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detect_object.py
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detect_object.py
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#%%
from ctypes import *
import random
import argparse
import os
import traceback
import cv2
import functools
import numpy as np
import time
import sys
import imutils
#%%
THIS_DIR = os.path.dirname(os.path.realpath(__file__))
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))]
libdarknet_path = os.path.join("/home/darknet/libdarknet.so")
lib = CDLL(libdarknet_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
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 array_to_image(arr):
# need to return old values to avoid python freeing memory
arr = arr.transpose(2,0,1)
c, h, w = arr.shape[0:3]
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(w,h,c,data)
return im, arr
def netdetect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45):
im, image = array_to_image(image)
rgbgr_image(im)
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):
a = dets[j].prob[0:meta.classes]
if any(a):
ai = np.array(a).nonzero()[0]
for i in ai:
b = dets[j].bbox
res.append((meta.names[i], dets[j].prob[i], (max(b.x,0), max(b.y,0), max(b.w,0), max(b.h,0))))
res = sorted(res, key=lambda x: -x[1])
if isinstance(image,bytes): free_image(im)
free_detections(dets, num)
return res
#%%
def convert_yolo_normal(x,y,w,h,s1,s2):
b0=(2*x-w)/2
b1=w+b0
b2=(2*y-h)/2
b3=h+b2
return (int(b0),int(b1),int(b2),int(b3))
#%%
if __name__ == "__main__":
config_file = 'object_detector.config'
with open('config.json') as f:
data = json.load(f)
weights_file = data['weights_file']
cfg_file = data['cfg_file']
obj_data = data['obj_file']
image_name = sys.argv[1]
img = cv2.imread(image_name)
netdet = load_net(cfg_file,weights_file,0)
metadet = load_meta(obj_data)
obj_res = netdetect(netlp,metalp, veh,0.7)
for obj_res:
print('All detected objects are:: ')
for i in range(len(obj_res)):
print(obj_res[i][0])