@Akkisony
from ctypes import *
import numpy as np
import os
import argparse
import sys
from ksnn.api import KSNN
from ksnn.types import *
import cv2 as cv
import re
import math
import random
import time
INPUT_SIZE = 300
NUM_RESULTS = 1917
NUM_CLASSES = 91
Y_SCALE = 10.0
X_SCALE = 10.0
H_SCALE = 5.0
W_SCALE = 5.0
CLASSES = ("???","person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light",
"fire hydrant","???","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant",
"bear","zebra ","giraffe","???","backpack","umbrella","???","???","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite",
"baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","???","wine glass","cup","fork","knife ",
"spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa",
"pottedplant","bed","???","diningtable","???","???","toilet ","???","tvmonitor","laptop ","mouse ","remote ","keyboard ","cell phone","microwave ",
"oven ","toaster","sink","refrigerator ","???","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ")
def sigmoid(x):
return 1. / (1. + np.exp(-x))
def CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1):
w = max(0.0, min(xmax0, xmax1) - max(xmin0, xmin1))
h = max(0.0, min(ymax0, ymax1) - max(ymin0, ymin1))
i = w * h
u = (xmax0 - xmin0) * (ymax0 - ymin0) + (xmax1 - xmin1) * (ymax1 - ymin1) - i
if u <= 0.0:
return 0.0
return i / u
def load_box_priors():
box_priors_ = []
fp = open('./box_priors.txt', 'r')
ls = fp.readlines()
for s in ls:
aList = re.findall('([-+]?\d+(\.\d*)?|\.\d+)([eE][-+]?\d+)?', s)
for ss in aList:
aNum = float((ss[0]+ss[2]))
box_priors_.append(aNum)
fp.close()
box_priors = np.array(box_priors_)
box_priors = box_priors.reshape(4, NUM_RESULTS)
return box_priors
def calc_position(vaildCnt, candidateBox, predictions, box_priors):
for i in range(0, vaildCnt):
if candidateBox[0][i] == -1:
continue
n = candidateBox[0][i]
ycenter = predictions[0][n][0] / Y_SCALE * box_priors[2][n] + box_priors[0][n]
xcenter = predictions[0][n][1] / X_SCALE * box_priors[3][n] + box_priors[1][n]
h = math.exp(predictions[0][n][2] / H_SCALE) * box_priors[2][n]
w = math.exp(predictions[0][n][3] / W_SCALE) * box_priors[3][n]
ymin = ycenter - h / 2.
xmin = xcenter - w / 2.
ymax = ycenter + h / 2.
xmax = xcenter + w / 2.
predictions[0][n][0] = ymin
predictions[0][n][1] = xmin
predictions[0][n][2] = ymax
predictions[0][n][3] = xmax
def nms(vaildCnt, candidateBox, predictions):
for i in range(0, vaildCnt):
if candidateBox[0][i] == -1:
continue
n = candidateBox[0][i]
xmin0 = predictions[0][n][1]
ymin0 = predictions[0][n][0]
xmax0 = predictions[0][n][3]
ymax0 = predictions[0][n][2]
for j in range(i+1, vaildCnt):
m = candidateBox[0][j]
if m == -1:
continue
xmin1 = predictions[0][m][1]
ymin1 = predictions[0][m][0]
xmax1 = predictions[0][m][3]
ymax1 = predictions[0][m][2]
iou = CalculateOverlap(xmin0, ymin0, xmax0, ymax0, xmin1, ymin1, xmax1, ymax1)
if iou >= 0.45:
candidateBox[0][j] = -1
def draw(img, vaildCnt, candidateBox, predictions, scoreBox):
for i in range(0, vaildCnt):
if candidateBox[0][i] == -1:
continue
n = candidateBox[0][i]
xmin = int(max(0.0, min(1.0, predictions[0][n][1])) * img.shape[1])
ymin = int(max(0.0, min(1.0, predictions[0][n][0])) * img.shape[0])
xmax = int(max(0.0, min(1.0, predictions[0][n][3])) * img.shape[1])
ymax = int(max(0.0, min(1.0, predictions[0][n][2])) * img.shape[0])
print("%d @ (%d, %d) (%d, %d) score=%f" % (candidateBox[1][i], xmin, ymin, xmax, ymax, scoreBox[0][i]))
cv.rectangle(img, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
cv.putText(img, '{0} {1:.2f}'.format(CLASSES[candidateBox[1][i]], scoreBox[0][i]),
(xmin, ymin),
cv.FONT_HERSHEY_SIMPLEX,
0.6, (0, 0, 255), 2)
cv.imwrite("out.jpg", img)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--nb-file", help="path to nb file")
parser.add_argument("--so-lib", help="path to so lib")
parser.add_argument("--input-picture", help="path to input picture")
args = parser.parse_args()
if args.nb_file :
if os.path.exists(args.nb_file) == False:
sys.exit('nb file \'' + args.nb_file + '\' not exist')
nbfile = args.nb_file
else :
sys.exit("nb file not found !!! Please specify argument: --nb-file /path/to/nb-file")
if args.input_picture :
if os.path.exists(args.input_picture) == False:
sys.exit('input picture \'' + args.input_picture + '\' not exist')
inputpicturepath = bytes(args.input_picture,encoding='utf-8')
else :
sys.exit("input picture not found !!! Please specify argument: --input-picture /path/to/picture")
if args.so_lib :
if os.path.exists(args.so_lib) == False:
sys.exit('so lib \'' + args.so_lib + '\' not exist')
solib = args.so_lib
else :
sys.exit("so lib not found !!! Please specify argument: --so-lib /path/to/lib")
ssd = KSNN('VIM3')
print(' |---+ KSNN Version: {} +---| '.format(ssd.get_nn_version()))
ssd.nn_init(c_lib_p = solib, nb_p = nbfile)
img = cv.imread( args.input_picture, cv.IMREAD_COLOR )
start = time.time()
outputs = ssd.nn_inference(img,platform = 'TENSORFLOW', num=2, reorder='0 1 2', out_format = out_format.OUT_FORMAT_FLOAT32)
end = time.time()
print('inference : ', end - start)
predictions = outputs[0].reshape((1, NUM_RESULTS, 4))
outputClasses = outputs[1].reshape((1, NUM_RESULTS, NUM_CLASSES))
candidateBox = np.zeros([2, NUM_RESULTS], dtype=int)
scoreBox = np.zeros([1, NUM_RESULTS], dtype=float)
vaildCnt = 0
box_priors = load_box_priors()
# Post Process
# got valid candidate box
for i in range(0, NUM_RESULTS):
topClassScore = -1000
topClassScoreIndex = -1
# Skip the first catch-all class.
for j in range(1, NUM_CLASSES):
score = sigmoid(outputClasses[0][i][j]);
if score > topClassScore:
topClassScoreIndex = j
topClassScore = score
if topClassScore > 0.4:
candidateBox[0][vaildCnt] = i
candidateBox[1][vaildCnt] = topClassScoreIndex
scoreBox[0][vaildCnt] = topClassScore
vaildCnt += 1
# calc position
calc_position(vaildCnt, candidateBox, predictions, box_priors)
# NMS
nms(vaildCnt, candidateBox, predictions)
# Draw result
draw(img, vaildCnt, candidateBox, predictions, scoreBox)
I will post it next week, you can also wait for that time to test directly
