def tracker(i):
boxes = Boxes(len(files))
tex = Texture()
ims = Slicer()
th = Threshold()
hog = Hog()
cells = Cells(sizeh, sizew)
names = list(listdir(path))
names = names[i:i + batch_size]
net = Network()
net.load('data/net/dataset_184_nets_ann/', 'compile_config.txt',
hog_block_size, hog_cell_size, hog_orientations)
index = 0
while (len(names) > 0):
index = len(names) - 1
name = names.pop()
img = cv2.imread(path + name)
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
images, coords = ims.split(img_grey, sizeh, sizew, sizeh, sizew)
signal = tex.analize(images, haralick_feature)
indexes = th.threshold_mean(signal)
#coords_f = []
#for x in indexes:
# coords_f.append(coords[x])
coords = coords[indexes]
coords = cells.group(coords)
coords = cells.reorganize_coords(coords)
for c in coords:
part_img = img_grey[c[2]:c[3], c[0]:c[1]]
part_img = cv2.resize(part_img, resize_img)
part = hog.runOne(part_img,
orient=hog_orientations,
pixels=hog_cell_size,
cells=hog_block_size)
part = part.reshape((1, part.shape[0]))
Y = net.predict(part)[0, 0]
if (Y > thresh and c[0] != 0 and c[1] != 500
and abs(c[0] - c[1]) * abs(c[2] - c[3]) > 625):
cells.box2(img, c)
box = Box()
box.box = [c[0], c[2], c[1], c[3]]
boxes.add(index, box)
cv2.imwrite(path_out + name, img)
boxes.save('boxes_predicted_ann_25.b')
def tracker(i):
boxes = Boxes(len(files))
tex = Texture()
ims = Slicer()
th = Threshold()
hog = Hog()
cells = Cells(sizeh,sizew)
names = list(listdir(path))
names = names[i:i+batch_size]
f = open('data/net/dataset_184_nets_svm/svm_c_2_p_8_o_2.svm','rb')
clf = pickle.load(f)
f.close()
while(len(names)>0):
index = len(names)-1
name = names.pop()
img = cv2.imread(path+name)
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
images,coords = ims.split(img_grey,sizeh,sizew,sizeh,sizew)
signal_p = tex.analize(images,haralick_feature)
indexes = th.threshold_mean(signal_p)
coords_f = []
for x in indexes:
coords_f.append(coords[x])
coords = cells.group(coords_f)
coords = cells.reorganize_coords(coords)
for c in coords:
part_img = img_grey[c[2]:c[3],c[0]:c[1]]
part_img = cv2.resize(part_img,resize_img)
part = hog.runOne(part_img,orient=hog_orientations,pixels=hog_cell_size,cells=hog_block_size)
part = part.reshape((1,part.shape[0]))
Y = clf.predict(part)
if(Y>thresh and c[0]!=0 and c[1]!=500 and abs(c[0]-c[1])*abs(c[2]-c[3])>625 ):
cells.box2(img,c)
box = Box()
box.box = [c[0],c[2],c[1],c[3]]
boxes.add(index,box)
cv2.imwrite(path_out+name,img)
boxes.save('boxes_predicted_svm_25.b')
def tracker(i):
boxes = Boxes(len(files))
tex = Texture()
ims = Slicer()
th = Threshold()
cells = Cells(sizeh,sizew)
names = list(listdir(path))
names = names[i:i+batch_size]
f = open('svmsemhog.svm','rb')
clf = pickle.load(f)
f.close()
while(len(names)>0):
index = len(names)-1
name = names.pop()
img = cv2.imread(path+name)
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
images,coords = ims.split(img_grey,sizeh,sizew,sizeh,sizew)
signal_p = tex.analize(images,haralick_feature)
indexes = th.threshold_mean(signal_p)
coords_f = []
for x in indexes:
coords_f.append(coords[x])
coords = cells.group(coords_f)
coords = cells.reorganize_coords(coords)
for c in coords:
part_img = img_grey[c[2]:c[3],c[0]:c[1]]
part_img = cv2.resize(part_img,resize_img).flatten()
print(part_img)
input()
Y = clf.predict(part_img)
if(Y>thresh and c[0]!=0 and c[1]!=500 and abs(c[0]-c[1])*abs(c[2]-c[3])>625 ):
cells.box2(img,c)
box = Box()
box.box = [c[0],c[2],c[1],c[3]]
boxes.add(index,box)
cv2.imwrite(path_out+name,img)
boxes.save('boxes_predicted_svm.b')
tex = Texture()
ims = Slicer()
th = Threshold()
img = cv2.imread("000475.jpg")
img_grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
images, coords = ims.split(img, 50, 50, 50, 50)
'''i=0
for img in images:
if(i%2==0):
cv2.rectangle(img, (0,0),(50,50), (225,105,65), 2)
cv2.imwrite('img'+str(i)+'.jpg',img)
i+=1'''
l = tex.analize(images, 3)
mean_l = statistics.mean(l)
std_l = statistics.stdev(l)
print(mean_l)
print(std_l)
x = list(range(0, len(l)))
top = max(l) - mean_l
plt.plot(x, l, linestyle='-', marker='o', color='c')
plt.plot(x, len(l) * [mean_l], 'g')
slicer = Slicer()
tex = Texture()
ths = Threshold()
img_p = cv2.imread("data/frames/frames0/000235.jpg")
img_n = cv2.imread("data/frames/frames0/000270.jpg")
imgs_p, coords = slicer.split(img_p, 25, 25, 25, 25)
imgs_n, coords = slicer.split(img_n, 25, 25, 25, 25)
# 1 - energia
# 2 - entropia
# 3 - correlação
# 5 - inércia
signal_p = tex.analize(imgs_p, 1)[1:]
media_p = media(signal_p)
#signal_p = transform(signal_p,media_p)
#media_p = media(signal_p)
#signal_p = transform(signal_p,media_p)
#signal_n = tex.analize(imgs_n,3)[1:]
#media_n = media(signal_n)
#signal_n = transform(signal_n,media_n)
#media_n = media(signal_n)
#signal_n = transform(signal_n,media_n)
x = range(len(signal_p))
#plt.subplot(121)
#plt.plot(x,[media_p]*len(signal_p),'-r')
