darknet1

from ctypes import *
import math
import random
import cv2
import os
import shutil
import time
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=.8, 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("//yolov3-obj1.cfg", "//darknet/backup-n104-608-anchorchengxu/yolov3-obj1_20000.weights", 0)
    
meta = load_meta("//axi.data")

path='//test'
path1='//dest'
if os.path.exists('//dest'):
  shutil.rmtree('/dest')
os.mkdir('//dest')
path2='//dest1'
if os.path.exists('/dest1'):
  shutil.rmtree('//dest1')
os.mkdir('//dest1')
list=[]
for filename in os.listdir(path):
  
    img = cv2.imread(path + "/" + filename)
   
    #size = (1230, 2390)
    #img = cv2.resize(img, size, interpolation=cv2.INTER_AREA)
   
    outpath=path+'/'+filename
   # print(outpath)

    rrr = detect(net, meta, outpath)

if len(rrr)>0:

    print(outpath)   
    print (rrr)


    for item in rrr:
      
      cv2.rectangle(img,(int(item[2][0]-item[2][2]/2),int(item[2][1]-item[2][3]/2)),(int(item[2][0]+item[2][2]/2),int(item[2][1]+item[2][3]/2)),(0,255,0),2)
      
      cv2.putText(img,str(item[0]),(int(item[2][0]-item[2][2]/2),int(item[2][1]-item[2][3]/2)+20),cv2.FONT_HERSHEY_SIMPLEX,1,[0,255,0],4)
    #if len(rrr) ==0:
    if len(rrr) >0:
    
      cv2.imwrite(path1 + "/" + filename,img)
      list.append(filename)
    else:
      cv2.imwrite(path2 + "/" + filename,img)
        
#print(list)