def show_featuremap(featuremap, win_name='feature_map'):
print("Feature map shape: ", featuremap.shape)
print("Feature part value: ", featuremap[0, 0:2, 0:10, 0:10])
if featuremap.type() == 'torch.FloatTensor':
featuremap = torch.mean(featuremap, dim=1)
featuremap = featuremap.detach()
featuremap = featuremap.numpy()[0]
elif featuremap.type() == 'torch.ByteTensor':
featuremap = featuremap.detach()
featuremap = featuremap.numpy()[0, 0]
wwtool.show_grayscale_as_heatmap(featuremap, win_name=win_name, wait_time=10)
def show_featuremap(featuremap, win_name='feature_map'):
if featuremap.type() == 'torch.FloatTensor':
print("original feature map shape: ", featuremap.shape)
featuremap = torch.mean(featuremap, dim=1)
print("mean feature map shape: ", featuremap.shape)
featuremap = featuremap.detach()
featuremap = featuremap.numpy()[0]
print("numpy feature map shape: ", featuremap.shape)
print("max num: {}, min num: {}".format(featuremap.max(),
featuremap.min()))
min_num = np.percentile(featuremap, 5)
max_num = np.percentile(featuremap, 90)
print("percentile max: {}, percentile min: {}".format(
max_num, min_num))
featuremap = rescale(featuremap, min_num, max_num)
elif featuremap.type() == 'torch.ByteTensor':
featuremap = featuremap.detach()
featuremap = featuremap.numpy()[0, 0]
heatmap = wwtool.show_grayscale_as_heatmap(featuremap,
win_name=win_name,
wait_time=500,
return_img=True)
cv2.imwrite(
'/home/jwwangchn/Documents/Nutstore/100-Work/110-Projects/2019-DOTA/05-CVPR/supplementary/heatmap/save.png',
heatmap)
def show_polygons_on_image(masks,
img,
alpha=0.4,
output_file=None):
"""show masks on image
Args:
masks (list): list of coordinate
img (np.array): original image
alpha (int): compress
output_file (str): save path
"""
color_list = list(wwtool.COLORS.keys())
img_h, img_w, _ = img.shape
foreground = wwtool.generate_image(img_h, img_w, (0, 0, 0))
for idx, mask in enumerate(masks):
mask = np.array(mask).reshape(1, -1, 2)
cv2.fillPoly(foreground, mask, (wwtool.COLORS[color_list[idx % 20]][2], wwtool.COLORS[color_list[idx % 20]][1], wwtool.COLORS[color_list[idx % 20]][0]))
heatmap = wwtool.show_grayscale_as_heatmap(foreground / 255.0, show=False, return_img=True)
beta = (1.0 - alpha)
fusion = cv2.addWeighted(heatmap, alpha, img, beta, 0.0)
if output_file is not None:
cv2.imwrite(output_file, fusion)
else:
wwtool.show_image(fusion, save_name=None)
return fusion
rgb_img = cv2.imread(image_file)
mask_parser = wwtool.MaskParse()
objects = mask_parser(mask_file, category=(1, 3))
gt_masks = []
for obj in objects:
mask = obj['segmentation']
gt_masks.append([mask])
img = wwtool.generate_image(2048, 2048, (0, 0, 0))
COLORS = {'Blue': (0, 130, 200), 'Red': (230, 25, 75), 'Yellow': (255, 225, 25), 'Green': (60, 180, 75), 'Orange': (245, 130, 48), 'Purple': (145, 30, 180), 'Cyan': (70, 240, 240), 'Magenta': (240, 50, 230), 'Lavender': (230, 190, 255), 'Lime': (210, 245, 60), 'Teal': (0, 128, 128), 'Pink': (250, 190, 190), 'Brown': (170, 110, 40), 'Beige': (255, 250, 200), 'Maroon': (128, 0, 0), 'Mint': (170, 255, 195), 'Olive': (128, 128, 0), 'Apricot': (255, 215, 180), 'Navy': (0, 0, 128), 'Grey': (128, 128, 128), 'White': (255, 255, 255), 'Black': (0, 0, 0)}
color_list = list(COLORS.keys())
masks = wwtool.generate_image(2048, 2048)
for idx, gt_mask in enumerate(gt_masks):
mask = poly2mask(gt_mask, 2048, 2048) * 1
masks[:, :, 0] = mask * COLORS[color_list[idx % 20]][2]
masks[:, :, 1] = mask * COLORS[color_list[idx % 20]][1]
masks[:, :, 2] = mask * COLORS[color_list[idx % 20]][0]
img += masks
heatmap = wwtool.show_grayscale_as_heatmap(img / 255.0, show=False, return_img=True)
alpha = 0.4
beta = (1.0 - alpha)
fusion = cv2.addWeighted(heatmap, alpha, rgb_img, beta, 0.0)
draw_grid(fusion, pxstep=1024)
wwtool.show_image(fusion, save_name='{}.png'.format(image_file_name))