res))
print("Done!")
6.Reference
https://blog.csdn.net/phinoo/article/details/83009061
https://github.com/pjreddie/darknet/issues/289