def plot_interactive(f_2d, indexs, dataset_obj: Dataset, labels):
n = f_2d.shape[0]
d_point_index = {i: indexs[i] for i in range(f_2d.shape[0])}
img = imshow_util_uint8(
dataset_obj.get_train_image_at(indexs[0])[0][0], dataset_obj)
# create figure and plot scatter
fig, axis = plt.subplots(1, 2)
line = axis[0].scatter(f_2d[:, 0], f_2d[:, 1], c=labels.reshape(n), s=5)
temp = axis[1].imshow(img)
def hover(event):
# if the mouse is over the scatter points
if line.contains(event)[0]:
ind = line.contains(event)[1]["ind"][0]
r_ind = d_point_index[ind]
print("Getting {0} index".format(r_ind))
img = imshow_util_uint8(
dataset_obj.get_train_image_at(r_ind)[0][0], dataset_obj)
temp.set_data(img)
fig.canvas.draw_idle()
# add callback for mouse moves
fig.canvas.mpl_connect('motion_notify_event', hover)
plt.show()
def generate(self, dataset_object: Dataset, index_list, mask_file,
select_path):
d_name = str(dataset_object.__class__.__name__)
gen_name = str(self.__class__.__name__)
f_name = '{0}_{1}_{2}'.format(d_name, gen_name, now_string())
out_folder = os.path.join('gen_images', f_name)
os.makedirs(out_folder, exist_ok=True)
gen_dict = {}
# open masks pickle
with open(mask_file, 'rb') as f:
mask_dict = pickle.load(f)
for ind in index_list:
mask = mask_dict['masks'][ind]
img = dataset_object.get_train_image_at(ind)[0][
0] # returns (img,label) , img = [batch,w,h,c]
result = self.generate_img_mask(img, mask)
cv2.imwrite(os.path.join(out_folder, '{0}__mask.png'.format(ind)),
mask.astype(np.uint8) * 255)
gen_dict[ind] = []
for ind_out, elem in enumerate(result):
out_path_img = os.path.join(
out_folder, '{0}__{1}.png'.format(ind, ind_out))
cv2.imwrite(out_path_img, elem)
gen_dict[ind].append(out_path_img)
exp_json = {
'date': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
'dataset': d_name,
'used_select': select_path,
'index_map': gen_dict,
'mask_file': str(mask_file),
'generator': gen_name
}
print("Results in " +
str(os.path.join(out_folder, 'exp_details.json')))
with open(os.path.join(out_folder, 'exp_details.json'), 'w') as f:
json.dump(exp_json, f)
def gabor_features_raw_data(kernels,dataset_obj : Dataset,img_shape):
limit = 1400
indexs=[]
labels=[]
imgs_raw=[]
cont=0
for indx in dataset_obj.get_index_list():
img,label = dataset_obj.get_train_image_at(indx)
if not(label in ['1','3']):
continue
indexs.append(indx)
img = img / 255
labels.append(int(label))
imgs_raw.append(img.reshape(img_shape) )
cont+=1
if cont > (limit):
break
# por cada imagen calcular activacion
dataset = []
r_labels = []
indexs_names = []
for ind, cam_img in enumerate(imgs_raw):
if ind > len(labels):
break
if ind != 0 and ind % 100 == 0:
print(ind)
current_vector = []
for k, kernel in enumerate(kernels):
filtered = ndi.convolve(cam_img, kernel, mode='wrap')
# reducir a 8x8
res = imresize(filtered, (8, 8))
# f,ax = plt.subplots(1,3)
# ax[0].imshow(cam_img)
# ax[1].imshow(kernel)
# ax[2].imshow(res)
# plt.show()
# convertir a vector
filter_vector = res.flatten()
current_vector.append(filter_vector)
# concadenar por todos los filtros
current_vector = np.array(current_vector)
current_vector = current_vector.flatten()
# agrega como un vector al dataset
dataset.append(current_vector)
r_labels.append((labels[ind]))
ind_to_add = indexs[ind].decode('utf8') if type(indexs[ind]) == bytes else indexs[ind]
indexs_names.append(ind_to_add)
dataset = np.vstack(dataset)
return dataset,indexs_names,r_labels,imgs_raw