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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
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):
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))
parser = argparse.ArgumentParser()
parser.add_argument("--name", type=str, default="obama")
parser.add_argument("--size", type=int, default=512)
parser.add_argument("--noseg", default=False, action="store_true" )
args = parser.parse_args()
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, args.name )
try:
image = load_image(os.path.join(input_folder, "img.png" ), size = args.size)
except:
image = load_image(os.path.join(input_folder, "img.jpg" ), size = args.size)
image =Image.fromarray(image)
image.save(os.path.join(input_folder,"img_{}.png".format(args.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, args.name)
os.makedirs(save_folder, exist_ok=True)
draw_panoptic_segmentation(**panoptic_segmentation, save_folder = save_folder, noseg = args.noseg) |