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darknet.py
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darknet.py
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from ctypes import *
import math
import random
import os
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)
def init():
current_path = os.getcwd()
lib_path = current_path + "/darknet/libdarknet.so"
global lib
lib = CDLL(lib_path, 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
global predict
predict = lib.network_predict
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
global set_gpu
set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]
global make_image
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
global get_network_boxes
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)
global make_network_boxes
make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)
global free_detections
free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]
global free_ptrs
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
global network_predict
network_predict = lib.network_predict
network_predict.argtypes = [c_void_p, POINTER(c_float)]
global reset_rnn
reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]
global load_net
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
global do_nms_obj
do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
global do_nms_sort
do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
global free_image
free_image = lib.free_image
free_image.argtypes = [IMAGE]
global letterbox_image
letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE
global load_meta
load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA
global load_image
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
global rgbgr_image
rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]
global predict_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_loadim(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
def array_to_image(arr):
arr = arr.transpose(2,0,1)
c = arr.shape[0]
h = arr.shape[1]
w = arr.shape[2]
arr = (arr/255.0).flatten()
data = c_array(c_float, arr)
im = IMAGE(w,h,c,data)
return im
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
image = array_to_image(image)
rgbgr_image(image)
num = c_int(0)
pnum = pointer(num)
predict_image(net, image)
dets = get_network_boxes(net, image.w, image.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_detections(dets, num)
return res
if __name__ == "__main__":
init()
current_path = os.getcwd()
darknet_path = current_path + "/darknet"
config_path = (darknet_path + "/cfg/yolov3.cfg").encode()
weight_path = (darknet_path + "/yolov3.weights").encode()
data_path = (darknet_path + "/cfg/coco.data").encode()
img_path = (darknet_path + "/data/dog.jpg").encode()
#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(config_path, weight_path, 0)
meta = load_meta(data_path)
r = detect_loadim(net, meta, img_path)
print(r)