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import torch.nn.functional as F
import numpy as np
import torch
markdown_default = """
<link href="https://fonts.googleapis.com/css2?family=Montserrat:wght@400;700&display=swap" rel="stylesheet">
<style>
.highlighted-text {
font-family: 'Montserrat', sans-serif;
font-weight: 600;
font-size: 14px;
color: rgb(255, 255, 239);
background-color: rgb(225, 231, 254);
border-radius: 7px;
padding: 5px 7px;
display: inline-block;
}
.regular-text {
font-family: 'Montserrat', sans-serif;
font-weight: 400;
font-size: 14px;
}
.highlighted-response {
font-family: 'Montserrat', sans-serif;
font-weight: 600;
font-size: 14px;
border-radius: 6px;
padding: 3px 4px;
display: inline-block;
}
</style>
<span class="highlighted-text" style='color:rgb(107, 100, 239)'>OMG-LLaVA</span>
"""
description = """
**Usage** : <br>
 (1) For **Grounded Caption Generation** Interleaved Segmentation, input prompt like: *"Could you provide me with a detailed analysis of this photo? Please output with interleaved segmentation masks for the corresponding parts of the answer."* <br>
 (2) For **Segmentation Output**, input prompt like: *"Can you please segment xxx in the given image"* <br>
 (3) For **Image Captioning** VQA, input prompt like: *"Could you please give me a detailed description of the image?"* <br>
 (4) For **Image Conversation**, input arbitrary text instruction. <br>
 (5) For **Visual prompt description**, first draw point or box in the image, and insert \<point\> or \<box\> into the text instruction. The input prompt like: *"Can you provide me with a detailed description of the region in the picture marked by region1 \<point\>?"* <br>
"""
ONE_THIRD = 1.0/3.0
ONE_SIXTH = 1.0/6.0
TWO_THIRD = 2.0/3.0
def desaturate(rgb, factor=0.65):
"""
Desaturate an RGB color by a given factor.
:param rgb: A tuple of (r, g, b) where each value is in [0, 255].
:param factor: The factor by which to reduce the saturation.
0 means completely desaturated, 1 means original color.
:return: A tuple of desaturated (r, g, b) values in [0, 255].
"""
r, g, b = [x / 255.0 for x in rgb]
h, l, s = rgb_to_hls(r, g, b)
l = factor
new_r, new_g, new_b = hls_to_rgb(h, l, s)
return (int(new_r * 255), int(new_g * 255), int(new_b * 255))
def rgb_to_hls(r, g, b):
maxc = max(r, g, b)
minc = min(r, g, b)
sumc = (maxc+minc)
rangec = (maxc-minc)
l = sumc/2.0
if minc == maxc:
return 0.0, l, 0.0
if l <= 0.5:
s = rangec / sumc
else:
s = rangec / (2.0-sumc)
rc = (maxc-r) / rangec
gc = (maxc-g) / rangec
bc = (maxc-b) / rangec
if r == maxc:
h = bc-gc
elif g == maxc:
h = 2.0+rc-bc
else:
h = 4.0+gc-rc
h = (h/6.0) % 1.0
return h, l, s
def hls_to_rgb(h, l, s):
if s == 0.0:
return l, l, l
if l <= 0.5:
m2 = l * (1.0+s)
else:
m2 = l+s-(l*s)
m1 = 2.0*l - m2
return (_v(m1, m2, h+ONE_THIRD), _v(m1, m2, h), _v(m1, m2, h-ONE_THIRD))
def _v(m1, m2, hue):
hue = hue % 1.0
if hue < ONE_SIXTH:
return m1 + (m2-m1)*hue*6.0
if hue < 0.5:
return m2
if hue < TWO_THIRD:
return m1 + (m2-m1)*(TWO_THIRD-hue)*6.0
return m1
def process_markdown(output_str, colors):
output_str = output_str.replace("\n", "").replace(" ", " ").replace("<s>", "")\
.replace("<|im_end|>", '')
output_str = output_str.split("ASSISTANT: ")[-1]
markdown_out = output_str.replace('[SEG]', '')
markdown_out = markdown_out.replace(
"<p>", "<span class='highlighted-response' style='background-color:rgb[COLOR]'>"
)
markdown_out = markdown_out.replace("</p>", "</span>")
for color in colors:
markdown_out = markdown_out.replace("[COLOR]", str(desaturate(tuple(color))), 1)
markdown_out = f"""
{markdown_out}
"""
markdown_out = markdown_default + "<p><span class='regular-text'>" + markdown_out
return markdown_out
def show_mask_pred(image, masks, crop_range=(0, 1024, 0, 1024)):
print(crop_range)
selected_colors = []
colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255),
(255, 255, 0), (255, 0, 255), (0, 255, 255),
(128, 128, 255), [255, 192, 203], # Pink
[165, 42, 42], # Brown
[255, 165, 0], # Orange
[128, 0, 128], # Purple
[0, 0, 128], # Navy
[128, 0, 0], # Maroon
[128, 128, 0], # Olive
[70, 130, 180], # Steel Blue
[173, 216, 230], # Light Blue
[255, 192, 0], # Gold
[255, 165, 165], # Light Salmon
[255, 20, 147], # Deep Pink
]
masks = F.interpolate(masks, size=image.size, mode='bilinear', align_corners=False)
masks = masks.sigmoid() > 0.5
masks = masks.to(torch.uint8).cpu().numpy()[:, 0]
_mask_image = np.zeros((masks.shape[1], masks.shape[2], 3), dtype=np.uint8)
for i, mask in enumerate(masks):
color = colors[i % len(colors)]
selected_colors.append(color)
_mask_image[:, :, 0] = _mask_image[:, :, 0] + mask.astype(np.uint8) * color[0]
_mask_image[:, :, 1] = _mask_image[:, :, 1] + mask.astype(np.uint8) * color[1]
_mask_image[:, :, 2] = _mask_image[:, :, 2] + mask.astype(np.uint8) * color[2]
image = np.array(image)
image = image * 0.5 + _mask_image * 0.5
image = image.astype(np.uint8)
image = image[crop_range[2]: crop_range[3], crop_range[0]: crop_range[1], :]
return image, selected_colors
def parse_visual_prompts(points):
ret = {'points': [], 'boxes': []}
for item in points:
if item[2] == 1.0:
ret['points'].append([item[0], item[1]])
elif item[2] == 2.0 or item[2] == 3.0:
ret['boxes'].append([item[0], item[1], item[3], item[4]])
else:
raise NotImplementedError
return ret |