metadata
tags:
- element_type:detect
- model:segformer-b0
- object:person
manako:
description: >-
Semantic segmentation is a computer vision technique for assigning a label
to each pixel in an image, representing the semantic class of the objects or
regions in the image. It's a form of dense prediction because it involves
assigning a label to each pixel in an image, instead of just boxes around
objects or key points as in object detection or instance segmentation. The
goal of semantic segmentation is to recognize and understand the objects and
scenes in an image, and partition the image into segments corresponding to
different entities.
source: https://huggingface.co/s3nh/SegFormer-b0-person-segmentation
input_payload:
- name: frame
type: image
description: RGB frame
output_payload:
- name: detections
type: detections
description: List of detections
evaluation_score: 0.45983522025589574
Description
Semantic segmentation is a computer vision technique for assigning a label to each pixel in an image, representing the semantic class of the objects or regions in the image. It's a form of dense prediction because it involves assigning a label to each pixel in an image, instead of just boxes around objects or key points as in object detection or instance segmentation. The goal of semantic segmentation is to recognize and understand the objects and scenes in an image, and partition the image into segments corresponding to different entities.
Parameters
model = SegformerForSemanticSegmentation.from_pretrained("/notebooks/segformer_5_epoch",
num_labels=2,
id2label=id2label,
label2id=label2id, )
Usage
from torch import nn
import numpy as np
import matplotlib.pyplot as plt
# Transforms
_transform = A.Compose([
A.Resize(height = 512, width=512),
ToTensorV2(),
])
trans_image = _transform(image=np.array(image))
outputs = model(trans_image['image'].float().unsqueeze(0))
logits = outputs.logits.cpu()
print(logits.shape)
# First, rescale logits to original image size
upsampled_logits = nn.functional.interpolate(logits,
size=image.size[::-1], # (height, width)
mode='bilinear',
align_corners=False)
seg = upsampled_logits.argmax(dim=1)[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
palette = np.array([[0, 0, 0],[255, 255, 255]])
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
# Convert to BGR
color_seg = color_seg[..., ::-1]