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on
CPU Upgrade
Running
on
CPU Upgrade
import os | |
import cv2 | |
from PIL import Image | |
import gradio as gr | |
import numpy as np | |
import random | |
import base64 | |
import requests | |
import json | |
def start_tryon(person_img, garment_img, seed, randomize_seed): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
encoded_person_img = cv2.imencode('.jpg', cv2.cvtColor(person_img, cv2.COLOR_RGB2BGR))[1].tobytes() | |
encoded_person_img = base64.b64encode(encoded_person_img).decode('utf-8') | |
encoded_garment_img = cv2.imencode('.jpg', cv2.cvtColor(garment_img, cv2.COLOR_RGB2BGR))[1].tobytes() | |
encoded_garment_img = base64.b64encode(encoded_garment_img).decode('utf-8') | |
url = "https://" + os.environ['tryon_url'] | |
token = os.environ['token'] | |
cookie = os.environ['Cookie'] | |
headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie} | |
data = { | |
"clothImage": encoded_garment_img, | |
"humanImage": encoded_person_img, | |
"seed": seed | |
} | |
response = requests.post(url, headers=headers, data=json.dumps(data)) | |
print("response code", response.status_code) | |
result_img = None | |
if response.status_code == 200: | |
result = response.json()['result'] | |
status = result['status'] | |
if status == "success": | |
result = base64.b64decode(result['result']) | |
result_np = np.frombuffer(result, np.uint8) | |
result_img = cv2.imdecode(result_np, cv2.IMREAD_UNCHANGED) | |
result_img = cv2.cvtColor(result_img, cv2.COLOR_RGB2BGR) | |
info = "Success" | |
else: | |
info = "Try again latter" | |
else: | |
info = "URL error" | |
return result_img, seed, info | |
MAX_SEED = 999999 | |
example_path = os.path.join(os.path.dirname(__file__), 'assets') | |
garm_list = os.listdir(os.path.join(example_path,"cloth")) | |
garm_list_path = [os.path.join(example_path,"cloth",garm) for garm in garm_list] | |
human_list = os.listdir(os.path.join(example_path,"human")) | |
human_list_path = [os.path.join(example_path,"human",human) for human in human_list] | |
css=""" | |
#col-left { | |
margin: 0 auto; | |
max-width: 380px; | |
} | |
#col-mid { | |
margin: 0 auto; | |
max-width: 380px; | |
} | |
#col-right { | |
margin: 0 auto; | |
max-width: 520px; | |
} | |
#col-showcase { | |
margin: 0 auto; | |
max-width: 1000px; | |
} | |
#button { | |
color: blue; | |
} | |
""" | |
def load_description(fp): | |
with open(fp, 'r', encoding='utf-8') as f: | |
content = f.read() | |
return content | |
with gr.Blocks(css=css) as Tryon: | |
gr.HTML(load_description("assets/title.md")) | |
with gr.Row(): | |
with gr.Column(elem_id = "col-left"): | |
imgs = gr.Image(label="Person image", sources='upload', type="numpy") | |
# category = gr.Dropdown(label="Garment category", choices=['upper_body', 'lower_body', 'dresses'], value="upper_body") | |
example = gr.Examples( | |
inputs=imgs, | |
examples_per_page=10, | |
examples=human_list_path | |
) | |
with gr.Column(elem_id = "col-mid"): | |
garm_img = gr.Image(label="Garment image", sources='upload', type="numpy") | |
example = gr.Examples( | |
inputs=garm_img, | |
examples_per_page=10, | |
examples=garm_list_path) | |
with gr.Column(elem_id = "col-right"): | |
image_out = gr.Image(label="Output", show_share_button=False) | |
with gr.Row(): | |
seed_used = gr.Number(label="Seed Used") | |
result_info = gr.Text(label="Info") | |
try_button = gr.Button(value="Try-on", elem_id="button") | |
with gr.Column(): | |
with gr.Accordion(label="Advanced Settings", open=False): | |
seed = gr.Slider( | |
label="Seed", | |
minimum=0, | |
maximum=MAX_SEED, | |
step=1, | |
value=0, | |
) | |
randomize_seed = gr.Checkbox(label="Randomize seed", value=True) | |
try_button.click(fn=start_tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon') | |
gr.HTML(""" | |
<div style="display: flex; justify-content: center; align-items: center; text-align: center;"> | |
<div> | |
<h1>Show Case</h1> | |
</div> | |
</div> | |
""") | |
with gr.Column(elem_id = "col-showcase"): | |
with gr.Row(): | |
image1 = gr.Image(label="Model", scale=1, value="assets/examples/model1.png", show_share_button=False, type="numpy") | |
image2 = gr.Image(label="Garment", scale=1, value="assets/examples/garment1.png", show_share_button=False, type="numpy") | |
image3 = gr.Image(label="Result", scale=1, value="assets/examples/result1.png", show_share_button=False, type="numpy") | |
show_case = gr.Examples( | |
examples=[ | |
["assets/examples/model1.png", "assets/examples/garment1.png", "assets/examples/result1.png"], | |
["assets/examples/model2.png", "assets/examples/garment2.png", "assets/examples/result2.png"] | |
], | |
inputs=[image1, image2, image3], | |
label=None | |
) | |
ip = requests.get('http://ifconfig.me/ip', timeout=1).text.strip() | |
print("ip address", ip) | |
Tryon.queue(max_size=10).launch() | |