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darknet_npinput_rpi.py
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darknet_npinput_rpi.py
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#!/usr/bin/python3
import math
import random
import time
from ctypes import *
from datetime import datetime
import cv2
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 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("/usr/lib/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)
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
make_boxes = lib.make_boxes
make_boxes.argtypes = [c_void_p]
make_boxes.restype = POINTER(BOX)
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
num_boxes = lib.num_boxes
num_boxes.argtypes = [c_void_p]
num_boxes.restype = c_int
make_probs = lib.make_probs
make_probs.argtypes = [c_void_p]
make_probs.restype = POINTER(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
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)
network_detect = lib.network_detect
network_detect.argtypes = [c_void_p, IMAGE, c_float, c_float, c_float, POINTER(BOX), POINTER(POINTER(c_float))]
def nparray_to_image(arr):
"""Convert nparray to Darknet image struct.
Args:
arr: nparray containing source image in BGR color model.
Returns:
Darknet image struct, whose data is a C array
containing flatten image in BGR color model.
"""
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)
rgbgr_image(im)
return im
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)
boxes = make_boxes(net)
probs = make_probs(net)
num = num_boxes(net)
t_start = time.time()
network_detect(net, im, thresh, hier_thresh, nms, boxes, probs)
t_end = time.time()
print('inference time: {} s'.format(t_end - t_start))
res = []
for j in range(num):
for i in range(meta.classes):
if probs[j][i] > 0:
res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
res = sorted(res, key=lambda x: -x[1])
free_image(im)
free_ptrs(cast(probs, POINTER(c_void_p)), num)
return res
def detect_np(net, meta, np_img, thresh=.5, hier_thresh=.5, nms=.45):
im = nparray_to_image(np_img)
boxes = make_boxes(net)
probs = make_probs(net)
num = num_boxes(net)
t_start = time.time()
network_detect(net, im, thresh, hier_thresh, nms, boxes, probs)
t_end = time.time()
print('inference time: {} s'.format(t_end - t_start))
res = []
for j in range(num):
for i in range(meta.classes):
if probs[j][i] > 0:
res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
res = sorted(res, key=lambda x: -x[1])
free_ptrs(cast(probs, POINTER(c_void_p)), num)
return res
if __name__ == "__main__":
net = load_net(
b"/usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.cfg",
b"/usr/share/dlmodels/tinyyolovoc-20170816/tiny-yolo-voc.weights",
0)
meta = load_meta(b"/usr/share/dlmodels/tinyyolovoc-20170816/voc.data")
print("===== default loading image =====")
r = detect(net, meta, b"data/dog.jpg")
print(r)
print("===== nparray support test =====")
im = cv2.imread("data/dog.jpg")
r = detect_np(net, meta, im)
print(r)
print([(i[0].decode('utf-8'), i[1], i[2]) for i in r])