def test_rectangle_perimiter_clip_bottom_right():
# clip=False
expected = np.array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 1],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0],
[0, 1, 0, 0, 0]], dtype=np.uint8)
img = np.zeros((5, 5), dtype=np.uint8)
start = (2, 2)
extent = (10, 10)
rr, cc = rectangle_perimeter(start, extent=extent, shape=img.shape,
clip=False)
img[rr, cc] = 1
assert_array_equal(img, expected)
# clip=True
expected = np.array([[0, 0, 0, 0, 0],
[0, 1, 1, 1, 1],
[0, 1, 0, 0, 1],
[0, 1, 0, 0, 1],
[0, 1, 1, 1, 1]], dtype=np.uint8)
img = np.zeros((5, 5), dtype=np.uint8)
rr, cc = rectangle_perimeter(start, extent=extent, shape=img.shape,
clip=True)
img[rr, cc] = 1
assert_array_equal(img, expected)
def test_rectangle_perimiter_clip_top_left():
# clip=False
expected = np.array([[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[0, 0, 0, 1, 0],
[1, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype=np.uint8)
img = np.zeros((5, 5), dtype=np.uint8)
start = (-5, -5)
end = (2, 2)
rr, cc = rectangle_perimeter(start, end=end, shape=img.shape,
clip=False)
img[rr, cc] = 1
assert_array_equal(img, expected)
# clip=True
expected = np.array([[1, 1, 1, 1, 0],
[1, 0, 0, 1, 0],
[1, 0, 0, 1, 0],
[1, 1, 1, 1, 0],
[0, 0, 0, 0, 0]], dtype=np.uint8)
img = np.zeros((5, 5), dtype=np.uint8)
rr, cc = rectangle_perimeter(start, end=end, shape=img.shape,
clip=True)
img[rr, cc] = 1
assert_array_equal(img, expected)
def test_rectangle_extent_negative():
# These two tests should be done together.
expected = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 2, 2, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]], dtype=np.uint8)
start = (3, 5)
extent = (-1, -2)
img = np.zeros(expected.shape, dtype=np.uint8)
rr, cc = rectangle_perimeter(start, extent=extent, shape=img.shape)
img[rr, cc] = 1
rr, cc = rectangle(start, extent=extent, shape=img.shape)
img[rr, cc] = 2
assert_array_equal(img, expected)
# Ensure that rr and cc have no overlap
img = np.zeros(expected.shape, dtype=np.uint8)
rr, cc = rectangle(start, extent=extent, shape=img.shape)
img[rr, cc] = 2
rr, cc = rectangle_perimeter(start, extent=extent, shape=img.shape)
img[rr, cc] = 1
assert_array_equal(img, expected)
def test_rectangle_perimiter():
expected = np.array([[0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 1, 1],
[0, 0, 1, 0, 0, 1],
[0, 0, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0]], dtype=np.uint8)
start = (2, 3)
end = (2, 4)
img = np.zeros(expected.shape, dtype=np.uint8)
# Test that the default parameter is indeed end
rr, cc = rectangle_perimeter(start, end, shape=img.shape)
img[rr, cc] = 1
assert_array_equal(img, expected)
# Swap start and end
img = np.zeros(expected.shape, dtype=np.uint8)
rr, cc = rectangle_perimeter(end=start, start=end, shape=img.shape)
img[rr, cc] = 1
assert_array_equal(img, expected)
img = np.zeros(expected.shape, dtype=np.uint8)
start = (2, 3)
extent = (1, 2)
rr, cc = rectangle_perimeter(start, extent=extent, shape=img.shape)
img[rr, cc] = 1
assert_array_equal(img, expected)
decoder = Decoder()
with open("examples/big_buck_bunny_360p24.h265", 'rb') as file:
for k, image in enumerate(decoder.decode(file)):
image: Image
reconstruction = image.get_image()
if k == 19:
code_structure = CodeStructure(image)
reconstruction = image.get_image()
h, w, _ = reconstruction.shape
cb_info = ycbcr2rgb(reconstruction)
for cb in code_structure.iter_code_blocks():
rr, cc = rectangle_perimeter(start=cb.position + 1,
extent=cb.size - 1)
rr = np.clip(rr, a_min=0, a_max=h - 1)
cc = np.clip(cc, a_min=0, a_max=w - 1)
cb_info[rr, cc] = (255, 25, 25)
pb_info = ycbcr2rgb(reconstruction)
for pb in code_structure.iter_prediction_blocks():
rr, cc = rectangle_perimeter(start=pb.position + 1,
extent=pb.size - 1)
rr = np.clip(rr, a_min=0, a_max=h - 1)
cc = np.clip(cc, a_min=0, a_max=w - 1)
pb_info[rr, cc] = (25, 255, 25)
pb_center = pb.position + pb.size / 2
pb_source = pb_center + pb.vec0
rr, cc, val = line_aa(int(pb_center[0]), int(pb_center[1]),
int(pb_source[0]), int(pb_source[1]))
dl = Loader('/home/ubelix/lejeune/data/medical-labeling/Dataset30')
label = 700
frame = 40
scale_factor = 2
sample = dl[frame]
im = sample['image']
labels = sample['labels'][..., 0]
sp = labels == label
contour = segmentation.find_boundaries(labels)
im[contour, ...] = (255, 0, 0)
bbox = sample['bboxes'][label]
rr, cc = draw.rectangle_perimeter(start=(bbox[1], bbox[0]),
end=(bbox[3], bbox[2]),
shape=labels.shape)
im[rr, cc, ...] = (0, 0, 255)
bbox_scaled = scale_boxes(sample['bboxes'], scale_factor)[label]
rr, cc = draw.rectangle_perimeter(start=(bbox_scaled[1], bbox_scaled[0]),
end=(bbox_scaled[3], bbox_scaled[2]),
shape=labels.shape)
im[rr, cc, ...] = (0, 255, 0)
print(bbox)
print(bbox_scaled)
plt.subplot(121)
plt.imshow(im)
plt.subplot(122)
def demo(path_to_data):
"""
Demonstration of the pre-processing pipeline.
Iterates over the test data and displays the original next to the pre-processed version of each
image.
:param path_to_data: Path to the folder containing "test.json", "training.json", and "images/"
"""
color_mapping = {
'difficult': [185, 51, 173], # purple
'gametocyte': [255, 99, 25], # orange
'leukocyte': [0, 0, 255], # blue
'red blood cell': [255, 0, 0], # red
'schizont': [252, 204, 10], # yellow
'ring': [153, 102, 51], # brown
'trophozoite': [0, 147, 60] # green
}
with open(path_to_data + "/training.json") as json_file:
files = json.load(json_file)
fig, axs = plt.subplots(ncols=2, nrows=2)
for file in files:
# read the image
file_path = file['image']['pathname']
fig.suptitle(file_path + "\nclick on plot to see next image")
print(f"processing file: {file_path}")
start_time = timeit.default_timer()
image = io.imread(path_to_data + file_path)
# no background removal (top right)
processed1 = apply_pre_processing(image,
background_rem=False,
morph_filter=False)
axs[0][1].imshow(processed1)
axs[0][1].set_title("no background rem.")
# background removal, w/o morphological filter on background mask (bottom left)
processed2 = apply_pre_processing(image,
background_rem=True,
morph_filter=False)
axs[1][0].imshow(processed2)
axs[1][0].set_title("background rem. (no morph)")
# background removal with morph. filters on background mask (bottom right)
processed3 = apply_pre_processing(image,
background_rem=True,
morph_filter=True)
axs[1][1].imshow(processed3)
axs[1][1].set_title("background rem. (with morph)")
# draw bounding boxes
for obj in file['objects']:
bounding_box = obj['bounding_box']
minimum = bounding_box['minimum']
start = (minimum['r'], minimum['c'])
maximum = bounding_box['maximum']
stop = (maximum['r'] - 2, maximum['c'] - 2)
rr, cc = draw.rectangle_perimeter(start=start, end=stop)
image[rr, cc] = color_mapping[obj['category']]
# image before pre-processing with bounding boxes (top left)
axs[0][0].imshow(image)
axs[0][0].set_title("input with ground truth")
fig.show()
print(
f"elapsed time: {timeit.default_timer() - start_time} seconds")
plt.ginput() # wait for click before doing more processing
def coords(self):
top_left = self.x - (self.height // 2), self.y - (self.width // 2)
bottom_right = self.x + (self.height // 2), self.y + (self.width // 2)
coords = draw.rectangle_perimeter(top_left,
extent=(self.height, self.width))
return coords
def get_image_preview(self):
"""
Get a preview of the image mask.
Returns:
An image representing the preview of the mask.
"""
img = self._image.create_overlay_img()
patch_size_x = self\
._image.patches[self._current_patch_index].patch.shape[0]
patch_size_y = self\
._image.patches[self._current_patch_index].patch.shape[1]
# Draw patch grid
if self._grid_img is None:
self._grid_img = np.zeros(img.shape, dtype=np.bool)
for i in range(self.NUM_PATCHES):
for j in range(self.NUM_PATCHES):
start_x = i * patch_size_x
stop_x = start_x + patch_size_x
start_y = j * patch_size_y
stop_y = start_y + patch_size_y
rec_start = (start_x, start_y)
rec_end = (stop_x, stop_y)
rr, cc = rectangle_perimeter(rec_start,
end=rec_end,
shape=self._grid_img.shape)
self._grid_img[rr, cc] = True
img[self._grid_img] = 207
# Draw current Patch
start_x = self._image\
.patches[self._current_patch_index].patch_index[0] * patch_size_x
stop_x = start_x + patch_size_x
start_y = self\
._image.patches[self._current_patch_index]\
.patch_index[1] * patch_size_y
stop_y = start_y + patch_size_y
rec_start = (start_x, start_y)
rec_end = (stop_x, stop_y)
rr, cc = rectangle_perimeter(rec_start,
end=rec_end,
shape=self._image.image.shape)
img[rr, cc] = [255, 255, 0]
for i in range(4):
rec_start = (rec_start[0] + 1, rec_start[1] + 1)
rec_end = (rec_end[0] - 1, rec_end[1] - 1)
rr, cc = rectangle_perimeter(rec_start,
end=rec_end,
shape=self._image.image.shape)
img[rr, cc] = [255, 255, 0]
return img
def optimum_heading(self, target, additional_coords, display=False):
flattened = np.copy(self.occupancy_grid)
flattened[flattened > 0.8] = 1
flattened[flattened <= 0.8] = 0
rr, cc = rectangle_perimeter((1, 1), (78, 78), shape=flattened.shape)
flattened[rr, cc] = 0
rr, cc = rectangle((0, 0), (13, 13), shape=flattened.shape)
flattened[rr, cc] = 0
rr, cc = rectangle((0, 65), (13, 79), shape=flattened.shape)
flattened[rr, cc] = 0
# coords = np.where(flattened > 0.8)
# rr, cc = ellipse(target[0][0] , target[1][0], 4,4)
# #print(np.array(coords).shape)
# for i in range(len(rr)):
# k1 = np.where(coords[0] == rr[i], True, False)
# k2 = np.where(coords[1] == cc[i], True, False)
# inter = np.logical_not(np.logical_and(k1, k2))
# #print(inter)
# coords = np.compress(inter, coords, axis = 1)
coords = []
rr, cc = ellipse(target[0], target[1], 4, 4)
for i in range(len(self.blocks)):
if np.linalg.norm(target[0:2] - self.blocks[i][0:2]) > 4:
coords.append([self.blocks[i][0], self.blocks[i][1]])
#coords.append(additional_coords)
coords = np.array(coords)
# vectors = []
# for i in range(len(self.blocks)):
# vectors.append([target[0] - self.blocks[i][0], target[1] - self.blocks[i][1]])
# vectors.append([target[0], 0])
# vectors.append([0, target[1]])
# vectors.append([79 - target[0], 0])
# vectors.append([0, 79 - target[1]])
if len(coords) > 0:
vectors = np.subtract(target, coords)
vectors = np.concatenate((vectors, np.array([[target[0], 0]])))
else:
vectors = np.array([[target[0], 0]])
vectors = np.concatenate((vectors, np.array([[0, target[1]]])))
vectors = np.concatenate((vectors, np.array([[-79 + target[0], 0]])))
vectors = np.concatenate((vectors, np.array([[0, -79 + target[1]]])))
#print(vectors)
#distances = np.sqrt(np.add(np.power(vectors[0,:], 2), np.power(vectors[1,:], 2)))
distances = np.linalg.norm(vectors, axis=1)
#print(distances)
w = 200
distance_weighting = 1 - np.tanh(np.divide(np.power(distances, 2), w))
#probability_weighting = (np.exp(np.power(self.occupancy_grid[coords[0], coords[1]], 6)) - 1)/1.718
probability_weighting = 100
overall_weighting = distance_weighting * probability_weighting + 0.0000001
overall_weighting = np.expand_dims(overall_weighting, axis=1)
#print(overall_weighting)
normed = []
for i in range(len(vectors)):
norm = vectors[i] / np.linalg.norm(vectors[i])
normed.append(norm)
normed = np.array(normed)
optimum_heading = np.sum(normed * overall_weighting,
axis=0) / (len(overall_weighting))
scaling_factor = 8
normed_optimum_heading = scaling_factor * optimum_heading / np.linalg.norm(
optimum_heading)
#print(optimum_heading)
optimum_target = np.array([
target[0] + normed_optimum_heading[0] + 0.00001,
target[1] + normed_optimum_heading[1] + 0.00001
])
#print(optimum_target)
#flattened[rr,cc] = 0.5
#flattened[int(optimum_target[0]), int(optimum_target[1])] = 0.8
#print('target_danger: ' + str())
target_danger = np.linalg.norm(optimum_heading)
if np.isnan(target_danger):
target_danger = 0
#print(target_danger)
test = flattened
if display == False:
return optimum_target, target_danger
else:
return optimum_target, target_danger, flattened
def plot_grid(imgs,
FOCUS=None,
ZOOM=None,
number_of_rows=1,
margin=None,
show=False,
save_name=None,
plot1d=None,
dpi=300):
"""Grid plotter function for
Args:
imgs ([type]): Array of Numpy ndarrays dim should be 2D
FOCUS (func, optional): Focus function creates
a rectangle and show zooomed version there. Defaults to None.
ZOOM (func, optional): Zoom function creates a rectange and
crop the images to that area. Defaults to None.
number_of_rows (int, optional): Number of row for images,
columns will be calculated automatically. Defaults to 1.
margin (int, optional): number of pixel for the margin. Defaults to None.
show (bool, optional): show the image grid or not. Defaults to False.
save_name (str, optional): grid save img directory
if not defined images will not be saved . Defaults to None.
plot1d (int, optional): 1d plot position, Defauls None.
dpi (int, optional): DPI of save image. Defaults to 300.
"""
updated_imgs = []
### FOCUS and Zoom
if FOCUS:
for img in imgs:
# find focus
focused_img, coor = FOCUS(img)
f_size_x, f_size_y = focused_img.shape
focused_img = resize(focused_img, (f_size_x * 2, f_size_y * 2))
f_size_x, f_size_y = focused_img.shape
size_x, size_y = img.shape
uimg = img.copy()
# draw focus rect
rr, cc = rectangle_perimeter(coor[0:2], coor[2:4])
uimg[rr, cc] = 1
# draw big focus rect
rr, cc = rectangle_perimeter(
(size_x - f_size_x, size_y - f_size_y),
(size_x - 2, size_y - 2),
)
uimg[size_x - f_size_x:size_x,
size_y - f_size_y:size_y] = focused_img
uimg[rr, cc] = 1
# append img
updated_imgs.append(uimg)
elif ZOOM:
for img in imgs:
focused_img, coor = ZOOM(img)
# find focus
updated_imgs.append(focused_img)
else:
updated_imgs = imgs
# GRID SHAPE
number_of_columns = len(imgs) // number_of_rows
updated_imgs = np.vstack([
np.hstack(updated_imgs[i:i + number_of_columns])
for i in range(0, len(imgs), number_of_columns)
])
# 1D plot
if plot1d:
h, w = updated_imgs.shape
plt.figure(figsize=(5, 1))
plt.plot(updated_imgs[plot1d, :])
plt.show()
th_h = h // 20
th_w = (w // number_of_columns) // 20
for th in range(-th_h // 2, th_h // 2):
updated_imgs[plot1d + th, [i for i in range(0, w, th_w)]] = 1
updated_imgs[plot1d + th, [i + 1 for i in range(0, w, th_w)]] = 1
# updated_imgs[plot1d+2,:] = 1
# Save and Show
ims = np.clip(updated_imgs, 0, 1) * 255
ims = ims.astype(np.uint8)
if show:
plt.figure()
imshow(ims)
plt.show()
if save_name:
imsave(save_name, ims)
# Center cross, 2 pixel width
xc = int(width / 2)
yc = int(height / 2)
length = int(pixel_per_um * 8)
gap = int(pixel_per_um * 3)
# Vertical
rr, cc = rectangle((yc - length, xc - 1), (yc - gap, xc))
img[rr, cc] = 0
rr, cc = rectangle((yc + length, xc - 1), (yc + gap, xc))
img[rr, cc] = 0
# Horizontal
rr, cc = rectangle((yc - 1, xc - length), (yc, xc - gap))
img[rr, cc] = 0
rr, cc = rectangle((yc - 1, xc + length), (yc, xc + gap))
img[rr, cc] = 0
# ROI
for ROI in (ROI_1, ROI_2):
ROI_width = int(ROI * pixel_per_um)
ROI_height = int(ROI * pixel_per_um)
x1 = int(width / 2 - ROI_width / 2)
y1 = int(height / 2 - ROI_height / 2)
x2 = int(width / 2 + ROI_width / 2)
y2 = int(height / 2 + ROI_height / 2)
rr, cc = rectangle_perimeter((y1, x1), (y2, x2))
img[rr, cc] = 0
# Save
imageio.imsave(name + '.bmp', img)
# rr, cc = draw.disk((pos[1],pos[0]),5)
# image[rr, cc] = [0,255,0]
# rr, cc = draw.rectangle_perimeter((boxmin[1],boxmin[0]),(boxmax[1],boxmax[0]))
# image[rr,cc] = [255,0,0]
# image[mask[1],mask[0]] = [100,100,100]
# ax = fig.add_subplot(5,1,i+1)
# ax.imshow(image)
# plt.show()
# gen = AugGenerator(img, data, (400,400))
# s = next(gen)
ds = create_train_dataset(img, data, (400, 300), 1)
sample = ds.take(5).as_numpy_iterator()
fig = plt.figure()
for i, s in enumerate(sample):
ax = fig.add_subplot(5, 1, i + 1)
img = s[0]['image'][0].swapaxes(0, 1)
height, width = img.shape[:2]
pos = s[0]['pos'][0] * [width, height]
xmin, ymin, xmax, ymax = s[1][0] * np.array(
[width, height, width, height])
rr, cc = draw.disk((pos[1], pos[0]), 5, shape=img.shape[:2])
img[rr, cc] = [0, 255, 0]
rr, cc = draw.rectangle_perimeter((ymin, xmin), (ymax, xmax),
shape=img.shape[:2])
img[rr, cc] = [255, 0, 0]
ax.imshow(img)
plt.show()
def quantify_single_image_redness(orig_image,
grid,
auto,
t=1,
d=3,
s=1,
negate=True,
reportAll=False,
hardImageThreshold=None,
hardSizeThreshold=None):
'''
Process a single image (phloxine mode).
'''
#Prepare image
image = prepare_redness_image(orig_image)
#Create grid
if auto:
grid, griddist = make_grid_auto(image, grid)
else:
grid, griddist = make_grid(grid)
#Make mask
#Adjust threshold for redness images slightly, just what works in practise. t parameter is still applied as additional coefficient
mask = make_mask(image,
t=1.02 * t,
s=s,
hardImageThreshold=hardImageThreshold,
hardSizeThreshold=hardSizeThreshold,
local=True)
#Measure regionprobs
data = {
r.label: {
p: r[p]
for p in
['label', 'area', 'centroid', 'mean_intensity', 'perimeter']
}
for r in regionprops(mask, intensity_image=image)
}
data = pd.DataFrame(data).transpose()
blob_to_pos = match_to_grid(data['label'],
data['centroid'],
grid,
griddist,
d=d,
reportAll=reportAll)
#Select only those blobs which have a corresponding grid position
data = data.loc[[l in blob_to_pos for l in data['label']]]
#Add grid position information to table
data['row'] = data['label'].map(lambda x: blob_to_pos[x].split('-')[0])
data['column'] = data['label'].map(lambda x: blob_to_pos[x].split('-')[1])
#Add circularity
data['circularity'] = (4 * math.pi * data['area']) / (data['perimeter']**2)
#Make qc image, add bounding boxes to blobs with grid assigned
qc = np.copy(orig_image)
for region in regionprops(mask):
if region.label in data['label']:
minr, minc, maxr, maxc = region.bbox
bboxrows, bboxcols = rectangle_perimeter([minr, minc],
end=[maxr, maxc],
shape=image.shape,
clip=True)
qc[bboxrows, bboxcols, :] = np.array((255, 255, 255))
return (data, qc)
