Spaces:
Runtime error
Runtime error
File size: 3,392 Bytes
d90aef7 9521e56 c3057ee 1f89823 78c0d52 6838488 78c0d52 d90aef7 78c0d52 d90aef7 78c0d52 d90aef7 1136f98 78c0d52 9521e56 bcfaff6 d90aef7 |
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 |
import gradio as gr
from transformers import pipeline
from PIL import Image
import pandas as pd
import numpy as np
import torch
import torch.nn as nn
import torchvision
from torchvision import datasets, models, transforms
from torch_mtcnn import detect_faces
from torch_mtcnn import show_bboxes
# pipeline = pipeline(task="image-classification", model="njgroene/fairface")
def pipeline(img):
bounding_boxes, landmarks = detect_faces(img)
bb = [bounding_boxes[0,0], bounding_boxes[0,1], bounding_boxes[0,2], bounding_boxes[0,3]]
img_cropped = img.crop(bb)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model_fair_7 = torchvision.models.resnet34(pretrained=True)
model_fair_7.fc = nn.Linear(model_fair_7.fc.in_features, 18)
model_fair_7.load_state_dict(torch.load('res34_fair_align_multi_7_20190809.pt', map_location=torch.device('cpu')))
model_fair_7 = model_fair_7.to(device)
model_fair_7.eval()
trans = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])
face_names = []
gender_scores_fair = []
age_scores_fair = []
gender_preds_fair = []
age_preds_fair = []
image = trans(img_cropped)
image = image.view(1, 3, 224, 224) # reshape image to match model dimensions (1 batch size)
image = image.to(device)
# fair 7 class
outputs = model_fair_7(image)
outputs = outputs.cpu().detach().numpy()
outputs = np.squeeze(outputs)
gender_outputs = outputs[7:9]
age_outputs = outputs[9:18]
gender_score = np.exp(gender_outputs) / np.sum(np.exp(gender_outputs))
age_score = np.exp(age_outputs) / np.sum(np.exp(age_outputs))
gender_pred = np.argmax(gender_score)
age_pred = np.argmax(age_score)
gender_scores_fair.append(gender_score)
age_scores_fair.append(age_score)
gender_preds_fair.append(gender_pred)
age_preds_fair.append(age_pred)
result = pd.DataFrame([gender_preds_fair,
age_preds_fair]).T
result.columns = ['gender_preds_fair',
'age_preds_fair']
# gender
result.loc[result['gender_preds_fair'] == 0, 'gender'] = 'Male'
result.loc[result['gender_preds_fair'] == 1, 'gender'] = 'Female'
# age
result.loc[result['age_preds_fair'] == 0, 'age'] = '0-2'
result.loc[result['age_preds_fair'] == 1, 'age'] = '3-9'
result.loc[result['age_preds_fair'] == 2, 'age'] = '10-19'
result.loc[result['age_preds_fair'] == 3, 'age'] = '20-29'
result.loc[result['age_preds_fair'] == 4, 'age'] = '30-39'
result.loc[result['age_preds_fair'] == 5, 'age'] = '40-49'
result.loc[result['age_preds_fair'] == 6, 'age'] = '50-59'
result.loc[result['age_preds_fair'] == 7, 'age'] = '60-69'
result.loc[result['age_preds_fair'] == 8, 'age'] = '70+'
return [result['gender'][0],result['age'][0]]
def predict(image):
predictions = pipeline(image)
return "A " + predictions[0] + " in the age range of " + predictions[1]
gr.Interface(
predict,
inputs=gr.inputs.Image(label="Upload a profile picture of a single person", type="pil"),
outputs=("text"),
title="Estimate age and gender from profile picture",
examples=["ex0.jpg","ex1.jpg","ex2.jpg","ex3.jpg"]
).launch() |