Spaces:
Runtime error
Runtime error
File size: 6,343 Bytes
ca30234 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 |
import os
import cv2
import spaces
from PIL import Image
import gradio as gr
import numpy as np
import random
import base64
import requests
import json
import time
# Add a new function for text-to-image generation
def generate_garment_image(prompt):
# This is a placeholder function. You'll need to implement actual text-to-image generation here.
# For example, you might use a service like DALL-E, Stable Diffusion, or any other text-to-image model.
# For now, we'll just return a placeholder image.
placeholder_image = np.zeros((256, 256, 3), dtype=np.uint8)
cv2.putText(placeholder_image, prompt, (10, 128), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
return placeholder_image
def tryon(person_img, garment_prompt, seed, randomize_seed):
post_start_time = time.time()
if person_img is None or garment_prompt == "":
return None, None, "Empty image or prompt"
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Generate garment image from prompt
garment_img = generate_garment_image(garment_prompt)
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')
# Rest of the function remains the same
# ...
def start_tryon(person_img, garment_prompt, seed, randomize_seed):
start_time = time.time()
if person_img is None or garment_prompt == "":
return None, None, "Empty image or prompt"
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Generate garment image from prompt
garment_img = generate_garment_image(garment_prompt)
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')
# Rest of the function remains the same
# ...
MAX_SEED = 999999
example_path = os.path.join(os.path.dirname(__file__), 'assets')
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: 430px;
}
#col-mid {
margin: 0 auto;
max-width: 430px;
}
#col-right {
margin: 0 auto;
max-width: 430px;
}
#col-showcase {
margin: 0 auto;
max-width: 1100px;
}
#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"):
gr.HTML("""
<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
<div>
Step 1. Upload a person image ⬇️
</div>
</div>
""")
with gr.Column(elem_id = "col-mid"):
gr.HTML("""
<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
<div>
Step 2. Enter a garment description ⬇️
</div>
</div>
""")
with gr.Column(elem_id = "col-right"):
gr.HTML("""
<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
<div>
Step 3. Press "Run" to get try-on results
</div>
</div>
""")
with gr.Row():
with gr.Column(elem_id = "col-left"):
imgs = gr.Image(label="Person image", sources='upload', type="numpy")
example = gr.Examples(
inputs=imgs,
examples_per_page=12,
examples=human_list_path
)
with gr.Column(elem_id = "col-mid"):
garm_prompt = gr.Textbox(label="Garment description", placeholder="Enter a description of the garment...")
example_prompts = gr.Examples(
inputs=garm_prompt,
examples=["A red t-shirt", "Blue jeans", "A floral summer dress", "A black leather jacket"]
)
with gr.Column(elem_id = "col-right"):
image_out = gr.Image(label="Result", show_share_button=False)
with gr.Row():
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Random seed", value=True)
with gr.Row():
seed_used = gr.Number(label="Seed used")
result_info = gr.Text(label="Response")
test_button = gr.Button(value="Run", elem_id="button")
test_button.click(fn=tryon, inputs=[imgs, garm_prompt, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon', concurrency_limit=40)
with gr.Column(elem_id = "col-showcase"):
gr.HTML("""
<div style="display: flex; justify-content: center; align-items: center; text-align: center; font-size: 20px;">
<div> </div>
<br>
<div>
Virtual try-on examples in pairs of person images and garment descriptions
</div>
</div>
""")
show_case = gr.Examples(
examples=[
["assets/examples/model2.png", "A blue t-shirt", "assets/examples/result2.png"],
["assets/examples/model3.png", "A red dress", "assets/examples/result3.png"],
["assets/examples/model1.png", "A black suit", "assets/examples/result1.png"],
],
inputs=[imgs, garm_prompt, image_out],
label=None
)
Tryon.launch() |