使用ctypes (image作为参数)从python调用darknet API

import ctypes
from ctypes import CDLL, c_char_p, POINTER, byref, c_int

# 加载Darknet库
lib = CDLL('path_to_darknet/libdarknet.so', RTLD_GLOBAL)

# 定义一些必要的结构和函数原型
class BOX(ctypes.Structure):
    _fields_ = [("x", ctypes.c_float),
                ("y", ctypes.c_float),
                ("w", ctypes.c_float),
                ("h", ctypes.c_float)]

class DETECTION(ctypes.Structure):
    _fields_ = [("bbox", BOX),
                ("classes", ctypes.c_int),
                ("prob", POINTER(ctypes.c_float)),
                ("mask", POINTER(ctypes.c_float)),
                ("objectness", ctypes.c_float),
                ("sort_class", ctypes.c_int)]

# 设置函数原型
lib.network_predict_image.argtypes = [ctypes.c_void_p, ctypes.c_void_p]
lib.get_network_boxes.argtypes = [ctypes.c_void_p, ctypes.c_int, ctypes.c_int, ctypes.c_float, ctypes.c_float, POINTER(ctypes.c_int), ctypes.c_int, POINTER(DETECTION)]
lib.free_detections.argtypes = [POINTER(DETECTION), ctypes.c_int]

# 加载网络配置和权重
net = lib.load_network("path_to_config.cfg", "path_to_weights.weights", 0)
meta = lib.load_meta("path_to_data.data")

# 准备图像数据
# 这里需要将Python的图像数据转换为Darknet可以接受的格式
# ...

# 进行预测
detections = lib.get_network_boxes(net, width, height, thresh, hier_thresh, None, 0, None)
# 处理检测结果
# ...

# 释放资源
lib.free_detections(detections, len(detections))
lib.free_network(net)