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"""
Using as reference:
- https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512
- https://huggingface.co/spaces/chansung/segformer-tf-transformers/blob/main/app.py
- https://huggingface.co/facebook/detr-resnet-50-panoptic
# https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/DETR/DETR_panoptic_segmentation_minimal_example_(with_DetrFeatureExtractor).ipynb
https://arxiv.org/abs/2005.12872
Additions
- add shown labels as strings
- show only animal masks (ask an nlp model?)
"""
from transformers import DetrFeatureExtractor, DetrForSegmentation
from PIL import Image
import gradio as gr
import numpy as np
import torch
import torchvision
# Returns a list with a color per ADE class (150 classes)
# from https://huggingface.co/spaces/chansung/segformer-tf-transformers/blob/main/app.py
def ade_palette():
"""ADE20K palette that maps each class to RGB values."""
return [
[120, 120, 120],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[140, 140, 140],
[204, 5, 255],
[230, 230, 230],
[4, 250, 7],
[224, 5, 255],
[235, 255, 7],
[150, 5, 61],
[120, 120, 70],
[8, 255, 51],
[255, 6, 82],
[143, 255, 140],
[204, 255, 4],
[255, 51, 7],
[204, 70, 3],
[0, 102, 200],
[61, 230, 250],
[255, 6, 51],
[11, 102, 255],
[255, 7, 71],
[255, 9, 224],
[9, 7, 230],
[220, 220, 220],
[255, 9, 92],
[112, 9, 255],
[8, 255, 214],
[7, 255, 224],
[255, 184, 6],
[10, 255, 71],
[255, 41, 10],
[7, 255, 255],
[224, 255, 8],
[102, 8, 255],
[255, 61, 6],
[255, 194, 7],
[255, 122, 8],
[0, 255, 20],
[255, 8, 41],
[255, 5, 153],
[6, 51, 255],
[235, 12, 255],
[160, 150, 20],
[0, 163, 255],
[140, 140, 140],
[250, 10, 15],
[20, 255, 0],
[31, 255, 0],
[255, 31, 0],
[255, 224, 0],
[153, 255, 0],
[0, 0, 255],
[255, 71, 0],
[0, 235, 255],
[0, 173, 255],
[31, 0, 255],
[11, 200, 200],
[255, 82, 0],
[0, 255, 245],
[0, 61, 255],
[0, 255, 112],
[0, 255, 133],
[255, 0, 0],
[255, 163, 0],
[255, 102, 0],
[194, 255, 0],
[0, 143, 255],
[51, 255, 0],
[0, 82, 255],
[0, 255, 41],
[0, 255, 173],
[10, 0, 255],
[173, 255, 0],
[0, 255, 153],
[255, 92, 0],
[255, 0, 255],
[255, 0, 245],
[255, 0, 102],
[255, 173, 0],
[255, 0, 20],
[255, 184, 184],
[0, 31, 255],
[0, 255, 61],
[0, 71, 255],
[255, 0, 204],
[0, 255, 194],
[0, 255, 82],
[0, 10, 255],
[0, 112, 255],
[51, 0, 255],
[0, 194, 255],
[0, 122, 255],
[0, 255, 163],
[255, 153, 0],
[0, 255, 10],
[255, 112, 0],
[143, 255, 0],
[82, 0, 255],
[163, 255, 0],
[255, 235, 0],
[8, 184, 170],
[133, 0, 255],
[0, 255, 92],
[184, 0, 255],
[255, 0, 31],
[0, 184, 255],
[0, 214, 255],
[255, 0, 112],
[92, 255, 0],
[0, 224, 255],
[112, 224, 255],
[70, 184, 160],
[163, 0, 255],
[153, 0, 255],
[71, 255, 0],
[255, 0, 163],
[255, 204, 0],
[255, 0, 143],
[0, 255, 235],
[133, 255, 0],
[255, 0, 235],
[245, 0, 255],
[255, 0, 122],
[255, 245, 0],
[10, 190, 212],
[214, 255, 0],
[0, 204, 255],
[20, 0, 255],
[255, 255, 0],
[0, 153, 255],
[0, 41, 255],
[0, 255, 204],
[41, 0, 255],
[41, 255, 0],
[173, 0, 255],
[0, 245, 255],
[71, 0, 255],
[122, 0, 255],
[0, 255, 184],
[0, 92, 255],
[184, 255, 0],
[0, 133, 255],
[255, 214, 0],
[25, 194, 194],
[102, 255, 0],
[92, 0, 255],
]
def predict_animal_mask(im,
gr_slider_confidence):
image = Image.fromarray(im) # im: numpy array 3d: 480, 640, 3: to PIL Image
image = image.resize((200,200)) # PIL image # could I upsample output instead? better?
# encoding is a dict with pixel_values and pixel_mask
encoding = feature_extractor(images=image, return_tensors="pt") #pt=Pytorch, tf=TensorFlow
outputs = model(**encoding) # odict with keys: ['logits', 'pred_boxes', 'pred_masks', 'last_hidden_state', 'encoder_last_hidden_state']
logits = outputs.logits # torch.Size([1, 100, 251]); why 251?
bboxes = outputs.pred_boxes
masks = outputs.pred_masks # torch.Size([1, 100, 200, 200]); for every pixel, score in each of the 100 classes? there is a mask per class
# keep only the masks with high confidence?--------------------------------
# compute the prob per mask (i.e., class), excluding the "no-object" class (the last one)
prob_per_query = outputs.logits.softmax(-1)[..., :-1].max(-1)[0] # why logits last dim 251?
# threshold the confidence
keep = prob_per_query > gr_slider_confidence/100.0
# postprocess the mask (numpy arrays)
label_per_pixel = torch.argmax(masks[keep].squeeze(),dim=0).detach().numpy() # from the masks per class, select the highest per pixel
color_mask = np.zeros(image.size+(3,))
for lbl, color in enumerate(ade_palette()):
color_mask[label_per_pixel==lbl,:] = color
# Show image + mask
pred_img = np.array(image.convert('RGB'))*0.5 + color_mask*0.5
pred_img = pred_img.astype(np.uint8)
return pred_img
#######################################
# get models from hugging face
feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50-panoptic')
model = DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')
# gradio components -inputs
gr_image_input = gr.inputs.Image()
gr_slider_confidence = gr.inputs.Slider(0,100,5,85,
label='Set confidence threshold for masks')
# gradio outputs
gr_image_output = gr.outputs.Image()
####################################################
# Create user interface and launch
gr.Interface(predict_animal_mask,
inputs = [gr_image_input,gr_slider_confidence],
outputs = gr_image_output,
title = 'Image segmentation with varying confidence',
description = "An image segmentation webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()
####################################
# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
# image = Image.open(requests.get(url, stream=True).raw)
# inputs = feature_extractor(images=image, return_tensors="pt")
# outputs = model(**inputs)
# logits = outputs.logits # shape (batch_size, num_labels, height/4, width/4)
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