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tiny_detection_mxnet.py
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tiny_detection_mxnet.py
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# coding: utf-8
# In[1]:
import mxnet as mx
import numpy as np
import matplotlib.pyplot as plt
import cv2
import scipy.io as sio
import pylab as pl
from collections import namedtuple
import time
Batch = namedtuple('Batch', ['data'])
# In[2]:
MAX_INPUT_DIM=5000.0
prob_thresh = 0.5
nms_thresh = 0.1
# In[3]:
def loadmeta(matpath):
f = sio.loadmat(matpath)
net = f['net']
clusters = np.copy(net['meta'][0][0][0][0][6])
averageImage = np.copy(net['meta'][0][0][0][0][2][0][0][2])
averageImage = averageImage[:, np.newaxis]
return clusters, averageImage
# In[4]:
def nms(dets, prob_thresh):
x1 = dets[:, 0]
y1 = dets[:, 1]
x2 = dets[:, 2]
y2 = dets[:, 3]
scores = dets[:, 4]
areas = (x2 - x1 + 1) * (y2 - y1 + 1)
order = scores.argsort()[::-1]
keep = []
while order.size > 0:
i = order[0]
keep.append(i)
xx1 = np.maximum(x1[i], x1[order[1:]])
yy1 = np.maximum(y1[i], y1[order[1:]])
xx2 = np.minimum(x2[i], x2[order[1:]])
yy2 = np.minimum(y2[i], y2[order[1:]])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
ovr = inter / (areas[i] + areas[order[1:]] - inter)
inds = np.where(ovr <= prob_thresh)[0]
order = order[inds + 1]
return keep
# In[5]:
clusters, averageImage = loadmeta('./hr_res101.mat')
# In[6]:
clusters_h = clusters[:,3] - clusters[:,1] + 1
clusters_w = clusters[:,2] - clusters[:,0] + 1
normal_idx = np.where(clusters[:,4] == 1)
# In[7]:
raw_img = cv2.imread('./stair_exit_000026.jpg')
"""
raw_img1=raw_img[:,:,0]
raw_img2=raw_img[:,:,1]
raw_img3=raw_img[:,:,2]
plt.imshow(raw_img1)
plt.show()
plt.imshow(raw_img2)
plt.show()
plt.imshow(raw_img3)
plt.show()
"""
raw_h = raw_img.shape[0]
raw_w = raw_img.shape[1]
#raw_img = cv2.cvtColor(raw_img, cv2.COLOR_BGR2RGB)
raw_img_f = raw_img.astype(np.float32)
# In[8]:
min_scale = min(np.floor(np.log2(np.max(clusters_w[normal_idx]/raw_w))), np.floor(np.log2(np.max(clusters_h[normal_idx]/raw_h))))
max_scale = min(1.0, -np.log2(max(raw_h, raw_w)/MAX_INPUT_DIM))
# In[9]:
scales_down = pl.frange(min_scale, 0, 1.)
scales_up = pl.frange(0.5, max_scale,0.5)
scales_pow = np.hstack((scales_down, scales_up))
scales = np.power(2.0, scales_pow)
# In[10]:
sym, arg_params, aux_params = mx.model.load_checkpoint('hr101',0)
all_layers = sym.get_internals()
# In[11]:
context=mx.gpu()
# In[12]:
mod = mx.mod.Module(symbol=all_layers['fusex_output'], context=context, data_names=['data'], label_names=None)
mod.bind(for_training=False, data_shapes=[('data', (1, 3, 224, 224))], label_shapes=None, force_rebind=False)
mod.set_params(arg_params=arg_params, aux_params=aux_params, force_init=False)
# In[13]:
start = time.time()
bboxes = np.empty(shape=(0,5))
for s in scales:
img = cv2.resize(raw_img_f, (0,0), fx = s, fy = s)
img = np.transpose(img,(2,0,1))
img = img - averageImage
tids = []
if s <= 1. :
tids = list(range(4, 12))
else :
tids = list(range(4, 12)) + list(range(18, 25))
ignoredTids = list(set(range(0,clusters.shape[0]))-set(tids))
img_h = img.shape[1]
img_w = img.shape[2]
img = img[np.newaxis, :]
mod.reshape(data_shapes=[('data', (1, 3, img_h, img_w))])
mod.forward(Batch([mx.nd.array(img)]))
mod.get_outputs()[0].wait_to_read()
fusex_res = mod.get_outputs()[0]
score_cls = mx.nd.slice_axis(fusex_res, axis=1, begin=0, end=25, name='score_cls')
score_reg = mx.nd.slice_axis(fusex_res, axis=1, begin=25, end=None, name='score_reg')
prob_cls = mx.nd.sigmoid(score_cls)
prob_cls_np = prob_cls.asnumpy()
prob_cls_np[0,ignoredTids,:,:] = 0.
_, fc, fy, fx = np.where(prob_cls_np > prob_thresh)
cy = fy * 8 - 1
cx = fx * 8 - 1
ch = clusters[fc, 3] - clusters[fc,1] + 1
cw = clusters[fc, 2] - clusters[fc, 0] + 1
Nt = clusters.shape[0]
score_reg_np = score_reg.asnumpy()
tx = score_reg_np[0, 0:Nt, :, :]
ty = score_reg_np[0, Nt:2*Nt,:,:]
tw = score_reg_np[0, 2*Nt:3*Nt,:,:]
th = score_reg_np[0,3*Nt:4*Nt,:,:]
dcx = cw * tx[fc, fy, fx]
dcy = ch * ty[fc, fy, fx]
rcx = cx + dcx
rcy = cy + dcy
rcw = cw * np.exp(tw[fc, fy, fx])
rch = ch * np.exp(th[fc, fy, fx])
score_cls_np = score_cls.asnumpy()
scores = score_cls_np[0, fc, fy, fx]
tmp_bboxes = np.vstack((rcx-rcw/2, rcy-rch/2, rcx+rcw/2,rcy+rch/2))
tmp_bboxes = np.vstack((tmp_bboxes/s, scores))
tmp_bboxes = tmp_bboxes.transpose()
bboxes = np.vstack((bboxes, tmp_bboxes))
# In[14]:
##print ("time", time.time()-start, "secs.")
refind_idx = nms(bboxes, nms_thresh)
refind_bboxes = bboxes[refind_idx]
refind_bboxes = refind_bboxes.astype(np.int32)
for r in refind_bboxes:
cv2.rectangle(raw_img, (r[0],r[1]), (r[2],r[3]), (0,255,255),3)
print ("time", time.time()-start, "secs.")
#cv2.imshow('result',raw_img)
#cv2.waitKey(0)
cv2.imwrite('./results/stair_exit_000026.jpg',raw_img)
#plt.imshow(raw_img)
#plt.show()
# In[15]:
bboxes.shape