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from io import BytesIO
import math
import numpy as np
from PIL import Image
import skimage.draw
from . import python_utils
CV2 = False
if python_utils.module_exists("cv2"):
import cv2
CV2 = True
if python_utils.module_exists("matplotlib.pyplot"):
import matplotlib.pyplot as plt
def get_image_size(filepath):
im = Image.open(filepath)
return im.size
def load_image(image_filepath):
image = Image.open(image_filepath)
image.load()
image_array = np.array(image, dtype=np.uint8)
image.close()
return image_array
def padded_boundingbox(boundingbox, padding):
boundingbox_new = np.empty_like(boundingbox)
boundingbox_new[0:2] = boundingbox[0:2] + padding
boundingbox_new[2:4] = boundingbox[2:4] - padding
return boundingbox_new
def center_bbox(spatial_shape, output_shape):
"""
Return a bbox centered in spatial_shape with size output_shape
:param spatial_shape:
:param output_shape:
:return:
"""
center = (spatial_shape[0] / 2, spatial_shape[1] / 2)
half_output_shape = (output_shape[0] / 2, output_shape[1] / 2)
bbox = [center[0] - half_output_shape[0], center[1] - half_output_shape[1], center[0] + half_output_shape[0], center[1] + half_output_shape[1]]
bbox = bbox_to_int(bbox)
return bbox
def bbox_add_margin(bbox, margin):
bbox_new = bbox.copy()
bbox_new[0:2] -= margin
bbox_new[2:4] += margin
return bbox_new
def bbox_to_int(bbox):
bbox_new = [
int(np.floor(bbox[0])),
int(np.floor(bbox[1])),
int(np.ceil(bbox[2])),
int(np.ceil(bbox[3])),
]
return bbox_new
def draw_line_aa_in_patch(edge, patch_bounds):
rr, cc, prob = skimage.draw.line_aa(edge[0][0], edge[0][1], edge[1][0], edge[1][1])
keep_mask = (patch_bounds[0] <= rr) & (rr < patch_bounds[2]) \
& (patch_bounds[1] <= cc) & (cc < patch_bounds[3])
rr = rr[keep_mask]
cc = cc[keep_mask]
prob = prob[keep_mask]
return rr, cc, prob
def convert_array_to_jpg_bytes(image_array, mode=None):
img = Image.fromarray(image_array, mode=mode)
output = BytesIO()
img.save(output, format="JPEG", quality=90)
contents = output.getvalue()
output.close()
return contents
def displacement_map_to_transformation_maps(disp_field_map):
disp_field_map = disp_field_map.astype(np.float32)
i = np.arange(disp_field_map.shape[0], dtype=np.float32)
j = np.arange(disp_field_map.shape[1], dtype=np.float32)
iv, jv = np.meshgrid(i, j, indexing="ij")
reverse_map_i = iv + disp_field_map[:, :, 1]
reverse_map_j = jv + disp_field_map[:, :, 0]
return reverse_map_i, reverse_map_j
if CV2:
def apply_displacement_field_to_image(image, disp_field_map):
trans_map_i, trans_map_j = displacement_map_to_transformation_maps(disp_field_map)
misaligned_image = cv2.remap(image, trans_map_j, trans_map_i, cv2.INTER_CUBIC)
return misaligned_image
def apply_displacement_fields_to_image(image, disp_field_maps):
disp_field_map_count = disp_field_maps.shape[0]
misaligned_image_list = []
for i in range(disp_field_map_count):
misaligned_image = apply_displacement_field_to_image(image, disp_field_maps[i, :, :, :])
misaligned_image_list.append(misaligned_image)
return misaligned_image_list
else:
def apply_displacement_fields_to_image(image, disp_field_map):
print("cv2 is not available, the apply_displacement_fields_to_image(image, disp_field_map) function cannot work!")
def apply_displacement_fields_to_image(image, disp_field_maps):
print("cv2 is not available, the apply_displacement_fields_to_image(image, disp_field_maps) function cannot work!")
def get_axis_patch_count(length, stride, patch_res):
total_double_padding = patch_res - stride
patch_count = max(1, int(math.ceil((length - total_double_padding) / stride)))
return patch_count
def compute_patch_boundingboxes(image_size, stride, patch_res):
"""
@param image_size:
@param stride:
@param patch_res:
@return: [[row_start, col_start, row_end, col_end], ...]
"""
im_rows = image_size[0]
im_cols = image_size[1]
row_patch_count = get_axis_patch_count(im_rows, stride, patch_res)
col_patch_count = get_axis_patch_count(im_cols, stride, patch_res)
patch_boundingboxes = []
for i in range(0, row_patch_count):
if i < row_patch_count - 1:
row_slice_begin = i * stride
row_slice_end = row_slice_begin + patch_res
else:
row_slice_end = im_rows
row_slice_begin = row_slice_end - patch_res
for j in range(0, col_patch_count):
if j < col_patch_count - 1:
col_slice_begin = j*stride
col_slice_end = col_slice_begin + patch_res
else:
col_slice_end = im_cols
col_slice_begin = col_slice_end - patch_res
patch_boundingbox = np.array([row_slice_begin, col_slice_begin, row_slice_end, col_slice_end], dtype=np.int)
assert row_slice_end - row_slice_begin == col_slice_end - col_slice_begin == patch_res, "ERROR: patch does not have the requested shape"
patch_boundingboxes.append(patch_boundingbox)
return patch_boundingboxes
def clip_boundingbox(boundingbox, clip_list):
assert len(boundingbox) == len(clip_list), "len(boundingbox) should be equal to len(clip_values)"
clipped_boundingbox = []
for bb_value, clip in zip(boundingbox[:2], clip_list[:2]):
clipped_value = max(clip, bb_value)
clipped_boundingbox.append(clipped_value)
for bb_value, clip in zip(boundingbox[2:], clip_list[2:]):
clipped_value = min(clip, bb_value)
clipped_boundingbox.append(clipped_value)
return clipped_boundingbox
def crop_or_pad_image_with_boundingbox(image, patch_boundingbox):
im_rows = image.shape[0]
im_cols = image.shape[1]
row_padding_before = max(0, - patch_boundingbox[0])
col_padding_before = max(0, - patch_boundingbox[1])
row_padding_after = max(0, patch_boundingbox[2] - im_rows)
col_padding_after = max(0, patch_boundingbox[3] - im_cols)
# Center padding:
row_padding = row_padding_before + row_padding_after
col_padding = col_padding_before + col_padding_after
row_padding_before = row_padding // 2
col_padding_before = col_padding // 2
row_padding_after = row_padding - row_padding // 2
col_padding_after = col_padding - col_padding // 2
clipped_patch_boundingbox = clip_boundingbox(patch_boundingbox, [0, 0, im_rows, im_cols])
if len(image.shape) == 2:
patch = image[clipped_patch_boundingbox[0]:clipped_patch_boundingbox[2], clipped_patch_boundingbox[1]:clipped_patch_boundingbox[3]]
patch = np.pad(patch, [(row_padding_before, row_padding_after), (col_padding_before, col_padding_after)], mode="constant")
elif len(image.shape) == 3:
patch = image[clipped_patch_boundingbox[0]:clipped_patch_boundingbox[2], clipped_patch_boundingbox[1]:clipped_patch_boundingbox[3], :]
patch = np.pad(patch, [(row_padding_before, row_padding_after), (col_padding_before, col_padding_after), (0, 0)], mode="constant")
else:
print("Image input does not have the right shape/")
patch = None
return patch
def make_grid(images, padding=2, pad_value=0, return_offsets=False):
nmaps = images.shape[0]
ymaps = int(math.floor(math.sqrt(nmaps)))
xmaps = nmaps // ymaps
height, width = int(images.shape[1] + padding), int(images.shape[2] + padding)
grid = np.zeros((height * ymaps + padding, width * xmaps + padding, images.shape[3])) + pad_value
k = 0
offsets = []
for y in range(ymaps):
for x in range(xmaps):
if k >= nmaps:
break
x_offset = x * width + padding
y_offset = y * height + padding
grid[y * height + padding:(y+1) * height, x * width + padding:(x+1) * width, :] = images[k]
offsets.append((x_offset, y_offset))
k = k + 1
if return_offsets:
return grid, offsets
else:
return grid
if __name__ == "__main__":
im_rows = 5
im_cols = 10
stride = 1
patch_res = 15
image = np.random.randint(0, 256, size=(im_rows, im_cols, 3), dtype=np.uint8)
image = Image.fromarray(image)
image = np.array(image)
plt.ion()
plt.figure(1)
plt.imshow(image)
plt.show()
# Cut patches
patch_boundingboxes = compute_patch_boundingboxes(image.shape[0:2], stride, patch_res)
plt.figure(2)
for patch_boundingbox in patch_boundingboxes:
patch = crop_or_pad_image_with_boundingbox(image, patch_boundingbox)
plt.imshow(patch)
plt.show()
input("Press <Enter> to finish...")
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