def split_set(Sset, k):
mean = themean(Sset, k)
const = mean[0]
dire = mean[1]
if dire == 0:
Z_ind = np.where(Sset.x[:, k] <= const)[0]
One_ind = np.where(Sset.x[:, k] > const)[0]
else:
Z_ind = np.where(Sset.x[:, k] >= const)[0]
One_ind = np.where(Sset.x[:, k] < const)[0]
Sset_0feat = np.delete(Sset.x, One_ind, 0)
Sset_0label = np.delete(Sset.label, One_ind)
Sset_1feat = np.delete(Sset.x, Z_ind, 0)
Sset_1label = np.delete(Sset.label, Z_ind)
return (theSet(Sset_0feat, Sset_0label), theSet(Sset_1feat, Sset_1label))
def split_set(Sset, k):
mean = themean(Sset,k)
const = mean[0]
dire = mean[1]
if dire == 0:
Z_ind = np.where(Sset.x[:,k] <= const)[0]
One_ind = np.where(Sset.x[:,k] > const)[0]
else:
Z_ind = np.where(Sset.x[:,k] >= const)[0]
One_ind = np.where(Sset.x[:,k] < const)[0]
Sset_0feat = np.delete(Sset.x, One_ind, 0)
Sset_0label = np.delete(Sset.label, One_ind)
Sset_1feat = np.delete(Sset.x, Z_ind, 0)
Sset_1label = np.delete(Sset.label, Z_ind)
return (theSet(Sset_0feat, Sset_0label), theSet(Sset_1feat, Sset_1label))
def createMedianMask(disparityMap, validDepthMask, rect=None):
"""Return a mask selecting the median layer, plus shadows."""
if rect is not None:
x, y, w, h = rect
disparityMap = disparityMap[y:y + h, x:x + w]
validDepthMask = validDepthMask[y:y + h, x:x + w]
median = np.median(disparityMap)
return np.where((validDepthMask == 0) | (abs(disparityMap - median) < 12),
1.0, 0.0)
def work_on_detected(self):
cpy = np.copy(self.channels["laser"])
height, width = cpy.shape
indices = np.transpose(np.where(cpy > 0))
if indices.any():
sum_y, sum_x = np.sum(indices, axis=0)
self.ave_y, self.ave_x = sum_y / len(indices), sum_x / len(indices)
# print("average x:", self.ave_x, "average y:", self.ave_y)
self.manager.update_player_place((self.ave_x, self.ave_y))
def deskew(image_path):
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bitwise_not(gray)
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
coords = np.column_stack(np.where(thresh > 0))
angle = cv2.minAreaRect(coords)[-1]
# the `cv2.minAreaRect` function returns values in the
# range [-90, 0); as the rectangle rotates clockwise the
# returned angle trends to 0 -- in this special case we
# need to add 90 degrees to the angle
if angle < -45:
angle = -(90 + angle)
# otherwise, just take the inverse of the angle to make
# it positive
else:
angle = -angle
# rotate the image to deskew it
(h, w) = image.shape[:2]
center = (w // 2, h // 2)
M = cv2.getRotationMatrix2D(center, angle, 1.0)
rotated = cv2.warpAffine(image,
M, (w, h),
flags=cv2.INTER_CUBIC,
borderMode=cv2.BORDER_REPLICATE)
cv2.putText(rotated, "Angle: {:.2f} degrees".format(angle), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)
# show the output image
print("[INFO] angle: {:.3f}".format(angle))
cv2.imshow("Input", image)
cv2.imshow("Rotated", rotated)
for x, color in enumerate(rgb):
with open(
'G:/My Drive/Projects/ifl_corridors/rp5k130k25k/annual_loss/tcaloss_'
+ str(x + 1) + '.clr', 'w') as f:
f.write('%s\n' % ' '.join(color))
#%%
# Reclassify south america rasters (32,33)
# Read raster
for n in range(1, 19, 1):
rname = 'G:/My Drive/Projects/ifl_corridors/rp5k130k25k/annual_loss/sa_rtile_lybytc2000_' + str(
n) + '_90m_33.tif'
with rasterio.open(rname) as r:
r_meta = r.profile
a = r.read(1)
a = np.where(a > 0, 1, 0)
r_meta['dtype'] = 'int16'
with rasterio.open(
'G:/My Drive/Projects/ifl_corridors/rp5k130k25k/annual_loss/sa_rtile_lybytc2000_'
+ str(n) + '_90m_33_rcl.tif', 'w', **r_meta) as dst:
dst.write(a.astype('int16'), 1)
#%%
# Reclassify africa rasters (25,26,31,32)
# Read raster
for n in range(1, 19, 1):
rname = 'G:/My Drive/Projects/ifl_corridors/rp5k130k25k/annual_loss/af_rtile_lybytc2000_' + str(
n) + '_90m_33.tif'
with rasterio.open(rname) as r:
r_meta = r.profile
a = r.read(1)
k = []
while len(k) != 30:
m = randint(1, 30)
if m not in k:
k.append(m)
print(
"----------------------------------------------------------------------------"
)
for l in range(0, 30):
y = int(input("Enter 1 to draw next number:"))
if y == 1:
print("The Number is:{}".format(k[l]))
if k[l] in A:
a = list(zip(np.where(A == k[l])))
r = list(zip(a[0], a[1]))
x, y = r[0]
A[x[0]][y[0]] = 0
print("{}:{}".format(A, name_1))
print('----------------------')
if k[l] in B:
a = list(zip(np.where(B == k[l])))
r = list(zip(a[0], a[1]))
x, y = r[0]
B[x[0]][y[0]] = 0
print("{}:{}".format(B, name_2))
print('----------------------')
def blurringkernel(shape, T, a, b):
xx, yy = shape
x, y = np.ogrid[(-xx / 2):(xx / 2), (-yy / 2):yy / 2]
q = (np.pi * (x * a + y * b))
q[np.where(q == 0)] = T
return (T / q) * np.sin(q) * np.exp(-1j * q)
def query(self, n, model, train_dataset, pool_dataset, budget=10000):
device = model.state_dict()['softmax.bias'].device
full_dataset = ConcatDataset([pool_dataset, train_dataset])
pool_len = len(pool_dataset)
self.embeddings = self.get_embeddings(model, device, full_dataset)
# Calc distance matrix
num_images = self.embeddings.shape[0]
dist_mat = self.calc_distance_matrix(num_images)
# We need to get k centers start with greedy solution
upper_bound = gb.UB
lower_bound = upper_bound / 2.0
max_dist = upper_bound
_x, _y = np.where(dist_mat <= max_dist)
_distances = dist_mat[_x, _y]
subset = [i for i in range(1)]
model = solve_fac_loc(_x, _y, subset, num_images, budget)
# model.setParam( 'OutputFlag', False )
x, y, z = model.__data
delta = 1e-7
while upper_bound - lower_bound > delta:
print("State", upper_bound, lower_bound)
current_radius = (upper_bound + lower_bound) / 2.0
violate = np.where(_distances > current_radius) # Point distances which violate the radius
new_max_d = np.min(_distances[_distances >= current_radius])
new_min_d = np.max(_distances[_distances <= current_radius])
print("If it succeeds, new max is:", new_max_d, new_min_d)
for v in violate[0]:
x[_x[v], _y[v]].UB = 0 # The upper bound for points, which violate the radius are set to zero
model.update()
r = model.optimize()
if model.getAttr(gb.GRB.Attr.Status) == gb.GRB.INFEASIBLE:
failed = True
print("Infeasible")
elif sum([z[i].X for i in range(len(z))]) > 0:
failed = True
print("Failed")
else:
failed = False
if failed:
lower_bound = max(current_radius, new_max_d)
# failed so put edges back
for v in violate[0]:
x[_x[v], _y[v]].UB = 1
else:
print("solution found", current_radius, lower_bound, upper_bound)
upper_bound = min(current_radius, new_min_d)
model.write("s_{}_solution_{}.sol".format(budget, current_radius))
idxs_labeled = np.arange(start=pool_len, stop=pool_len + len(train_dataset))
# Perform kcenter greedy
self.update_distances(idxs_labeled, idxs_labeled, only_new=False, reset_dist=True)
sel_ind = []
for _ in range(n):
ind = np.argmax(self.min_distances) # Get sample with highest distance
assert ind not in idxs_labeled, "Core-set picked index already labeled"
self.update_distances([ind], idxs_labeled, only_new=True, reset_dist=False)
sel_ind.append(ind)
assert len(set(sel_ind)) == len(sel_ind), "Core-set picked duplicate samples"
remaining_ind = list(set(np.arange(pool_len)) - set(sel_ind))
return sel_ind, remaining_ind
