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import cv2
import random
import numpy as np
from PIL import Image
def get_strided_size(orig_size, stride):
return ((orig_size[0]-1)//stride+1, (orig_size[1]-1)//stride+1)
def get_strided_up_size(orig_size, stride):
strided_size = get_strided_size(orig_size, stride)
return strided_size[0]*stride, strided_size[1]*stride
def imshow(image, delay=0, mode='RGB', title='show'):
if mode == 'RGB':
demo_image = image[..., ::-1]
else:
demo_image = image
cv2.imshow(title, demo_image)
if delay >= 0:
cv2.waitKey(delay)
def transpose(image):
return image.transpose((1, 2, 0))
def denormalize(image, mean=None, std=None, dtype=np.uint8, tp=True):
if tp:
image = transpose(image)
if mean is not None:
image = (image * std) + mean
if dtype == np.uint8:
image *= 255.
return image.astype(np.uint8)
else:
return image
def colormap(cam, shape=None, mode=cv2.COLORMAP_JET):
if shape is not None:
h, w, c = shape
cam = cv2.resize(cam, (w, h))
cam = cv2.applyColorMap(cam, mode)
return cam
def decode_from_colormap(data, colors):
ignore = (data == 255).astype(np.int32)
mask = 1 - ignore
data *= mask
h, w = data.shape
image = colors[data.reshape((h * w))].reshape((h, w, 3))
ignore = np.concatenate([ignore[..., np.newaxis], ignore[..., np.newaxis], ignore[..., np.newaxis]], axis=-1)
image[ignore.astype(np.bool)] = 255
return image
def normalize(cam, epsilon=1e-5):
cam = np.maximum(cam, 0)
max_value = np.max(cam, axis=(0, 1), keepdims=True)
return np.maximum(cam - epsilon, 0) / (max_value + epsilon)
def crf_inference(img, probs, t=10, scale_factor=1, labels=21):
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_softmax
h, w = img.shape[:2]
n_labels = labels
d = dcrf.DenseCRF2D(w, h, n_labels)
unary = unary_from_softmax(probs)
unary = np.ascontiguousarray(unary)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=3/scale_factor, compat=3)
d.addPairwiseBilateral(sxy=80/scale_factor, srgb=13, rgbim=np.copy(img), compat=10)
Q = d.inference(t)
return np.array(Q).reshape((n_labels, h, w))
def crf_with_alpha(ori_image, cams, alpha):
# h, w, c -> c, h, w
# cams = cams.transpose((2, 0, 1))
bg_score = np.power(1 - np.max(cams, axis=0, keepdims=True), alpha)
bgcam_score = np.concatenate((bg_score, cams), axis=0)
cams_with_crf = crf_inference(ori_image, bgcam_score, labels=bgcam_score.shape[0])
# return cams_with_crf.transpose((1, 2, 0))
return cams_with_crf
def crf_inference_label(img, labels, t=10, n_labels=21, gt_prob=0.7):
import pydensecrf.densecrf as dcrf
from pydensecrf.utils import unary_from_labels
h, w = img.shape[:2]
d = dcrf.DenseCRF2D(w, h, n_labels)
unary = unary_from_labels(labels, n_labels, gt_prob=gt_prob, zero_unsure=False)
d.setUnaryEnergy(unary)
d.addPairwiseGaussian(sxy=3, compat=3)
d.addPairwiseBilateral(sxy=50, srgb=5, rgbim=np.ascontiguousarray(np.copy(img)), compat=10)
q = d.inference(t)
return np.argmax(np.array(q).reshape((n_labels, h, w)), axis=0) |