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import cv2
import numpy as np
from interactive_pipe import interactive
def get_color_channel_offset(image):
# size is defined in power of 2
if len(image.shape) == 2:
offset = 0
elif len(image.shape) == 3:
channel_guesser_max_size = 4
if image.shape[0] <= channel_guesser_max_size: # channel first C,H,W
offset = 0
elif image.shape[-1] <= channel_guesser_max_size: # channel last or numpy H,W,C
offset = 1
else:
raise NameError(f"Not supported shape {image.shape}")
return offset
def crop_selector(image, center_x=0.5, center_y=0.5, size=9., global_params={}):
offset = get_color_channel_offset(image)
crop_size_pixels = int(2.**(size)/2.)
h, w = image.shape[-2-offset], image.shape[-1-offset]
ar = w/h
half_crop_h, half_crop_w = crop_size_pixels, int(ar*crop_size_pixels)
def round(val):
return int(np.round(val))
center_x_int = round(half_crop_w + center_x*(w-2*half_crop_w))
center_y_int = round(half_crop_h + center_y*(h-2*half_crop_h))
start_x = max(0, center_x_int-half_crop_w)
start_y = max(0, center_y_int-half_crop_h)
end_x = min(start_x+2*half_crop_w, w-1)
end_y = min(start_y+2*half_crop_h, h-1)
start_x = max(0, end_x-2*half_crop_w)
start_y = max(0, end_y-2*half_crop_h)
MAX_ALLOWED_SIZE = 512
w_resize = int(min(MAX_ALLOWED_SIZE, w))
h_resize = int(w_resize/w*h)
h_resize = int(min(MAX_ALLOWED_SIZE, h_resize))
w_resize = int(h_resize/h*w)
global_params["crop"] = (start_x, start_y, end_x, end_y)
global_params["resize"] = (w_resize, h_resize)
return
def plug_crop_selector(num_pad: bool = False, low_resources: bool = False):
if low_resources:
size_control = (5., [5., 7., 0.3], "crop size", ["+", "-"])
else:
size_control = (9., [6., 13., 0.3], "crop size", ["+", "-"])
interactive(
center_x=(0.5, [0., 1.], "crop horizontally", ["4" if num_pad else "left", "6" if num_pad else "right"]),
center_y=(0.5, [0., 1.], "crop vertically", ["8" if num_pad else "up", "2" if num_pad else "down"]),
size=size_control
)(crop_selector)
def crop(*images, global_params={}):
images_resized = []
for image in images:
offset = get_color_channel_offset(image)
start_x, start_y, end_x, end_y = global_params["crop"]
w_resize, h_resize = global_params["resize"]
if offset == 0:
crop = image[..., start_y:end_y, start_x:end_x]
if offset == 1:
crop = image[..., start_y:end_y, start_x:end_x, :]
image_resized = cv2.resize(crop, (w_resize, h_resize), interpolation=cv2.INTER_NEAREST)
images_resized.append(image_resized)
return tuple(images_resized)
def rescale_thumbnail(image, global_params={}):
if image is None: # support no blur kernel!
return None
resize_dim = max(global_params.get("resize", (512, 512)))
return cv2.resize(image, (resize_dim, resize_dim), interpolation=cv2.INTER_NEAREST)
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