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| from transformers import SegformerImageProcessor, AutoModelForSemanticSegmentation | |
| from PIL import Image | |
| import requests | |
| import matplotlib.pyplot as plt | |
| import torch.nn as nn | |
| FASHION_MAP = { | |
| "0":"Everything Else", "1": "shirt, blouse", "2": "top, t-shirt, sweatshirt", | |
| "3": "sweater", "4": "cardigan", "5": "jacket", "6": "vest", "7": "pants", | |
| "8": "shorts", "9": "skirt", "10": "coat", "11": "dress", "12": "jumpsuit", | |
| "13": "cape", "14": "glasses", "15": "hat", "16": "headband, head covering, hair accessory", | |
| "17": "tie", "18": "glove", "19": "watch", "20": "belt", "21": "leg warmer", | |
| "22": "tights, stockings", "23": "sock", "24": "shoe", "25": "bag, wallet", | |
| "26": "scarf", "27": "umbrella", "28": "hood", "29": "collar", "30": "lapel", | |
| "31": "epaulette", "32": "sleeve", "33": "pocket", "34": "neckline", "35": "buckle", | |
| "36": "zipper", "37": "applique", "38": "bead", "39": "bow", "40": "flower", "41": "fringe", | |
| "42": "ribbon", "43": "rivet", "44": "ruffle", "45": "sequin", "46": "tassel" | |
| } | |
| HUMAN_MAP = { | |
| "0":"Background","1":"shirt, blouse","2":"top, t-shirt, sweatshirt","3":"sweater", | |
| "4":"cardigan","5":"jacket","6":"vest","7":"pants","8":"shorts","9":"skirt", | |
| "10":"coat","11":"dress","12":"jumpsuit","13":"cape","14":"glasses","15":"hat", | |
| "16":"headband, head covering, hair accessory","17":"tie","18":"glove","19":"watch", | |
| "20":"belt","21":"leg warmer","22":"tights, stockings","23":"sock","24":"shoe", | |
| "25":"bag, wallet","26":"scarf","27":"umbrella","28":"hood","29":"collar","30":"lapel", | |
| "31":"epaulette","32":"sleeve","33":"pocket","34":"neckline","35":"buckle","36":"zipper", | |
| "37":"applique","38":"bead","39":"bow","40":"flower","41":"fringe","42":"ribbon", | |
| "43":"rivet","44":"ruffle","45":"sequin","46":"tassel","47":"Hair","48":"Sunglasses", | |
| "49":"Upper-clothes","50":"Left-shoe","51":"Right-shoe","52":"Face","53":"Left-leg", | |
| "54":"Right-leg","55":"Left-arm","56":"Right-arm" | |
| } | |
| class Segformer: | |
| def __init__(self, model_name, device='cuda'): | |
| self.device = device | |
| self.processor = SegformerImageProcessor.from_pretrained(model_name) | |
| self.model = AutoModelForSemanticSegmentation.from_pretrained(model_name).to(device) | |
| def predict(self, image: Image): | |
| inputs = self.processor(images=image, return_tensors="pt").to(self.device) | |
| outputs = self.model(**inputs) | |
| logits = outputs.logits.cpu() | |
| upsampled_logits = nn.functional.interpolate( | |
| logits, | |
| size=image.size[::-1], | |
| mode="bilinear", | |
| align_corners=False, | |
| ) | |
| pred_seg = upsampled_logits.argmax(dim=1)[0] | |
| # to PIL image | |
| pred_seg = Image.fromarray(pred_seg.byte().cpu().numpy()) | |
| return pred_seg | |
| def __call__(self, image: Image): | |
| return self.predict(image) | |