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README.md

@@ -1,12 +1,13 @@
 ---
-title: Info Try On Deep Learning
-emoji: 🦀
-colorFrom: pink
-colorTo: green
+title: Info-Try-on
+emoji: 👕
+colorFrom: purple
+colorTo: gray
 sdk: gradio
-sdk_version: 4.42.0
+sdk_version: 4.38.1
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,244 @@
+import os
+import cv2
+from PIL import Image
+import gradio as gr
+import numpy as np
+import random
+import base64
+import requests
+import json
+import time
+from requests.adapters import HTTPAdapter
+
+def tryon(person_img, garment_img, seed, randomize_seed):
+    post_start_time = time.time()
+    if person_img is None or garment_img is None:
+        return None, None, "Empty image"
+    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 = "http://" + os.environ['tryon_url'] + "Submit"
+    token = os.environ['token']
+    cookie = os.environ['Cookie']
+    referer = os.environ['referer']
+    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
+    data = {
+        "clothImage": encoded_garment_img,
+        "humanImage": encoded_person_img,
+        "seed": seed
+    }
+    try:
+        response = requests.post(url, headers=headers, data=json.dumps(data), timeout=50)
+        print("post response code", response.status_code)
+        if response.status_code == 200:
+            result = response.json()['result']
+            status = result['status']
+            if status == "success":
+                uuid = result['result']
+                print(uuid)
+    except Exception as err:
+        print(f"Error: {err}")
+        raise gr.Error("Too many users, please try again later")
+    post_end_time = time.time()
+    print(f"post time used: {post_end_time-post_start_time}")
+
+    get_start_time =time.time()
+    time.sleep(9)
+    Max_Retry = 10
+    result_img = None
+    for i in range(Max_Retry):
+        try:
+            url = "http://" + os.environ['tryon_url'] + "Query?taskId=" + uuid
+            response = requests.get(url, headers=headers, timeout=15)
+            print("get response code", response.status_code)
+            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"
+                    break
+                elif status == "error":
+                    raise gr.Error("Too many users, please try again later")
+            else:
+                print(response.text)
+                info = "URL error, pleace contact the admin"
+        except requests.exceptions.ReadTimeout:
+            print("timeout")
+            info = "Too many users, please try again later"
+        time.sleep(1)
+    get_end_time = time.time()
+    print(f"get time used: {get_end_time-get_start_time}")
+
+    return result_img, seed, info
+
+def start_tryon(person_img, garment_img, seed, randomize_seed):
+    start_time = time.time()
+    if person_img is None or garment_img is None:
+        return None, None, "Empty image"
+    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 = "http://" + os.environ['tryon_url']
+    token = os.environ['token']
+    cookie = os.environ['Cookie']
+    referer = os.environ['referer']
+
+    headers = {'Content-Type': 'application/json', 'token': token, 'Cookie': cookie, 'referer': referer}
+    data = {
+        "clothImage": encoded_garment_img,
+        "humanImage": encoded_person_img,
+        "seed": seed
+    }
+
+    result_img = None
+    try:
+        session = requests.Session()
+        response = session.post(url, headers=headers, data=json.dumps(data), timeout=60)
+        print("response code", response.status_code)
+        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:
+            print(response.text)
+            info = "URL error, pleace contact the admin"
+    except requests.exceptions.ReadTimeout:
+        print("timeout")
+        info = "Too many users, please try again later"
+        raise gr.Error("Too many users, please try again later")
+    except Exception as err:
+        print(f"其他错误: {err}")
+        info = "Error, pleace contact the admin"
+    end_time = time.time()
+    print(f"time used: {end_time-start_time}")
+
+    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: 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
+
+def change_imgs(image1, image2):
+    return image1, image2
+
+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. Upload a garment image ⬇️
+                </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")
+            # category = gr.Dropdown(label="Garment category", choices=['upper_body', 'lower_body', 'dresses'],  value="upper_body")
+            example = gr.Examples(
+                inputs=imgs,
+                examples_per_page=12,
+                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=12,
+                examples=garm_list_path
+            )
+        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")
+            # try_button = gr.Button(value="Run", elem_id="button")
+            test_button = gr.Button(value="Run", elem_id="button")
+
+
+    # try_button.click(fn=start_tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon',concurrency_limit=10)
+    test_button.click(fn=tryon, inputs=[imgs, garm_img, seed, randomize_seed], outputs=[image_out, seed_used, result_info], api_name='tryon',concurrency_limit=10)
+
+   
+
+# ip = requests.get('http://ifconfig.me/ip', timeout=1).text.strip()
+# print("ip address", ip)
+Tryon.queue(max_size = 20).launch(max_threads = 5)

requirements.txt

@@ -0,0 +1,8 @@
+accelerate
+diffusers
+invisible_watermark
+torch
+transformers
+xformers
+opencv-python
+requests