mirror of https://github.com/pjreddie/darknet.git
157 lines
4.3 KiB
Python
157 lines
4.3 KiB
Python
from ctypes import *
|
|
import math
|
|
import random
|
|
|
|
def sample(probs):
|
|
s = sum(probs)
|
|
probs = [a/s for a in probs]
|
|
r = random.uniform(0, 1)
|
|
for i in range(len(probs)):
|
|
r = r - probs[i]
|
|
if r <= 0:
|
|
return i
|
|
return len(probs)-1
|
|
|
|
def c_array(ctype, values):
|
|
arr = (ctype*len(values))()
|
|
arr[:] = values
|
|
return arr
|
|
|
|
class BOX(Structure):
|
|
_fields_ = [("x", c_float),
|
|
("y", c_float),
|
|
("w", c_float),
|
|
("h", c_float)]
|
|
|
|
class DETECTION(Structure):
|
|
_fields_ = [("bbox", BOX),
|
|
("classes", c_int),
|
|
("prob", POINTER(c_float)),
|
|
("mask", POINTER(c_float)),
|
|
("objectness", c_float),
|
|
("sort_class", c_int)]
|
|
|
|
|
|
class IMAGE(Structure):
|
|
_fields_ = [("w", c_int),
|
|
("h", c_int),
|
|
("c", c_int),
|
|
("data", POINTER(c_float))]
|
|
|
|
class METADATA(Structure):
|
|
_fields_ = [("classes", c_int),
|
|
("names", POINTER(c_char_p))]
|
|
|
|
|
|
|
|
#lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
|
|
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
|
|
|
|
predict = lib.network_predict
|
|
predict.argtypes = [c_void_p, POINTER(c_float)]
|
|
predict.restype = POINTER(c_float)
|
|
|
|
set_gpu = lib.cuda_set_device
|
|
set_gpu.argtypes = [c_int]
|
|
|
|
make_image = lib.make_image
|
|
make_image.argtypes = [c_int, c_int, c_int]
|
|
make_image.restype = IMAGE
|
|
|
|
get_network_boxes = lib.get_network_boxes
|
|
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int)]
|
|
get_network_boxes.restype = POINTER(DETECTION)
|
|
|
|
make_network_boxes = lib.make_network_boxes
|
|
make_network_boxes.argtypes = [c_void_p]
|
|
make_network_boxes.restype = POINTER(DETECTION)
|
|
|
|
free_detections = lib.free_detections
|
|
free_detections.argtypes = [POINTER(DETECTION), c_int]
|
|
|
|
free_ptrs = lib.free_ptrs
|
|
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
|
|
|
|
network_predict = lib.network_predict
|
|
network_predict.argtypes = [c_void_p, POINTER(c_float)]
|
|
|
|
reset_rnn = lib.reset_rnn
|
|
reset_rnn.argtypes = [c_void_p]
|
|
|
|
load_net = lib.load_network
|
|
load_net.argtypes = [c_char_p, c_char_p, c_int]
|
|
load_net.restype = c_void_p
|
|
|
|
do_nms_obj = lib.do_nms_obj
|
|
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
|
|
|
|
do_nms_sort = lib.do_nms_sort
|
|
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
|
|
|
|
free_image = lib.free_image
|
|
free_image.argtypes = [IMAGE]
|
|
|
|
letterbox_image = lib.letterbox_image
|
|
letterbox_image.argtypes = [IMAGE, c_int, c_int]
|
|
letterbox_image.restype = IMAGE
|
|
|
|
load_meta = lib.get_metadata
|
|
lib.get_metadata.argtypes = [c_char_p]
|
|
lib.get_metadata.restype = METADATA
|
|
|
|
load_image = lib.load_image_color
|
|
load_image.argtypes = [c_char_p, c_int, c_int]
|
|
load_image.restype = IMAGE
|
|
|
|
rgbgr_image = lib.rgbgr_image
|
|
rgbgr_image.argtypes = [IMAGE]
|
|
|
|
predict_image = lib.network_predict_image
|
|
predict_image.argtypes = [c_void_p, IMAGE]
|
|
predict_image.restype = POINTER(c_float)
|
|
|
|
def classify(net, meta, im):
|
|
out = predict_image(net, im)
|
|
res = []
|
|
for i in range(meta.classes):
|
|
res.append((meta.names[i], out[i]))
|
|
res = sorted(res, key=lambda x: -x[1])
|
|
return res
|
|
|
|
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
|
|
im = load_image(image, 0, 0)
|
|
num = c_int(0)
|
|
pnum = pointer(num)
|
|
predict_image(net, im)
|
|
dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum)
|
|
num = pnum[0]
|
|
if (nms): do_nms_obj(dets, num, meta.classes, nms);
|
|
|
|
res = []
|
|
for j in range(num):
|
|
for i in range(meta.classes):
|
|
if dets[j].prob[i] > 0:
|
|
b = dets[j].bbox
|
|
res.append((meta.names[i], dets[j].prob[i], (b.x, b.y, b.w, b.h)))
|
|
res = sorted(res, key=lambda x: -x[1])
|
|
free_image(im)
|
|
free_detections(dets, num)
|
|
return res
|
|
|
|
if __name__ == "__main__":
|
|
#net = load_net("cfg/densenet201.cfg", "/home/pjreddie/trained/densenet201.weights", 0)
|
|
#im = load_image("data/wolf.jpg", 0, 0)
|
|
#meta = load_meta("cfg/imagenet1k.data")
|
|
#r = classify(net, meta, im)
|
|
#print r[:10]
|
|
net = load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
|
|
meta = load_meta("cfg/coco.data")
|
|
r = detect(net, meta, "data/dog.jpg")
|
|
print(r)
|
|
|
|
|