def analysis(img, model, preprocess_input, decode_predictions, layer_name,
n_classes):
# Preprocess data
imgArray, originalImage = process_image(img, preprocess_input)
preds = model.predict(imgArray)
pred_class = np.argmax(preds) # Change here to get view of something else
decoded_preds = decode_predictions(preds)
class_data = decoded_preds[0][0]
# Compute methods
localization_grad = grad_cam(imgArray, model, pred_class, originalImage,
relu_activation, linear, layer_name,
n_classes)
localization_squad = grad_cam(imgArray, model, pred_class, originalImage,
relu_activation, squared_weights, layer_name,
n_classes)
bprop = guided_backprop(imgArray, model, pred_class, layer_name, n_classes)
# Make it three dimensions to allow for multiplication
localization_grad = np.array(
[localization_grad, localization_grad, localization_grad])
localization_grad = np.swapaxes(localization_grad, 0, 2)
localization_grad = np.swapaxes(localization_grad, 0, 1)
localization_squad = np.array(
[localization_squad, localization_squad, localization_squad])
localization_squad = np.swapaxes(localization_squad, 0, 2)
localization_squad = np.swapaxes(localization_squad, 0, 1)
# Combine gradcam and backprop to get guided gradcam
guided_gradcam = np.multiply(localization_grad, bprop)
guided_squadcam = np.multiply(localization_squad, bprop)
guided_gradcam = rescale(guided_gradcam)
guided_squadcam = rescale(guided_squadcam)
return bprop, guided_gradcam, guided_squadcam, class_data
def save(self, *args, **kwargs):
status = kwargs.pop('status', None)
if self.image:
# ProfileSerializer update에서, image를 제외한 다른
# Profile 필드들을 새로 업데이트하는 경우 resizing을 실시하지 않는다
if status != 'same-image':
image_file_byte_data = self.image.read()
thumbnail_image_file_200 = rescale(image_file_byte_data,
200,
200,
force=True)
thumbnail_image_file_50 = rescale(image_file_byte_data,
50,
50,
force=True)
thumbnail_image_file_25 = rescale(image_file_byte_data,
25,
25,
force=True)
self.thumbnail_image_200.save(f'{self.image.name}',
thumbnail_image_file_200,
save=False)
self.thumbnail_image_50.save(f'{self.image.name}',
thumbnail_image_file_50,
save=False)
self.thumbnail_image_25.save(f'{self.image.name}',
thumbnail_image_file_25,
save=False)
super().save(*args, **kwargs)
def _parallel_eval(Classifier, params, X, y, w, n_repeat=5, verbose=1):
if verbose > 0:
print "[Start]", params
thresholds, scores, decisions = [], [], []
for i in range(n_repeat):
if verbose > 0:
print "Fold", i
X_train, X_valid, y_train, y_valid, w_train, w_valid = train_test_split(X, y, w, train_size=0.5, random_state=i)
X_train = np.asfortranarray(X_train, dtype=np.float32)
w_train = rescale(w_train)
w_train = rebalance(y_train, w_train)
clf = Classifier(**params)
try:
clf = clf.fit(X_train, y_train, sample_weight=w_train)
except:
clf = clf.fit(X_train, y_train)
threshold, score, d = find_threshold(clf, X_valid, y_valid, w_valid)
print params, i, threshold, score
thresholds.append(threshold)
scores.append(score)
decisions.append(d)
if verbose > 0:
print "[End]", params, np.mean(thresholds), np.mean(scores)
return (np.mean(scores), np.mean(thresholds), params, thresholds, scores, decisions)
def draw_cell(cell_info, position):
rw, rh = calculate_cell_parameters(cell_info)
rx = position[0] * cell_w + ((cell_w - rw) / 2)
ry = position[1] * cell_h + ((cell_h - rh) / 2)
size = rw * rh
c = int(utils.rescale(size, 0, cell_w * cell_h, 0, num_colors))
pygame.draw.rect(screen, colors[c], (rx, ry, rw, rh))
def predict():
image_data = request.files.get("image")
image_data_filename = os.path.join(
app.config["UPLOAD_DIRECTORY"],
str(token_hex(16)) + os.path.splitext(image_data.filename)[1],
)
image_data.save(image_data_filename)
x = load_img(image_data_filename, target_size=(224, 224))
x = np.array(img_to_array(x))
x = rescale(x)
value = None
pred = model.predict_proba(x.reshape(1, 224, 224, 3)).flatten()
print(pred)
for i in pred:
if 0 <= i <= 0.2:
value = "Cat"
elif 0.99 <= i <= 1:
value = "Dog"
else:
value = "Neither a cat nor a dog"
return {"message": value}
def _parallel_eval(Classifier, params, X, y, w, n_repeat=5, verbose=1):
if verbose > 0:
print "[Start]", params
thresholds, scores = [], []
for i in range(n_repeat):
if verbose > 0:
print "Fold", i
_, X_fold, _, y_fold, _, w_fold = train_test_split(X, y, w, train_size=0.5, random_state=i)
X_pred = load_predictions("stack/*-fold%d.npy" % i)
X_fold = np.hstack((X_fold, X_pred))
X_train, X_valid, y_train, y_valid, w_train, w_valid = train_test_split(X_fold, y_fold, w_fold, train_size=0.33, random_state=i)
X_train = np.asfortranarray(X_train, dtype=np.float32)
w_train = rescale(w_train)
w_train = rebalance(y_train, w_train)
clf = Classifier(**params)
try:
clf = clf.fit(X_train, y_train, sample_weight=w_train)
except:
clf = clf.fit(X_train, y_train)
threshold, score, _ = find_threshold(clf, X_valid, y_valid, w_valid)
thresholds.append(threshold)
scores.append(score)
if verbose > 0:
print "[End]", params, np.mean(thresholds), np.mean(scores)
return (np.mean(scores), np.mean(thresholds), params, thresholds, scores)
def is_sushi(image_file):
image = rescale(misc.imread(image_file, mode='L'), max_shape=max_shape)
x_image = image.reshape(1, max_shape[0], max_shape[1], 1)
logit = sess.run(logits,
feed_dict={
X: x_image,
y: np.array([-1]).reshape(1, 1),
keep_prob: 1
})[0][0]
p_sushi = 1 / (1 + np.exp(-logit))
if p_sushi > 0.8:
title = "It's sushi! ({0:.0f}% sure)".format(p_sushi * 100)
elif p_sushi >= 0.5 and p_sushi < 0.8:
title = "Is it sushi...? ({0:.0f}% sure)".format(p_sushi * 100)
elif p_sushi < 0.5 and p_sushi > 0.2:
title = "Is it a sandwich...? ({0:.0f}% sure)".format(
(1 - p_sushi) * 100)
else: #p_sushi < 0.2:
title = "It's a sandwich! ({0:.0f}% sure)".format((1 - p_sushi) * 100)
plt.imshow(misc.imread(image_file))
plt.title(title)
plt.show()
def do_ppdrc(fp, filtsize):
dat = fp.astype('float64')
dat[np.isnan(dat)] = 0
dat1 = ppdrc.ppdrc(dat, filtsize)
dat1 = humutils.rescale(dat1.getdata(), np.min(dat), np.max(dat))
dat1[np.isnan(fp)] = np.nan
return dat1
def gradX1(self, X1, X2=None):
# Compute the derivative of the kernel w.r.t to the first argument
# should return a m * n * d tensor
X1, X2 = rescale(np.exp(self.log_b_), X1, X2)
D = diff(X1, X2) # m * n * d array
K = np.exp(self.log_b_ * 2 - 0.5 * np.sum(np.square(D), axis=-1)) # sum alsong the last axis, which is d
G = -D * K[:, :, None] / self.log_c_ # G(m, n, d) corresponds to the derivative of of K(m ,n) w.r.t X1(m, d)
return G
def msg_received(msg):
with cmd_mutex:
scale_func = lambda val: val * 0.5
if msg.get_type() == LEFT_GO:
speed = scale_func(msg.get_value())
controllers['fl'].set_torque_buffered(speed)
controllers['bl'].set_torque_buffered(speed)
elif msg.get_type() == RIGHT_GO:
#Value has to be negated because servos oriented differently
speed = -scale_func(msg.get_value())
controllers['fr'].set_torque_buffered(speed)
controllers['br'].set_torque_buffered(speed)
elif msg.get_type() == ARM_GO:
#Value is between 0 and 127. Arm goes from 200-850 (AX12 Units)
lower = 200
upper = 850
result = rescale(msg.get_value(), 0, 127, 0,
(upper - lower) / 1023.0 * radians(300))
controllers['arm_base'].set_position_buffered(result)
elif msg.get_type() == WRIST_GO:
#Value is between 0 and 127. Wrist goes from 173 - 820 (AX12 Units)
lower = 173
upper = 820
result = rescale(msg.get_value(), 0, 127, 0,
(upper - lower) / 1023.0 * radians(300))
controllers['arm_wrist'].set_position_buffered(result)
elif msg.get_type() == HAND_GO:
#Value is between 0 and 127. Hand goes from 150-870 (AX12 Units)
lower = 150
upper = 870
result = rescale(msg.get_value(), 0, 127, 0,
(upper - lower) / 1023.0 * radians(300))
controllers['arm_hand'].set_position_buffered(result)
else:
print 'Unexpected message'
print str(msg)
def get_batch(batch_size):
image_indexes = np.random.randint(0, len(all_images), batch_size)
images = np.array([
np.expand_dims(np.array(
Image.open(os.path.join(DATASET_PATH, all_images[i]))),
axis=2) for i in image_indexes
])
return rescale(images.astype(np.float32), -1, 1, data_min=0, data_max=255)
def grad(self, X1, X2=None):
# Compute the derivative w.r.t the kernel hyper parameters
X1, X2 = rescale(np.exp(self.log_b_), X1, X2)
D = sqdist(X1, X2)
K = np.exp(self.log_c_ * 2 - 0.5 * D)
yield 2 * K # the gradient w.r.t the signal stddev
if self.iso_:
yield K * D # the gradient w.r.t the lengscale (square-kernel case)
else:
for D in sqdist_foreach(X1, X2):
yield K * D # the gradient w.r.t the lengscales (ARD-kernel case)
def rolling_scores(tested, true_ints=None, show=1000, window=50, rescale=0,
**kwargs):
if rescale > 0:
tested = [(t[0],t[1],ut.rescale(t[2],rescale), t[3]) for t in tested]
if true_ints:
tested = cv.tested_from_trues(tested, true_ints)
padded = list(np.zeros((50,4)))+list(tested)
rolling = [len([t for t in padded[i:i+window] if t[3]==1])/window for i in range(show)]
plot(rolling, **kwargs)
plot([t[2] for t in tested[:show]], **kwargs)
xlabel('starting index in scored examples')
ylabel('fraction true in index:index+%s'%window)
legend(['fraction true','score'])
def train(Classifier, params, X, y, w, verbose=1):
if verbose > 0:
print "[Start]"
w = rescale(w)
w = rebalance(y, w)
clf = Classifier(**params)
clf.fit(X, y, sample_weight=w)
if verbose > 0:
print "[End]"
return clf
def create_and_store_output(ndim, func, fidelity):
file_out = Path(
f'regression_files/output_{ndim}d_{func.name}_{fidelity}.npy')
if file_out.exists():
return
file_in = Path(f'regression_files/input_{ndim}d.npy')
if not file_in.exists():
create_and_store_input(ndim)
x = rescale(np.load(file_in),
range_in=ValueRange(0, 1),
range_out=ValueRange(*func.bounds))
np.save(file_out, func[fidelity](x))
print(f"output {ndim}d {func.name} created")
def save(self, **kwargs):
"""
image를 썸네일화 하여 저장
:param kwargs:
:return:
"""
image = self.validated_data.pop('image', None)
if not image:
return super().save(**kwargs)
with atomic():
try:
super().save(**kwargs)
resized_image = utils.rescale(data=image.read(), width=200, height=200)
filename = f"{self.instance.pk}/{image.name}"
self.instance.image.save(filename, resized_image, save=True)
except:
raise ParseError({"error": "이미지 저장에 실패했습니다."})
def _parallel_eval(Classifier, params, X, y, w, n_repeat=5, verbose=1):
if verbose > 0:
print "[Start]", params
thresholds, scores = [], []
for i in range(n_repeat):
if verbose > 0:
print "Fold", i
_, X_fold, _, y_fold, _, w_fold = train_test_split(X,
y,
w,
train_size=0.5,
random_state=i)
X_pred = load_predictions("stack/*-fold%d.npy" % i)
X_fold = np.hstack((X_fold, X_pred))
X_train, X_valid, y_train, y_valid, w_train, w_valid = train_test_split(
X_fold, y_fold, w_fold, train_size=0.33, random_state=i)
X_train = np.asfortranarray(X_train, dtype=np.float32)
w_train = rescale(w_train)
w_train = rebalance(y_train, w_train)
clf = Classifier(**params)
try:
clf = clf.fit(X_train, y_train, sample_weight=w_train)
except:
clf = clf.fit(X_train, y_train)
threshold, score, _ = find_threshold(clf, X_valid, y_valid, w_valid)
thresholds.append(threshold)
scores.append(score)
if verbose > 0:
print "[End]", params, np.mean(thresholds), np.mean(scores)
return (np.mean(scores), np.mean(thresholds), params, thresholds, scores)
def grad_cam(input_data, model, index, originalImage, activation_function,
combination, layer_name, n_classes):
# Define the loss function
def loss_function(x):
return categorical_crossentropy(one_hot([index], n_classes), x)
# Create loss layer
loss_layer = Lambda(loss_function)(model.output)
# Build a new model with the loss function.
model = Model(inputs=model.input, outputs=loss_layer)
# Extract layer and loss to compute gradient
conv_layer = model.get_layer(layer_name).output
loss = sum(model.output)
# Snagged this code from someplace else
grads = K.gradients(loss, conv_layer)
gradient_function = function([model.inputs[0]], [conv_layer, grads])
# Compute the desired values
output_values, gradients = gradient_function([input_data])
output_values = output_values[0]
gradients = -gradients[0][0]
# Compute weights according to equation 1
alphas = np.mean(gradients, axis=(0, 1))
# Apply combination
combo = combination(alphas, output_values)
# Apply activation function
combo = activation_function(combo)
# Reshape and rescale feature map
combo = cv2.resize(combo, (224, 224))
combo = rescale(combo)
return combo
else:
filename = data_folder + 'control/' + ID
data, filter = utils.load_edf_file(filename,
channels_to_load,
epoch_duration=5*60)
if counter == 0:
n_chans, n_epochs, n_epoch_samples = data["sigbufs"].shape
output_file_name = output_folder + ID[:-4] + ".hpf5"
with h5py.File(output_file_name, "w") as f:
x = data['sigbufs']
if simulated_data:
x = np.random.normal(0, 1, x.shape)
if group[counter] == 1:
x = utils.add_known_complex(x, data['fs'])
x = utils.rescale(x, data['fs'], rescale_mode)
dset = f.create_dataset("x", data=x, chunks=True)
f['fs'] = data['fs']
f["group"] = group[counter]
print("All files processed.")
now = datetime.datetime.now()
with open(output_folder + "conversion.log", "w") as log:
log.write("Conversion for all files concluded on: " + str(now))
log.write("\n Utilized channels: " + str(channels_to_load))
log.write("\n Applied filter: " + filter["type"])
log.write("\n\t Low cut: " + str(filter["lowcut"]))
log.write("\n\t High cut: " + str(filter["highcut"]))
log.write("\n Rejection of epochs based on sum of Pxx above 1000.)")
log.write("\n Signal has been scaled by: " + str(rescale_mode))
except:
print(' Hypnogram error')
continue
epoched = np.zeros(
(int(len(channels_to_load)), int(n_epochs), int(epoch_samples)))
for i in range(len(channels_to_load)):
epoched[int(i), :, :] = np.asarray(
list(zip(*[iter(x[int(i), :])] * int(epoch_samples))))
if simulated_data:
x = np.random.normal(0, 1, x.shape)
if group[counter] == 1:
x = utils.add_known_complex(x, data['fs'])
x = utils.rescale(epoched, data['fs'], rescale_mode)
if cohort == 'SSC':
stages_to_use = [5, 2]
stage_names = [
'wake', 'n1', 'n2', 'n3', 'n4', 'rem', 'unknown', 'artefact'
]
for group, stage in enumerate(stages_to_use):
output_file_name = output_folder + ID[:-4] + '_' + stage_names[
stage] + ".hpf5"
with h5py.File(output_file_name, "w") as f:
dset = f.create_dataset("x",
data=x[:, hypnogram == stage, :],
chunks=True)
f['fs'] = data['fs']
f["group"] = group
w,
train_size=0.5,
random_state=i)
X_pred = load_predictions("stack/*-fold%d.npy" % i)
X_fold = np.hstack((X_fold, X_pred))
all_X.append(X_fold)
all_y.append(y_fold)
all_w.append(w_fold)
X = np.vstack(all_X)
y = np.concatenate(all_y)
w = np.concatenate(all_w)
clf = Classifier(**params)
w = rescale(w)
w = rebalance(y, w)
try:
clf.fit(X, y, sample_weight=w)
except:
clf.fit(X, y)
# And make a submussion
print "Making submission..."
X_test, _, _, _ = load_test()
X_pred = load_predictions("stack/*-test.npy")
X_test = np.hstack((X_test, X_pred))
make_submission(clf, threshold, "output-stacking.csv", X_test=X_test)
def query_2d(num_id):
segs = match[num_id]
project_points = list()
remainder_points = list()
remainder_heads = list()
base_line = segs[0]
index = df[df['id'] == num_id].index[0]
df_data = df.loc[index]
#df_data = df[df['id']==num_id]
#inv_T = np.zeros((4,4))
direction = False
norm_0 = None
direction_z = None
ref_z = None
reflectances_effect = list()
normals_effect = list()
depths_effect = list()
for i, (name, nid) in enumerate(segs):
print('\rseg: {}/{}'.format(i, len(segs) - 1), end='')
with open(os.path.join(root, name, "tmp_building.dat"), 'rb') as f:
seg, bounding = pickle.load(f)
with open(os.path.join(parse_root, name, "coordinate.dat"), 'rb') as f:
coor = pickle.load(f)
with open(os.path.join(parse_root, name, "reflectance.dat"),
'rb') as f:
o_reflectance = pickle.load(f)
with open(os.path.join(parse_root, name, "normal.dat"), 'rb') as f:
o_normal = pickle.load(f)
with open(os.path.join(parse_root, name, "head.dat"), 'rb') as f:
o_head = pickle.load(f)
o_depth = generate_depth_image(coor, o_head)
with open(os.path.join(parse_root, name, "head_info.dat"), 'rb') as f:
header = pickle.load(f)
norm, bounding2d, bounding3d, _, _ = bounding[nid]
bg = np.zeros(coor.shape)
bg = np.where(coor == bg, 0, 1)
bg_mask = (bg == 0).all(axis=2)
bg_mask = np.logical_not(bg_mask) #背景处为0， 其余为1
_, bounding2d, _, _, _ = bounding[nid]
if i == 0:
base_x, base_y, base_z = header["original_x"], header[
"original_y"], header["original_z"]
if bounding3d['x_min'][2] > bounding3d['x_max'][2]:
direction_z = bounding3d['x_min'] - bounding3d['x_max']
else:
direction_z = bounding3d['x_max'] - bounding3d['x_min']
ref_z = df_data['ref_h'] - header["original_z"]
mask_seg = np.where(seg == nid, 1, 0).reshape(-1)
idx_seg = mask_seg.nonzero()[0]
points_seg = coor.reshape(-1, 3)[idx_seg, :]
model = ransac.RANSAC()
m, inlier_mask, _ = model.run(points_seg,
inlier_thres=config.inliers_thres,
max_iterations=config.max_iterations)
norm_0 = norm
inliers = points_seg[inlier_mask]
T0, inv_T0, _b_min, _b_max = calculate_matrix(
inliers, m, norm_0, ref_z, direction_z)
facade_line = LineString([(_b_min[0] + base_x, _b_min[1] + base_y),
(_b_max[0] + base_x, _b_max[1] + base_y)
])
facade_lines.append(facade_line)
with open(
os.path.join('./', 'tmp_facade_line_{}.txt'.format(MODE)),
'a+') as file_handle: # .txt可以不自己新建,代码会自动新建
file_handle.write("{} {}".format(num_id, facade_line)) # 写入
file_handle.write('\n')
mask_seg = np.where(seg == nid, 1, 0).reshape(-1)
idx_seg = mask_seg.nonzero()[0]
points_seg = coor.reshape(-1, 3)[idx_seg, :]
points_seg[:, 0] += header["original_x"] - base_x
points_seg[:, 1] += header["original_y"] - base_y
points_seg[:, 2] += header["original_z"] - base_z
home_points_seg = np.ones((points_seg.shape[0], 4), dtype=np.float32)
home_points_seg[:, 0:3] = points_seg
proj_points_seg = inv_T0 @ home_points_seg.T
proj_points_seg = proj_points_seg.T[:, 0:3]
mask_bb = np.zeros(seg.shape)
#mask_bb[bounding2d['x_min'].x:bounding2d['x_max'].x,
# bounding2d['y_min'].y:bounding2d['y_max'].y] = 1
adjust = 0
mask_bb[max(0, bounding2d['x_min'].x -
adjust):min(bounding2d['x_max'].x + adjust, seg.shape[0]),
max(0, bounding2d['y_min'].y -
adjust):min(bounding2d['y_max'].y +
adjust, seg.shape[1])] = 1
mask_bb = np.logical_and(mask_bb, bg_mask).reshape(-1)
idx_bb = mask_bb.nonzero()[0]
points_bb = coor.reshape(-1, 3)[idx_bb, :]
reflectance_bb = o_reflectance.reshape(-1, )[idx_bb]
normal_bb = o_normal.reshape(-1, 3)[idx_bb, :]
depth_bb = o_depth.reshape(-1, )[idx_bb]
points_bb[:, 0] += header["original_x"] - base_x
points_bb[:, 1] += header["original_y"] - base_y
points_bb[:, 2] += header["original_z"] - base_z
heads_bb = o_head.reshape(-1, 3)[idx_bb, :]
heads_bb[:, 0] += header["original_x"] - base_x
heads_bb[:, 1] += header["original_y"] - base_y
heads_bb[:, 2] += header["original_z"] - base_z
home_points_bb = np.ones((points_bb.shape[0], 4), dtype=np.float32)
home_points_bb[:, 0:3] = points_bb
proj_points_bb = inv_T0 @ home_points_bb.T
proj_points_bb = proj_points_bb.T[:, 0:3]
range_max = np.max(proj_points_seg[:, 0])
range_min = np.min(proj_points_seg[:, 0])
mask_effect = np.where(proj_points_bb[:, 0] <= range_max, 1, 0)
mask_effect = np.where(proj_points_bb[:, 0] >= range_min, mask_effect,
0)
idx_effect = mask_effect.nonzero()[0]
proj_points = proj_points_bb[idx_effect]
remainder = points_bb[idx_effect]
remainder_head = heads_bb[idx_effect]
reflectances_effect.append(reflectance_bb[idx_effect])
normals_effect.append(normal_bb[idx_effect])
depths_effect.append(depth_bb[idx_effect])
remainder_heads.append(remainder_head)
remainder_points.append(remainder)
project_points.append(proj_points)
print(" ")
project_points = np.concatenate(project_points, axis=0)
remainder_points = np.concatenate(remainder_points, axis=0)
remainder_heads = np.concatenate(remainder_heads, axis=0)
reflectances_effect = np.concatenate(reflectances_effect, axis=0)
normals_effect = np.concatenate(normals_effect, axis=0)
depths_effect = np.concatenate(depths_effect, axis=0)
path_ply_original = os.path.join(save_root_2d, 'ply_original')
if not os.path.exists(path_ply_original):
os.makedirs(path_ply_original)
#cloud.from_array(remainder_points)
#pcl.save(cloud, os.path.join(path_ply_original, '{}_original.ply'.format(num_id)), format="ply")
proj_3d = project_points[:, 0:3]
average_height = np.median(proj_3d[:, 1])
index_front_threshold = np.where(proj_3d[:, 1] > average_height - 1.0, 1,
0).nonzero()[0]
#print("idx: ", index_front_threshold.shape)
proj_3d = proj_3d[index_front_threshold]
remainder_points = remainder_points[index_front_threshold]
remainder_heads = remainder_heads[
index_front_threshold] # remove points which behind facade with distance large than 1m.
reflectances_effect = reflectances_effect[index_front_threshold]
normals_effect = normals_effect[index_front_threshold]
depths_effect = depths_effect[index_front_threshold]
index_under_threshold = np.where(
remainder_points[:, 2] < np.min(remainder_points[:, 2]) + 2.0, 1,
0).nonzero()[0]
index_above_threshold = np.where(
remainder_points[:, 2] >= np.min(remainder_points[:, 2]) + 2.0, 1,
0).nonzero()[0]
under_points = remainder_points[index_under_threshold]
under_reflectances = reflectances_effect[index_under_threshold]
under_normals = normals_effect[index_under_threshold]
under_depths = depths_effect[index_under_threshold]
under_heads = remainder_heads[index_under_threshold]
above_heads = remainder_heads[index_above_threshold]
above_points = remainder_points[index_above_threshold]
above_reflectances = reflectances_effect[index_above_threshold]
above_normals = normals_effect[index_above_threshold]
above_depths = depths_effect[index_above_threshold]
under_proj_3d = proj_3d[index_under_threshold]
above_proj_3d = proj_3d[index_above_threshold]
index_under_behind_threshold = np.where(
under_proj_3d[:, 1] < average_height + 0.5, 1, 0).nonzero()[0]
index_under_front_threshold = np.where(
under_proj_3d[:, 1] >= average_height + 0.5, 1, 0).nonzero()[0]
under_proj_3d = under_proj_3d[index_under_behind_threshold]
occlusion_points_1 = under_points[index_under_front_threshold]
occlusion_heads_1 = under_heads[index_under_front_threshold]
under_points = under_points[index_under_behind_threshold]
under_reflectances = under_reflectances[index_under_behind_threshold]
under_normals = under_normals[index_under_behind_threshold]
under_depths = under_depths[index_under_behind_threshold]
index_above_behind_threshold = np.where(
above_proj_3d[:, 1] < average_height + 2.5, 1, 0).nonzero()[0]
index_above_front_threshold = np.where(
above_proj_3d[:, 1] >= average_height + 2.5, 1, 0).nonzero()[0]
occlusion_points_2 = above_points[index_above_front_threshold]
occlusion_heads_2 = above_heads[index_above_front_threshold]
above_proj_3d = above_proj_3d[index_above_behind_threshold]
above_points = above_points[index_above_behind_threshold]
above_reflectances = above_reflectances[index_above_behind_threshold]
above_normals = above_normals[index_above_behind_threshold]
above_depths = above_depths[index_above_behind_threshold]
remainder_points = np.concatenate((under_points, above_points), axis=0)
reflectances = np.concatenate((under_reflectances, above_reflectances),
axis=0)
normals = np.concatenate((under_normals, above_normals), axis=0)
depths = np.concatenate((under_depths, above_depths), axis=0)
m, inlier_mask, flag = model.run(remainder_points,
inlier_thres=config.inliers_thres,
max_iterations=config.max_iterations)
inliers = remainder_points[inlier_mask]
T_final, inv_T_final, b_min, b_max = calculate_matrix(
inliers, m, norm_0, ref_z, direction_z)
#facade_line = LineString([(b_min[0]+base_x,b_min[1]+base_y), (b_max[0]+base_x,b_max[1]+base_y)])
#facade_lines.append(facade_line)
if len(occlusion_points_1) > 0 and len(occlusion_points_2) > 0:
occlusion_points = np.concatenate(
[occlusion_points_1, occlusion_points_2])
occlusion_heads = np.concatenate(
[occlusion_heads_1, occlusion_heads_2])
elif len(occlusion_points_1) > 0:
occlusion_points = occlusion_points_1
occlusion_heads = occlusion_heads_1
else:
occlusion_points = occlusion_points_2
occlusion_heads = occlusion_heads_2
'''
path_ply_occlusion = os.path.join(save_root_2d, 'ply_occlusion')
if not os.path.exists(path_ply_occlusion):
os.makedirs(path_ply_occlusion)
cloud = pcl.PointCloud()
if len(occlusion_points) > 0:
cloud.from_array(occlusion_points)
pcl.save(cloud, os.path.join(path_ply_occlusion, '{}_occlusion.ply'.format(num_id)), format="ply")
path_ply_o_head = os.path.join(save_root_2d, 'ply_o_head')
if not os.path.exists(path_ply_o_head):
os.makedirs(path_ply_o_head)
cloud = pcl.PointCloud()
if len(occlusion_heads) > 0:
cloud.from_array(occlusion_heads)
pcl.save(cloud, os.path.join(path_ply_o_head, '{}_o_head.ply'.format(num_id)), format="ply")
'''
intersection_points = intersection_lines_plane(m,
occlusion_heads,
occlusion_points,
p0=inliers[0])
a, b, c, d = m
dis_indices = intersection_points[:,
0] * a + intersection_points[:,
1] * b + intersection_points[:,
2] * c + d
dis_inds = np.where(dis_indices < 0.2, 1, 0).nonzero()[0]
intersection_points = intersection_points[dis_inds]
'''
path_ply_intersection = os.path.join(save_root_2d, 'ply_intersection')
if not os.path.exists(path_ply_intersection):
os.makedirs(path_ply_intersection)
cloud = pcl.PointCloud()
if len(intersection_points) > 0:
cloud.from_array(intersection_points)
pcl.save(cloud, os.path.join(path_ply_intersection, '{}_intersection.ply'.format(num_id)), format="ply")
'''
#remainder_points = np.concatenate([remainder_points, intersection_points])
path_ply_in_depth = os.path.join(save_root_2d, 'ply_in_depth')
if not os.path.exists(path_ply_in_depth):
os.makedirs(path_ply_in_depth)
'''
tmp_remainder_points = remainder_points.copy()
tmp_remainder_points[:, 0] += base_x
tmp_remainder_points[:, 1] += base_y
tmp_remainder_points[:, 2] += base_z
'''
#cloud.from_array(remainder_points)
#pcl.save(cloud, os.path.join(path_ply_in_depth, '{}_in_depth.ply'.format(num_id)), format="ply")
#m, inlier_mask, flag = model.run(remainder_points, inlier_thres=config.inliers_thres, max_iterations=config.max_iterations)
#inliers = remainder_points[inlier_mask]
#if not flag:
# T, inv_T = T0, inv_T0
#else:
#T_final, inv_T_final, b_min, b_max = calculate_matrix(inliers, m, norm_0, ref_z, direction_z)
homo_inliers = np.ones((inliers.shape[0], 4), dtype=np.float32)
homo_inliers[:, 0:3] = inliers
proj_3d_inliers = inv_T_final @ homo_inliers.T
proj_3d_inliers = proj_3d_inliers.T[:, 0:3]
ref_plane = np.mean(proj_3d_inliers[:, 1])
homo_remainder_points = np.ones((remainder_points.shape[0], 4),
dtype=np.float32)
homo_remainder_points[:, 0:3] = remainder_points
proj_3d = inv_T_final @ homo_remainder_points.T
proj_3d = proj_3d.T[:, 0:3]
#proj_3d = np.concatenate((under_proj_3d, above_proj_3d), axis=0)
homo_intersection_points = np.ones((intersection_points.shape[0], 4),
dtype=np.float32)
homo_intersection_points[:, 0:3] = intersection_points
proj_intersection_3d = inv_T_final @ homo_intersection_points.T
proj_intersection_3d = proj_intersection_3d.T[:, 0:3]
range_x_min = np.min(proj_3d[:, 0])
range_x_max = np.max(proj_3d[:, 0])
range_y_min = np.min(proj_3d[:, 2])
range_y_max = np.max(proj_3d[:, 2])
#print(range_x_min, range_x_max, range_y_min, range_y_max)
bg = np.min(proj_3d[:, 1]) - 0.1
img_2d = np.ones(
(int(math.ceil(range_y_max - range_y_min) * config.SCALE) + 1,
int(math.ceil((range_x_max - range_x_min) * config.SCALE)) + 1),
dtype=np.float32) * bg
fill_flag = np.zeros(img_2d.shape)
occlusion_mask = np.zeros(img_2d.shape)
density_img = np.zeros(img_2d.shape, dtype=np.uint16)
img_reflectance = np.ones(img_2d.shape) * (np.min(reflectances) - 1)
img_norm = np.zeros((*img_2d.shape, 3))
img_original_depth = np.zeros(img_2d.shape)
density = float(len(proj_3d)) / (img_2d.shape[0] * img_2d.shape[1])
for i, (x, z, y) in enumerate(proj_3d):
x = int(round((x - range_x_min) * config.SCALE))
y = int(round((y - range_y_min) * config.SCALE))
if fill_flag[img_2d.shape[0]-y-1,x] == 0.5 and \
abs(img_2d[img_2d.shape[0]-y-1,x]-average_height) < abs(z-average_height):
pass
else:
img_2d[img_2d.shape[0] - y - 1, x] = z
img_reflectance[img_2d.shape[0] - y - 1, x] = reflectances[i]
img_norm[img_2d.shape[0] - y - 1, x, :] = normals[i]
img_original_depth[img_2d.shape[0] - y - 1, x] = depths[i]
fill_flag[img_2d.shape[0] - y - 1, x] = 0.5
density_img[img_2d.shape[0] - y - 1, x] += 1
path_depth_dat = os.path.join(save_root_2d, 'depth_dat')
if not os.path.exists(path_depth_dat):
os.makedirs(path_depth_dat)
with open(os.path.join(path_depth_dat, '{}_depth.dat'.format(num_id)),
'wb') as f:
pickle.dump(img_2d.copy(), f)
path_info = os.path.join(save_root_2d, 'info')
if not os.path.exists(path_info):
os.makedirs(path_info)
with open(os.path.join(path_info, '{}_info.dat'.format(num_id)),
'wb') as f:
pickle.dump(
{
'size': img_2d.shape,
'density': density, # density of point cloud
'resolution': 1.0 / config.SCALE,
'left_edge': range_x_min,
'buttom_edge': range_y_min,
'trans_i2o': T_final,
'trans_o2i': inv_T_final,
'original_x': base_x,
'original_y': base_y,
'original_z': base_z,
'ref_road': ref_z,
'ref_plane': ref_plane
},
f)
#cloud = pcl.PointCloud()
#cloud.from_array(remainder_points)
#pcl.save(cloud, os.path.join(save_root_2d, 'ply', '{}.ply'.format(num_id)), format="ply")
img_2d_save1 = utils.rescale(img_2d)
img_2d_save1 = np.round(img_2d_save1 * 255).astype(np.uint8)
density_img_save1 = utils.rescale(density_img)
density_img_save1 = np.round(density_img_save1 * 255).astype(np.uint8)
path_camera_depth = os.path.join(save_root_2d, 'camera_depth')
if not os.path.exists(path_camera_depth):
os.makedirs(path_camera_depth)
with open(os.path.join(path_camera_depth, '{}.dat'.format(num_id)),
'wb') as f:
pickle.dump(img_original_depth, f)
path_normal = os.path.join(save_root_2d, 'normal')
if not os.path.exists(path_normal):
os.makedirs(path_normal)
io.imsave(os.path.join(path_normal, '{}.png'.format(num_id)), img_norm)
path_reflectance = os.path.join(save_root_2d, 'reflectance')
if not os.path.exists(path_reflectance):
os.makedirs(path_reflectance)
with open(os.path.join(path_reflectance, '{}.dat'.format(num_id)),
'wb') as f:
pickle.dump(img_reflectance, f)
path_depth_img = os.path.join(save_root_2d, 'depth_image')
if not os.path.exists(path_depth_img):
os.makedirs(path_depth_img)
path_density_img = os.path.join(save_root_2d, 'density_image')
if not os.path.exists(path_density_img):
os.makedirs(path_density_img)
path_geometry_image = os.path.join(save_root_2d, 'geometry_image')
if not os.path.exists(path_geometry_image):
os.makedirs(path_geometry_image)
io.imsave(os.path.join(path_depth_img, '{}.png'.format(num_id)),
img_2d_save1)
io.imsave(os.path.join(path_density_img, '{}.png'.format(num_id)),
density_img_save1)
io.imsave(os.path.join(path_geometry_image, '{}.png'.format(num_id)),
fill_flag)
path_occl_mask = os.path.join(save_root_2d, 'occlusion_mask')
if not os.path.exists(path_occl_mask):
os.makedirs(path_occl_mask)
##### for occlusion compensation
avg_reflectance = reflectances.mean()
avg_depth = depths.mean()
avg_norm = normals.mean(axis=0)
for i, (x, z, y) in enumerate(proj_intersection_3d):
if z < -1.0:
continue
if x < range_x_min or x > range_x_max:
continue
if y < range_y_min or y > range_y_max:
continue
x = int(round((x - range_x_min) * config.SCALE))
y = int(round((y - range_y_min) * config.SCALE))
occlusion_mask[img_2d.shape[0] - y - 1, x] = 0.5
'''
#if (fill_flag[img_2d.shape[0]-y-1,x] != 0 or fill_flag[img_2d.shape[0]-y-1,max(0, x-1)] != 0 or
# fill_flag[img_2d.shape[0]-y-1,min(x-1, img_2d.shape[1]-1)]) != 0:
if fill_flag[img_2d.shape[0]-y-1,x] != 0:
continue
if fill_flag[img_2d.shape[0]-y-1,x] == 0.5 and \
abs(img_2d[img_2d.shape[0]-y-1,x]-average_height) < abs(z-average_height):
pass
else:
img_2d[img_2d.shape[0]-y-1,x] = z
img_reflectance[img_2d.shape[0]-y-1,x] = avg_reflectance
img_norm[img_2d.shape[0]-y-1,x, :] = avg_norm
img_original_depth[img_2d.shape[0]-y-1,x] = avg_depth
fill_flag[img_2d.shape[0]-y-1,x] = 0.5
density_img[img_2d.shape[0]-y-1,x] += 1
'''
io.imsave(os.path.join(path_occl_mask, '{}.png'.format(num_id)),
occlusion_mask)
'''
path_depth_dat = os.path.join(save_root_2d, 'occl_comp', 'depth_dat')
if not os.path.exists(path_depth_dat):
os.makedirs(path_depth_dat)
with open(os.path.join(path_depth_dat, '{}_depth.dat'.format(num_id)), 'wb') as f:
pickle.dump(img_2d.copy(), f)
img_2d_save2 = utils.rescale(img_2d)
img_2d_save2 = np.round(img_2d_save2*255).astype(np.uint8)
density_img_save2 = utils.rescale(density_img)
density_img_save2 = np.round(density_img_save2*255).astype(np.uint8)
path_camera_depth = os.path.join(save_root_2d, 'occl_comp', 'camera_depth')
if not os.path.exists(path_camera_depth):
os.makedirs(path_camera_depth)
with open(os.path.join(path_camera_depth, '{}.dat'.format(num_id)), 'wb') as f:
pickle.dump(img_original_depth, f)
path_normal = os.path.join(save_root_2d, 'occl_comp', 'normal')
if not os.path.exists(path_normal):
os.makedirs(path_normal)
io.imsave(os.path.join(path_normal, '{}.png'.format(num_id)), img_norm)
path_reflectance = os.path.join(save_root_2d, 'occl_comp', 'reflectance')
if not os.path.exists(path_reflectance):
os.makedirs(path_reflectance)
with open(os.path.join(path_reflectance, '{}.dat'.format(num_id)), 'wb') as f:
pickle.dump(img_reflectance, f)
path_depth_img = os.path.join(save_root_2d, 'occl_comp', 'depth_image')
if not os.path.exists(path_depth_img):
os.makedirs(path_depth_img)
path_density_img = os.path.join(save_root_2d, 'occl_comp', 'density_image')
if not os.path.exists(path_density_img):
os.makedirs(path_density_img)
path_geometry_image = os.path.join(save_root_2d, 'occl_comp', 'geometry_image')
if not os.path.exists(path_geometry_image):
os.makedirs(path_geometry_image)
io.imsave(os.path.join(path_depth_img, '{}.png'.format(num_id)), img_2d_save2)
io.imsave(os.path.join(path_density_img, '{}.png'.format(num_id)), density_img_save2)
io.imsave(os.path.join(path_geometry_image, '{}.png'.format(num_id)), fill_flag)
'''
print("done...{}".format(num_id))
all_X, all_y, all_w = [], [], []
for i in range(5):
_, X_fold, _, y_fold, _, w_fold = train_test_split(X, y, w, train_size=0.5, random_state=i)
X_pred = load_predictions("stack/*-fold%d.npy" % i)
X_fold = np.hstack((X_fold, X_pred))
all_X.append(X_fold)
all_y.append(y_fold)
all_w.append(w_fold)
X = np.vstack(all_X)
y = np.concatenate(all_y)
w = np.concatenate(all_w)
clf = Classifier(**params)
w = rescale(w)
w = rebalance(y, w)
try:
clf.fit(X, y, sample_weight=w)
except:
clf.fit(X, y)
# And make a submussion
print "Making submission..."
X_test, _, _, _ = load_test()
X_pred = load_predictions("stack/*-test.npy")
X_test = np.hstack((X_test, X_pred))
make_submission(clf, threshold, "output-stacking.csv", X_test=X_test)
def get(self, X1, X2=None):
# Compute the kernel between two matrix
X1, X2 = rescale(np.exp(self.log_b_), X1, X2)
return np.exp(self.log_b_ * 2 - 0.5 * sqdist(X1, X2))
# Load data
print '\nLoading data...\n'
data = wave.read(song_file)
sample_rate = data[0]
data = numpy.asarray(data[1])
num_samples = len(data)
example_length = frame_length*seq_length
# Normalize?
if normalize == True:
print '\nNormalizing...\n'
data_mean = numpy.mean(data)
data_min = numpy.min(data)
data_max = numpy.max(data)
data = rescale(data-data_mean, -1, 1)
print "subtracted training mean and normalized data to [-1,1]"
else:
print "using unnormalized data"
# Make sure data will be the right length for reshaping
num_examples = len(data) // example_length
leftover = len(data) % example_length
if leftover < frame_length:
data_to_use = data[:((num_examples-1)*example_length)+frame_length]
else:
data_to_use = data[:(num_examples*example_length)+frame_length]
# Reshape
#chars = data.reshape(len(data)/frame_length, frame_length) #list(set(data))
#vocab_size = len(chars)
G.load_state_dict(
torch.load("Checkpoints/G_22.pt", map_location=torch.device('cpu')))
GR.load_state_dict(
torch.load("Checkpoints/GR_7.pt", map_location=torch.device('cpu')))
img = plt.imread(path_i)
faces = detect_face(img)
if len(faces) == 0:
print("No faces detected")
exit()
resz = cv2.resize(faces[0], (100, 100))
plt.imsave("out_ld.png", resz)
resz = resz.reshape(1, 100, 100, 3)
resz = np.transpose(resz, (0, 3, 1, 2))
resz = torch.from_numpy(resz)
resz = resz.float()
inp = scale(resz)
out1 = infer(G, inp)
out2 = infer(GR, inp)
inp = rescale(inp)
out1 = rescale(out1)
out2 = rescale(out2)
w = 0.5
out = out1 * w + out2 * (1 - w)
imsave(out[0], "out_hd.png")
def export_cxs(tested, fname, negmult):
ut.write_tab_file([(t[0], t[1], ut.rescale(float(t[2]),negmult)) for t in
tested], fname)
frm_list = frm_list[0:n_frame]
i = -1
for frm in frm_list:
i += 1
found_true = true_detection_list[i]
found_filtered = []
found, w = hog_obj.detectMultiScale(frm, **hog_par)
for r in found:
inside = False
for q in found:
if utils.is_inside(r, q):
inside = True
break
if not inside:
found_filtered.append(r)
found_hog = utils.rescale(found_filtered)
hog_detection_list.append(found_hog)
if do_draw:
frm = cv2.cvtColor(frm, cv2.COLOR_GRAY2BGR)
# utils.draw_detections(frm, found, (0, 0, 255), 1)
# utils.draw_detections(frm, found_filtered, (0, 0, 255), 3)
utils.draw_detections(frm, found_hog, (0, 0, 255), 3)
utils.draw_detections(frm, found_true, (0, 255, 0), 1)
cv2.imshow('People Detector', frm)
cv2.waitKey()
"""Performances indices"""
if do_perf:
precision, recall, f_score = utils.get_performances(hog_detection_list, true_detection_list, n_frame)
print precision, recall, f_score
def merge_one_image(depth, density, reflectance):
reflectance = utils.rescale(reflectance)
reflectance *= 255
reflectance = reflectance.astype(np.uint8)
img = cv2.merge([depth, density, reflectance])
return img
teaser['user']['photos'][0]['url']) for teaser in teasers}
for rec in recs:
user = rec['user']
rec_id = user['_id']
print(f"{Fore.GREEN}Analyzing {user['name']} ({rec_id}) ...")
for rec_photo in user['photos']:
print(f"\t{Fore.BLUE}Image {rec_photo['url']}")
rec_img = get_image(rec_photo['url'])
for (tea_id, tea_img) in teaser_images.items():
print(
f"\t\t{Fore.BLACK}{Style.BRIGHT}Comparing with teaser {tea_id}")
if (compare(rec_img, tea_img)):
for i, rec_found_img in enumerate([get_image(pic['url']) for pic in user['photos']]):
cv2.imshow(f"Rec {i}", rescale(rec_found_img, 30))
cv2.imshow("Teaser", rescale(tea_img, 30))
action = cv2.waitKey(0)
cv2.destroyAllWindows()
if (action == KEY_LIKE or action == KEY_LIKE - 32):
like(rec_id)
if (action == KEY_PASS or action == KEY_PASS - 32):
dislike(rec_id)
if (action == KEY_SUPERLIKE or action == KEY_SUPERLIKE - 32):
superlike(rec_id)
print("\n")
# Deinit Colorama
deinit()
# Load data
print '\nLoading data...'
data = wave.read(song_file)
sample_rate = data[0]
data = numpy.asarray(data[1])
num_samples = len(data)
example_length = frame_length*seq_length
# Normalize?
if normalize == True:
print '\nNormalizing -1 to 1...'
data_mean = numpy.mean(data)
data_min = numpy.min(data)
data_max = numpy.max(data)
data = rescale(data-data_mean, -1, 1, data_min, data_max)
print 'subtracted training mean and normalized data to [-1,1]'
elif normalize == 'stdev':
data_stdev = numpy.std(data)
data = data/data_stdev
print 'divided by standard deviation'
else:
print 'using unnormalized data'
# Make sure data will be the right length for reshaping
print '\nGetting examples...'
num_examples = 0
data_to_use = []
if example_shift == False:
num_examples = len(data)-frame_length // example_length
data_to_use = data[:num_examples*example_length + frame_length]
def construct_UP(inp_df,
output_size,
n_classes=2,
P=20,
preprocess='inception',
np_save_file=None,
save_with_indices=None,
return_Data=False,
log_save=False,
conv_to_uint8=True,
min_max_exposure=True):
batch_input = inp_df.copy() # copy to prevent modifying original df
images_list = []
labels_list = []
nu_list = []
single_labels = []
pd_indices = []
LL_view_list = []
no_view_list = []
derivative_view = []
for idx, row in batch_input.iterrows():
_dicom, _label, _xmin, _xmax, class_0_fill = row[[
'dicom', 'label', 'xmin', 'xmax', 'class_0_fill'
]]
a = pydicom.dcmread(_dicom)
try:
if a.DerivationDescription: #derivate view
derivative_view.append(_dicom)
continue
except AttributeError:
pass
try:
c_view = a.ViewPosition
if c_view == 'LL':
LL_view_list.append(_dicom)
continue
if c_view not in ['PA', 'AP']:
no_view_list.append(_dicom)
continue
except AttributeError:
continue
_img = a.pixel_array
bs = a.BitsStored
_img = erase_borders(_img)
if min_max_exposure:
_img = rescale(_img, 0, 1, np.min(_img),
np.max(_img)).astype(np.float32)
if a.PhotometricInterpretation == 'MONOCHROME1':
_img = skimage.util.invert(_img, signed_float=False)
#All pixels ABOVE 99th percentile set TO 99th percentile (getting around super bright labels)
_img[_img > np.percentile(_img, 99)] = np.percentile(_img, 99)
#Rescaling again after pixel cutt-off
_img = rescale(_img, 0, 1, np.min(_img),
np.max(_img)).astype(np.float32)
if conv_to_uint8:
_img = skimage.img_as_uint(_img)
else:
_img = exposure.rescale_intensity(
_img,
in_range=('uint' +
str(bs))) #most, if not all, are of type uint16
if conv_to_uint8:
_img = skimage.img_as_uint(_img)
if a.PhotometricInterpretation == 'MONOCHROME1':
_img = cv2.bitwise_not(_img)
_img = transform.resize(_img, (output_size, output_size),
mode='reflect',
anti_aliasing=True) #img_as_float
_img = np.stack([_img, _img, _img], axis=-1)
images_list.append(_img)
_label_arr = np.zeros((P, P, n_classes))
single_labels.append([_label])
_label_arr[..., 0] = class_0_fill
# For boxed cases
_label_arr[:, _xmin:_xmax, _label] = 1
labels_list.append(_label_arr)
#TODO: change the nu for PTX (0 or 1)
#all chest cases have laterality, and negatives cases should have class 0 in all patches
#nu_list.append(_label) # only positives cases with laterality known have nu of 1
nu_list.append(1) # all cases have nu of 1.
#nu_list.append(np.logical_not(_label)) # NL with nu of 1 and PTX with nu of 0
# nu_list.append(0) # no cases with localization
pd_indices.append(idx)
images_list = np.array(images_list, np.float32)
pd_indices = np.array(pd_indices, np.int32)
preprocess_methods = {'resnet', 'inception', 'none'}
if preprocess not in preprocess_methods:
raise KeyError('Must select a preprocess method from {}'.format(
preprocess_methods))
if preprocess == 'inception':
images_list = (images_list -
0.5) * 2 # get values [-1, 1]. Shift and scale.
if preprocess == 'resnet': #substract mean instead for Resnet preprocessing
# RGB values from tensorflow resnet divded by uint8 pixel range (255)
images_list[..., 0] -= 123.68 / 255
images_list[..., 1] -= 116.78 / 255
images_list[..., 2] -= 103.94 / 255
labels_list = np.array(labels_list, np.float32)
nu_list = np.array(nu_list, np.float32)
# single labels as one-hot
single_labels = sklearn.preprocessing.MultiLabelBinarizer(
np.arange(n_classes)).fit_transform(single_labels)
single_labels = np.array(single_labels, np.float32)
nu_list = nu_list.astype(np.int64)
#image list, labels, and single_labels list as float32, and nu as int64
if np_save_file is not None:
np.savez(np_save_file,
images=images_list,
labels=labels_list,
nus=nu_list,
singles=single_labels)
if save_with_indices is not None:
np.savez(save_with_indices,
images=images_list,
labels=labels_list,
nus=nu_list,
singles=single_labels,
pd_indices=pd_indices)
if log_save:
with open('LL_view.txt', 'w') as f:
for i in LL_view_list:
f.write(i + '\n')
with open('no_view.txt', 'w') as f:
for i in no_view_list:
f.write(i + '\n')
with open('derivative_view.txt', 'w') as f:
for i in derivative_view:
f.write(i + '\n')
if return_Data:
return Data_indexed(images_list, labels_list, nu_list, single_labels,
pd_indices)
# Get the rectangle with the min area
rect = cv2.minAreaRect(frame)
frame = cv2.boxPoints(rect)
frame = np.int0(frame)
# Get the 2 main angles of the frame
angles = ut.get_angles(frame)
rect2 = (rect[0], rect[1], angles[-1])
img_croped = ut.crop_min_area_rect(color, rect2)
cv2.imshow("Cropped", img_croped)
cv2.waitKey(0)
# As we scale the image, we need to scale back the contour to the original image
frame_orig = list(
map(lambda x: ut.rescale(x, size_ini, size_end), frame))
# Save data to export
frames.append([ut.rad2deg(angles[-1]), frame_orig])
# Save the data in a pickle file
ut.bbox_to_pkl(frames, fname='frames', folder='pkl')
"""
################################## TASK 3: Filter keypoints ####################################
"""
# Check for training database file
if not os.path.exists(PICKLE_MUSEUM_DATASET):
logger.info("Creating pickle database for museum dataset...")
db_museum = ut.create_db(TRAIN_MUSEUM_DIR, FEATURES, candidates)
ut.save_db(db_museum, PICKLE_MUSEUM_DATASET)
else:
def calculate_cell_parameters(cell_info):
brightness = (int(cell_info[0]) + int(cell_info[1]) +
int(cell_info[2])) / 3
width = utils.rescale(brightness, 0, 255, 0, cell_w)
height = utils.rescale(brightness, 0, 255, 0, cell_h)
return width, height
def export_ints(tested, fname, negmult, header):
ut.write_tab_file([header] + [[t[0], t[1], ut.rescale(float(t[2]),negmult)]
+ list(t[3:]) for t in tested], fname)
def query_nid(num_id):
segs = match[num_id]
project_points = list()
remainder_points = list()
base_line = segs[0]
index = df[df['id'] == num_id].index[0]
df_data = df.loc[index]
#df_data = df[df['id']==num_id]
#inv_T = np.zeros((4,4))
direction = False
norm_0 = None
direction_z = None
ref_z = None
for i, (name, nid) in enumerate(segs):
print('\rseg: {}/{}'.format(i, len(segs) - 1), end='')
with open(os.path.join(root, name, "tmp_building.dat"), 'rb') as f:
seg, bounding = pickle.load(f)
with open(os.path.join(parse_root, name, "coordinate.dat"), 'rb') as f:
coor = pickle.load(f)
with open(os.path.join(parse_root, name, "head_info.dat"), 'rb') as f:
header = pickle.load(f)
norm, bounding2d, bounding3d, _, _ = bounding[nid]
bg = np.zeros(coor.shape)
bg = np.where(coor == bg, 0, 1)
bg_mask = (bg == 0).all(axis=2)
bg_mask = np.logical_not(bg_mask) #背景处为0， 其余为1
_, bounding2d, _, _, _ = bounding[nid]
if i == 0:
base_x, base_y, base_z = header["original_x"], header[
"original_y"], header["original_z"]
if bounding3d['x_min'][2] > bounding3d['x_max'][2]:
direction_z = bounding3d['x_min'] - bounding3d['x_max']
else:
direction_z = bounding3d['x_max'] - bounding3d['x_min']
ref_z = df_data['ref_h'] - header["original_z"]
mask_seg = np.where(seg == nid, 1, 0).reshape(-1)
idx_seg = mask_seg.nonzero()[0]
points_seg = coor.reshape(-1, 3)[idx_seg, :]
model = ransac.RANSAC()
m, inlier_mask, _ = model.run(points_seg,
inlier_thres=config.inliers_thres,
max_iterations=config.max_iterations)
norm_0 = norm
inliers = points_seg[inlier_mask]
T0, inv_T0, _, _ = calculate_matrix(inliers, m, norm_0, ref_z,
direction_z)
'''
homo_inliers = np.ones((inliers.shape[0],4), dtype=np.float32)
homo_inliers[:, 0:3] = inliers
proj_3d = inv_T0 @ homo_inliers.T
proj_3d = proj_3d.T[:, 0:3]
xyz0 = T0[:3]
xyz0[:, 0] += xyz0[:, 3]
xyz0[:, 1] += xyz0[:, 3]
xyz0[:, 2] += xyz0[:, 3]
xyz0 = xyz0.T
xyz0_aug = np.ones((xyz0.shape[0], 4))
xyz0_aug[:, 0:3] = xyz0
pro_xyz0 = inv_T0 @ xyz0_aug.T
pro_xyz0 = pro_xyz0.T[:,:3]
fig = plt.figure('graph')
ax = fig.add_subplot(111, projection='3d')
ax.set_title(r'3d')
plt.plot(proj_3d[:,0], proj_3d[:,1], proj_3d[:,2], '.', color='coral')
plt.plot([pro_xyz0[0, 0], pro_xyz0[3, 0]], [pro_xyz0[0, 1], pro_xyz0[3, 1]], [pro_xyz0[0, 2], pro_xyz0[3, 2]], '-r', label='x')
plt.plot([pro_xyz0[1, 0], pro_xyz0[3, 0]], [pro_xyz0[1, 1], pro_xyz0[3, 1]], [pro_xyz0[1, 2], pro_xyz0[3, 2]], '-g', label='y')
plt.plot([pro_xyz0[2, 0], pro_xyz0[3, 0]], [pro_xyz0[2, 1], pro_xyz0[3, 1]], [pro_xyz0[2, 2], pro_xyz0[3, 2]], '-b', label='z')
ax.legend()
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.text(0, 0, 0, 'x') # (0,0,0)
ax.view_init(60,0)
plt.show()
raise InterruptedError()
'''
mask_seg = np.where(seg == nid, 1, 0).reshape(-1)
idx_seg = mask_seg.nonzero()[0]
points_seg = coor.reshape(-1, 3)[idx_seg, :]
points_seg[:, 0] += header["original_x"] - base_x
points_seg[:, 1] += header["original_y"] - base_y
points_seg[:, 2] += header["original_z"] - base_z
home_points_seg = np.ones((points_seg.shape[0], 4), dtype=np.float32)
home_points_seg[:, 0:3] = points_seg
proj_points_seg = inv_T0 @ home_points_seg.T
proj_points_seg = proj_points_seg.T[:, 0:3]
mask_bb = np.zeros(seg.shape)
#mask_bb[bounding2d['x_min'].x:bounding2d['x_max'].x,
# bounding2d['y_min'].y:bounding2d['y_max'].y] = 1
adjust = 100
mask_bb[max(0, bounding2d['x_min'].x -
adjust):min(bounding2d['x_max'].x + adjust, seg.shape[0]),
max(0, bounding2d['y_min'].y -
adjust):min(bounding2d['y_max'].y +
adjust, seg.shape[1])] = 1
mask_bb = np.logical_and(mask_bb, bg_mask).reshape(-1)
idx_bb = mask_bb.nonzero()[0]
points_bb = coor.reshape(-1, 3)[idx_bb, :]
points_bb[:, 0] += header["original_x"] - base_x
points_bb[:, 1] += header["original_y"] - base_y
points_bb[:, 2] += header["original_z"] - base_z
home_points_bb = np.ones((points_bb.shape[0], 4), dtype=np.float32)
home_points_bb[:, 0:3] = points_bb
proj_points_bb = inv_T0 @ home_points_bb.T
proj_points_bb = proj_points_bb.T[:, 0:3]
range_max = np.max(proj_points_seg[:, 0])
range_min = np.min(proj_points_seg[:, 0])
mask_effect = np.where(proj_points_bb[:, 0] <= range_max, 1, 0)
mask_effect = np.where(proj_points_bb[:, 0] >= range_min, mask_effect,
0)
idx_effect = mask_effect.nonzero()[0]
proj_points = proj_points_bb[idx_effect]
remainder = points_bb[idx_effect]
#cloud = pcl.PointCloud()
#loud.from_array(remainder)
#pcl.save(cloud, os.path.join(save_root_2d, 'ply', '{}_sub_{}.ply'.format(num_id, i)), format="ply")
remainder_points.append(remainder)
project_points.append(proj_points)
print(" ")
project_points = np.concatenate(project_points, axis=0)
remainder_points = np.concatenate(remainder_points, axis=0)
path_ply_original = os.path.join(save_root_2d, 'ply_original')
if not os.path.exists(path_ply_original):
os.makedirs(path_ply_original)
cloud = pcl.PointCloud()
'''
tmp_remainder_points = remainder_points.copy()
tmp_remainder_points[:, 0] += base_x
tmp_remainder_points[:, 1] += base_y
tmp_remainder_points[:, 2] += base_z
'''
cloud.from_array(remainder_points)
pcl.save(cloud,
os.path.join(path_ply_original, '{}_original.ply'.format(num_id)),
format="ply")
proj_3d = project_points[:, 0:3]
average_height = np.median(proj_3d[:, 1])
index_front_threshold = np.where(proj_3d[:, 1] > average_height - 1.0, 1,
0).nonzero()[0]
#print("idx: ", index_front_threshold.shape)
proj_3d = proj_3d[index_front_threshold]
remainder_points = remainder_points[index_front_threshold]
index_under_threshold = np.where(
remainder_points[:, 2] < np.min(remainder_points[:, 2]) + 2.0, 1,
0).nonzero()[0]
index_above_threshold = np.where(
remainder_points[:, 2] >= np.min(remainder_points[:, 2]) + 2.0, 1,
0).nonzero()[0]
under_points = remainder_points[index_under_threshold]
above_points = remainder_points[index_above_threshold]
under_proj_3d = proj_3d[index_under_threshold]
above_proj_3d = proj_3d[index_above_threshold]
index_under_behind_threshold = np.where(
under_proj_3d[:, 1] < average_height + 0.5, 1, 0).nonzero()[0]
under_proj_3d = under_proj_3d[index_under_behind_threshold]
under_points = under_points[index_under_behind_threshold]
index_above_behind_threshold = np.where(
above_proj_3d[:, 1] < average_height + 2.5, 1, 0).nonzero()[0]
above_proj_3d = above_proj_3d[index_above_behind_threshold]
above_points = above_points[index_above_behind_threshold]
remainder_points = np.concatenate((under_points, above_points), axis=0)
path_ply_in_depth = os.path.join(save_root_2d, 'ply_in_depth')
if not os.path.exists(path_ply_in_depth):
os.makedirs(path_ply_in_depth)
cloud = pcl.PointCloud()
'''
tmp_remainder_points = remainder_points.copy()
tmp_remainder_points[:, 0] += base_x
tmp_remainder_points[:, 1] += base_y
tmp_remainder_points[:, 2] += base_z
'''
cloud.from_array(remainder_points)
pcl.save(cloud,
os.path.join(path_ply_in_depth, '{}_in_depth.ply'.format(num_id)),
format="ply")
m, inlier_mask, flag = model.run(remainder_points,
inlier_thres=config.inliers_thres,
max_iterations=config.max_iterations)
inliers = remainder_points[inlier_mask]
#if not flag:
# T, inv_T = T0, inv_T0
#else:
T_final, inv_T_final, b_min, b_max = calculate_matrix(
inliers, m, norm_0, ref_z, direction_z)
path_ply_inliers = os.path.join(save_root_2d, 'ply_inliers')
if not os.path.exists(path_ply_inliers):
os.makedirs(path_ply_inliers)
'''
tmp_inliers = inliers.copy()
tmp_inliers[:, 0] += base_x
tmp_inliers[:, 1] += base_y
tmp_inliers[:, 2] += base_z
'''
cloud.from_array(inliers)
pcl.save(cloud,
os.path.join(path_ply_inliers, '{}_inliers.ply'.format(num_id)),
format="ply")
homo_inliers = np.ones((inliers.shape[0], 4), dtype=np.float32)
homo_inliers[:, 0:3] = inliers
proj_3d_inliers = inv_T_final @ homo_inliers.T
proj_3d_inliers = proj_3d_inliers.T[:, 0:3]
ref_plane = np.mean(proj_3d_inliers[:, 1])
homo_remainder_points = np.ones((remainder_points.shape[0], 4),
dtype=np.float32)
homo_remainder_points[:, 0:3] = remainder_points
proj_3d = inv_T_final @ homo_remainder_points.T
proj_3d = proj_3d.T[:, 0:3]
#proj_3d = np.concatenate((under_proj_3d, above_proj_3d), axis=0)
range_x_min = np.min(proj_3d[:, 0])
range_x_max = np.max(proj_3d[:, 0])
range_y_min = np.min(proj_3d[:, 2])
range_y_max = np.max(proj_3d[:, 2])
#print(range_x_min, range_x_max, range_y_min, range_y_max)
bg = np.min(proj_3d[:, 1]) - 0.1
img_2d = np.ones(
(int(math.ceil(range_y_max - range_y_min) * config.SCALE) + 1,
int(math.ceil((range_x_max - range_x_min) * config.SCALE)) + 1),
dtype=np.float32) * bg
fill_flag = np.zeros(img_2d.shape)
density_img = np.zeros(img_2d.shape, dtype=np.uint16)
density = float(len(proj_3d)) / (img_2d.shape[0] * img_2d.shape[1])
for i, (x, z, y) in enumerate(proj_3d):
x = int(round((x - range_x_min) * config.SCALE))
y = int(round((y - range_y_min) * config.SCALE))
if fill_flag[img_2d.shape[0]-y-1,x] == 0.5 and \
abs(img_2d[img_2d.shape[0]-y-1,x]-average_height) < abs(z-average_height):
pass
else:
img_2d[img_2d.shape[0] - y - 1, x] = z
fill_flag[img_2d.shape[0] - y - 1, x] = 0.5
density_img[img_2d.shape[0] - y - 1, x] += 1
path_depth_dat = os.path.join(save_root_2d, 'depth_dat')
if not os.path.exists(path_depth_dat):
os.makedirs(path_depth_dat)
with open(os.path.join(path_depth_dat, '{}_depth.dat'.format(num_id)),
'wb') as f:
pickle.dump(img_2d.copy(), f)
path_info = os.path.join(save_root_2d, 'info')
if not os.path.exists(path_info):
os.makedirs(path_info)
with open(os.path.join(path_info, '{}_info.dat'.format(num_id)),
'wb') as f:
pickle.dump(
{
'size': img_2d.shape,
'density': density, # density of point cloud
'resolution': 1.0 / config.SCALE,
'left_edge': range_x_min,
'buttom_edge': range_y_min,
'trans_i2o': T_final,
'trans_o2i': inv_T_final,
'original_x': base_x,
'original_y': base_y,
'original_z': base_z,
'ref_road': ref_z,
'ref_plane': ref_plane
},
f)
#cloud = pcl.PointCloud()
#cloud.from_array(remainder_points)
#pcl.save(cloud, os.path.join(save_root_2d, 'ply', '{}.ply'.format(num_id)), format="ply")
img_2d = utils.rescale(img_2d)
img_2d = np.round(img_2d * 255).astype(np.uint8)
density_img = utils.rescale(density_img)
density_img = np.round(density_img * 255).astype(np.uint8)
#img_2d = np.where(img_2d<1.0, img_2d, 0.999)
#img_2d = np.where(img_2d>0.001, img_2d, 0.001)
#with open(os.path.join(save_root_2d, '{}_project3d.dat'.format(num_id)), 'wb') as f:
# pickle.dump(proj_3d, f)
path_depth_img = os.path.join(save_root_2d, 'depth_image')
if not os.path.exists(path_depth_img):
os.makedirs(path_depth_img)
path_density_img = os.path.join(save_root_2d, 'density_image')
if not os.path.exists(path_density_img):
os.makedirs(path_density_img)
path_geometry_image = os.path.join(save_root_2d, 'geometry_image')
if not os.path.exists(path_geometry_image):
os.makedirs(path_geometry_image)
io.imsave(os.path.join(path_depth_img, '{}.png'.format(num_id)), img_2d)
io.imsave(os.path.join(path_density_img, '{}.png'.format(num_id)),
density_img)
io.imsave(os.path.join(path_geometry_image, '{}.png'.format(num_id)),
fill_flag)
print("done...{}".format(num_id))
def main():
# Get arguments
current_folder = os.path.abspath(
os.path.normpath(os.path.join(__file__, os.path.pardir)))
in_folder, out_folder = get_args()
source = os.path.join(current_folder, in_folder)
target = os.path.join(current_folder, out_folder)
# Define the target model
modelname = "vgg"
model = VGG16(weights="imagenet")
preprocessor = preprocess_input
decoder = decode_predictions
target_layer = "block5_conv3"
n_classes = 1000
# Get all image filenames
filenames = glob.glob(source)
# Define processing images folder name
if not os.path.isdir(target): os.mkdir(target)
errorlog = []
print("Analysing all provided images.")
# Loop over all files
for filename in tqdm(filenames):
# Extract filename without extension
name = os.path.basename(filename).split(".")[0]
# Load the image
img = image.load_img(filename, target_size=(224, 224))
# Preprocess the image
_, ground_truth = process_image(img, preprocess_input)
# Apply explanation methods
try:
backprop, gradcam, squadcam, data = analysis(
img, model, preprocessor, decoder, target_layer, n_classes)
except ValueError:
errorlog.append(
"ValueError: Image {} - Expected image size (224, 224). Got {}"
.format(filename, img.size))
# Create folder for output if none exists
if not os.path.isdir(os.path.join(target, name)):
os.mkdir(os.path.join(target, name))
# Save explanations and ground truth
plt.imsave("{}/{}/ground_truth.jpg".format(target, name),
rescale(ground_truth))
plt.imsave(
"{}/{}/backprop_{}_{}.jpg".format(target, name, modelname,
data[1]), backprop)
plt.imsave(
"{}/{}/gradcam_{}_{}.jpg".format(target, name, modelname, data[1]),
gradcam)
plt.imsave(
"{}/{}/squadcam_{}_{}.jpg".format(target, name, modelname,
data[1]), squadcam)
if errorlog:
print("Errors:\n{}".format("\n".join(errorlog)))
print("Analysis completed")
#
from sklearn.metrics import log_loss
#
import utils
import models
import params
###############################################################################
if __name__ == '__main__':
np.random.seed(1017)
target = 'is_iceberg'
#Load data
test, test_bands = utils.read_jason(file='test.json', loc='../input/')
test_X_dup = utils.rescale(test_bands)
test_meta = test['inc_angle'].values
tmp = dt.datetime.now().strftime("%Y-%m-%d-%H-%M")
file_weights = '../weights/weights_current.hdf5'
if os.path.isfile(file_weights):
#define and load model
nb_filters = params.nb_filters
nb_dense = params.nb_dense
weights_file = params.weights_file
model = models.get_model(img_shape=(75, 75, 2),
f=nb_filters,
h=nb_dense)
model.load_weights(weights_file)
locals().update(config)
# Set up model and prediction function
x = tensor.tensor3('inputs', dtype='float64')
y = tensor.tensor3('targets', dtype='float64')
model = 'bs'
with open ('gru_best.pkl', 'r') as picklefile:
model = load(picklefile)
y_hat, cost, cells = nn_fprop(x, y, frame_length, hidden_size, num_layers, model)
predict_fn = theano.function([x], y_hat)
# Generate
print "generating audio..."
seed = get_seed(hdf5_file, [seed_index])
sec = 16000
samples_to_generate = sec*secs_to_generate
num_frames_to_generate = samples_to_generate/frame_length + seq_length #don't include seed
predictions = []
prev_input = seed
for i in range(num_frames_to_generate):
prediction = predict_fn(prev_input)
predictions.append(prediction)
pred_min = numpy.min(predictions)
pred_max = numpy.max(predictions)
prev_input = rescale(prediction, pred_max, pred_min)
actually_generated = numpy.asarray(predictions)[seq_length:,:,:,:] #cut off seed
last_frames = actually_generated[:,:,-1,:]
make_wav(output_filename, actually_generated.flatten())
print str(secs_to_generate)+'seconds of audio generated'
print output_filename
files = get_files(cat)
matrices = {}
for fname in sorted(files, key=lambda x: string2prefix_num(x)):
######################################################################
#generate results for current fname
word_modacolor = get_modacolor_dict(cat, fname)
print "word_modacolor:", word_modacolor
word_modacolor = convert_modacolor_to_naturals(word_modacolor)
print "word_modacolor:", word_modacolor
#mtx = [[0]*(max_y-min_y+1)]*(max_x-min_x+1) VERY WRONG!! CHECK WHY
mtx = [[0 for _ in xrange(max_y-min_y+1)] for _ in xrange(max_x-min_x+1)]
for key, moda in word_modacolor.iteritems():
x, y = position2coordinates[key+1]
mtx[x][y] = moda
mtx = rescale(mtx, 10)
#print matrix(mtx)
#matrices[string2prefix_num(fname)] = ...
#scipy.misc.imsave(str(string2prefix_num(fname))+'.jpg', matrix(mtx))
ax = plt.gca()
ax.patch.set_alpha(0.0)
xtick_locs = [0, 20, 40, 60, 80]
xtick_labels = [0, 2, 4, 6, 8]
plt.yticks(xtick_locs, xtick_labels)
ytick_locs = [0, 100, 200, 300, 400]
ytick_labels = [0, 10, 20, 30, 40]
plt.xticks(ytick_locs, ytick_labels)
img = plt.imshow(matrix(mtx))#, cmap=cm.Greys_r) #Needs to be in row,col order
