darknet forecast

from ctypes import *
import math
import random
import cv2
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)
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=.01, 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("/home/zz-admin/nfs/xuyunlong/darknet/AXI3/yolov3-obj1.cfg", "/home/zz-admin/nfs/xuyunlong/darknet/backup/AXI4/yolov3-obj_last.weights", 0)
    
meta = load_meta("/home/zz-admin/nfs/xuyunlong/darknet/AXI3/axi.data")

path='/home/zz-admin/nfs/xuyunlong/darknet/AXI3/test'
path1='/home/zz-admin/nfs/xuyunlong/darknet/AXI3/dest'

array_of_img = [] # this if for store all of the image data

# this function is for read image,the input is directory name

    # this loop is for read each image in this foder,directory_name is the foder name with images.
for filename in os.listdir(path):
 # print(filename)
        # print(filename) #just for test
        # img is used to store the image data
    img = cv2.imread(path + "/" + filename)
  #  array_of_img.append(img)
        # print(img)
    outpath=path+'/'+filename
    print(outpath)
    #print(array_of_img)

#    net = load_net("/home/zz-admin/nfs/xuyunlong/darknet/AXI3/yolov3-obj1.cfg", "/home/zz-admin/nfs/xuyunlong/darknet/backup/AXI4/yolov3-obj_last.weights", 0)
 #   meta = load_meta("/home/zz-admin/nfs/xuyunlong/darknet/AXI3/axi.data")
    rrr = detect(net, meta, outpath)
    print (rrr)
      
   # blktmp=smlregion_val[i].copy()  
    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.rectangle(rawimgcpy,(int(item[2][0]-item[2][2]/2+smlregion_pos[i][2]),int(item[2][1]-item[2][3]/2+smlregion_pos[i][0])),(int(item[2][0]+item[2][2]/2+smlregion_pos[i][2]),int(item[2][1]+item[2][3]/2+smlregion_pos[i][0])),(0,255,0),2)
   # cv2.putText(rawimgcpy, str(int(item[0])) + " [" + str(round(item[1] * 100, 2)) + "]", (int(item[2][0]-item[2][2]/2+smlregion_pos[i][2]),int(item[2][1]-item[2][3]/2+smlregion_pos[i][0]-20)), cv2.FONT_HERSHEY_SIMPLEX, 1, [0, 255, 0], 4)
     # if(len(rrr)>0):
      #cv2.imwrite(os.path.join(lookbig_res,str(i)+"_"+items[0].strip(" \t\r\n")),blktmp)
    #cv2.rectangle(rawimgcpy,(items[1],items[2]),(items[5],items[6]),(255,0,0),2)
    cv2.imwrite(path1 + "/" + filename,img)