Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,772 Bytes
d807efd 3f3b681 d807efd 850ea5b d807efd 3f3b681 d807efd 850ea5b d807efd 850ea5b 8963af6 850ea5b 3f3b681 5569753 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from transformers import AutoImageProcessor, Mask2FormerForUniversalSegmentation
from PIL import Image
import torch
from collections import defaultdict
import matplotlib.pyplot as plt
from matplotlib import cm
import matplotlib.patches as mpatches
import os
import numpy as np
import argparse
import matplotlib
import gradio as gr
def load_image(image_path, left=0, right=0, top=0, bottom=0, size = 512):
if type(image_path) is str:
image = np.array(Image.open(image_path))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((size, size)))
return image
def draw_panoptic_segmentation(segmentation, segments_info,save_folder=None, noseg = False, model =None):
if torch.max(segmentation)==torch.min(segmentation)==-1:
print("nothing is detected!")
noseg=True
viridis = matplotlib.colormaps['viridis'].resampled(1)
else:
viridis = matplotlib.colormaps['viridis'].resampled(torch.max(segmentation)-torch.min(segmentation)+1)
fig, ax = plt.subplots()
ax.imshow(segmentation)
instances_counter = defaultdict(int)
handles = []
label_list = []
if not noseg:
if torch.min(segmentation) == 0:
mask = segmentation==0
mask = mask.cpu().detach().numpy() # [512,512] bool
segment_label = "rest"
np.save( os.path.join(save_folder, "mask{}_{}.npy".format(0,"rest")) , mask)
color = viridis(0)
label = f"{segment_label}-{0}"
handles.append(mpatches.Patch(color=color, label=label))
label_list.append(label)
for segment in segments_info:
segment_id = segment['id']
mask = segmentation==segment_id
if torch.min(segmentation) != 0:
segment_id -= 1
mask = mask.cpu().detach().numpy() # [512,512] bool
segment_label = model.config.id2label[segment['label_id']]
instances_counter[segment['label_id']] += 1
np.save( os.path.join(save_folder, "mask{}_{}.npy".format(segment_id,segment_label)) , mask)
color = viridis(segment_id)
label = f"{segment_label}-{segment_id}"
handles.append(mpatches.Patch(color=color, label=label))
label_list.append(label)
else:
mask = np.full(segmentation.shape, True)
segment_label = "all"
np.save( os.path.join(save_folder, "mask{}_{}.npy".format(0,"all")) , mask)
color = viridis(0)
label = f"{segment_label}-{0}"
handles.append(mpatches.Patch(color=color, label=label))
label_list.append(label)
plt.xticks([])
plt.yticks([])
# plt.savefig(os.path.join(save_folder, 'mask_clear.png'), dpi=500)
ax.legend(handles=handles)
plt.savefig(os.path.join(save_folder, 'seg_init.png'), dpi=500 )
print("; ".join(label_list))
def run_segmentation(image, name="example_tmp", size = 512, noseg=False):
base_folder_path = "."
processor = AutoImageProcessor.from_pretrained("facebook/mask2former-swin-base-coco-panoptic")
model = Mask2FormerForUniversalSegmentation.from_pretrained("facebook/mask2former-swin-base-coco-panoptic")
# input_folder = os.path.join(base_folder_path, name )
# try:
# image = load_image(os.path.join(input_folder, "img.png" ), size = size)
# except:
# image = load_image(os.path.join(input_folder, "img.jpg" ), size = size)
image =Image.fromarray(image)
image = image.resize((size, size))
os.makedirs(name, exist_ok=True)
image.save(os.path.join(name,"img_{}.png".format(size)))
inputs = processor(image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
panoptic_segmentation = processor.post_process_panoptic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
save_folder = os.path.join(base_folder_path, name)
os.makedirs(save_folder, exist_ok=True)
draw_panoptic_segmentation(**panoptic_segmentation, save_folder = save_folder, noseg = noseg, model = model)
print("Finish segment")
#block_flag += 1
return gr.Button.update("1.2 Load original masks",visible = True)#, gr.Button.update("1.2 Load edited masks",visible = True), gr.Checkbox.update(label = "Show Segmentation",visible = True)
|