[Feat] frontend/backend 초안

.gitignore

@@ -0,0 +1 @@
+backend/app/__pycache__/

backend/app/confirm_button_hack.py

@@ -51,9 +51,9 @@ def evaluate(self):
                 if st.button(label):
                     cache_entry.evaluate()
                 else:
-                    raise st.script_runner.StopException
+                    st.stop()
             return cache_entry.return_value
 
         return wrapped_func
 
-    return function_decorator
+    return function_decorator

backend/app/frontend.py

@@ -17,37 +17,84 @@
 
 
 def main():
-    st.title("Mask Classification Model")
-    uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"])
+    st.title("Welcome to VTON World :)")
+
+    with st.container():
+        col1, col2, col3 = st.columns([1,1,1])
+
+        with col1:
+            st.header("Human")
+            uploaded_target = st.file_uploader("Choose an target image", type=["jpg", "jpeg", "png"])
+
+            if uploaded_target:
+                target_bytes = uploaded_target.getvalue()
+                target_img = Image.open(io.BytesIO(target_bytes))
 
-    if uploaded_file:
-        image_bytes = uploaded_file.getvalue()
-        image = Image.open(io.BytesIO(image_bytes))
+                st.image(target_img, caption='Uploaded target Image')
+
+        with col2:
+            st.header("Cloth")
+            uploaded_garment = st.file_uploader("Choose an garment image", type=["jpg", "jpeg", "png"])
 
-        st.image(image, caption='Uploaded Image')
-        st.write("Classifying...")
+            if uploaded_garment:
+                # st.spinner("dehazing now...")
+
+                garment_bytes = uploaded_garment.getvalue()
+                garment_img = Image.open(io.BytesIO(garment_bytes))
 
-        # 기존 stremalit 코드
-        # _, y_hat = get_prediction(model, image_bytes)
-        # label = config['classes'][y_hat.item()]
-        files = [
-            ('files', (uploaded_file.name, image_bytes,
-                       uploaded_file.type))
-        ]
-        response = requests.post("http://localhost:8001/order", files=files)
-        label = response.json()["products"][0]["result"]
-        st.write(f'label is {label}')
+                st.image(garment_img, caption='Uploaded garment Image')
+
+        with col3:  
+            st.header("Result")
+        if uploaded_target and uploaded_garment:
+            files = [
+                ('files', (uploaded_target.name, target_bytes,
+                        uploaded_target.type))
+                ,
+                ('files', (uploaded_garment.name, garment_bytes,
+                        uploaded_garment.type))
+            ]
+
+            with col3:  
+                st.write(' ')
+                empty_slot = st.empty()
+                empty_slot.markdown("<h2 style='text-align: center;'>\nLoading...</h2>", unsafe_allow_html=True)
 
+                response = requests.post("http://localhost:8001/order", files=files)
+                empty_slot.empty()
+                empty_slot.markdown("<h2 style='text-align: center;'>Here it is !</h2>", unsafe_allow_html=True)
 
-@cache_on_button_press('Authenticate')
-def authenticate(password) -> bool:
-    return password == root_password
+                category = 'lower_body'
+                output_ladi_buffer_dir = '/opt/ml/user_db/ladi/buffer'
+                final_result_dir = output_ladi_buffer_dir
+                final_img = Image.open(os.path.join(final_result_dir, f'{category}.png'))
+
+                st.write(' ')
+                st.write(' ')
+                st.write(' ')
+                st.write(' ')
+                st.write(' ')
+                st.write(' ')
+                st.image(final_img, caption='Final Image', use_column_width=True)
+
+                # option = '선택 안 함'
+                # down_btn = st.download_button(
+                #     label='Download Image',
+                #     data=dehaze_image_bytes,
+                #     file_name='dehazed_image.jpg',
+                #     mime='image/jpg',
+                #     on_click=save_btn_click(option, dehaze_image_bytes)
+                # )
 
+# @cache_on_button_press('Authenticate')
+# def authenticate(password) -> bool:
+#     return password == root_password
+# password = st.text_input('password', type="password")
+# if authenticate(password):
+#     st.success('You are authenticated!')
+#     main()
+# else:
+#     st.error('The password is invalid.')
 
-password = st.text_input('password', type="password")
 
-if authenticate(password):
-    st.success('You are authenticated!')
-    main()
-else:
-    st.error('The password is invalid.')
+main()

backend/app/main.py

@@ -7,7 +7,30 @@
 from datetime import datetime
 
 from app.model import MyEfficientNet, get_model, get_config, predict_from_image_byte
+from PIL import Image
+import io
 
+# scp setting
+import sys, os
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/Self_Correction_Human_Parsing/')
+from simple_extractor import main_schp
+
+# openpose
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/pytorch_openpose/')
+from extract_keypoint import main_openpose
+
+# ladi
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton')
+sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model/ladi_vton/src')
+from inference import main_ladi
+
+
+
+# sys.path.append('/opt/ml/level3_cv_finalproject-cv-12/model')
+# print('sys.path:', sys.path)
+# from Self_Correction_Human_Parsing.simple_extractor import main_schp
+# from pytorch_openpose.extract_keypoint import main_openpose
+# from ladi_vton.src.inference import main_ladi
 app = FastAPI()
 
 orders = []
@@ -71,17 +94,59 @@ def get_order_by_id(order_id: UUID) -> Optional[Order]:
     return next((order for order in orders if order.id == order_id), None)
 
 
+# post!!
 @app.post("/order", description="주문을 요청합니다")
 async def make_order(files: List[UploadFile] = File(...),
 #  def make_order(files: List[UploadFile] = File(...),
                      model: MyEfficientNet = Depends(get_model),
                      config: Dict[str, Any] = Depends(get_config)):
     products = []
-    for file in files:
-        image_bytes = await file.read()
-        inference_result = predict_from_image_byte(model=model, image_bytes=image_bytes, config=config)
-        product = InferenceImageProduct(result=inference_result)
-        products.append(product)
+
+    # target:files[0], garment:files[1]
+
+    target_bytes = await files[0].read()
+    garment_bytes = await files[1].read()
+
+    # TODO image byte 
+    target_image = Image.open(io.BytesIO(target_bytes))
+    target_image = target_image.convert("RGB")
+
+    garment_image = Image.open(io.BytesIO(garment_bytes))
+    garment_image = garment_image.convert("RGB")
+
+    input_dir = '/opt/ml/user_db/input/'
+
+    os.makedirs(f'{input_dir}/buffer', exist_ok=True)
+
+    target_image.save(f'{input_dir}/target.jpg')
+    target_image.save(f'{input_dir}/buffer/target/target.jpg')
+
+    garment_image.save(f'{input_dir}/garment.jpg')
+    garment_image.save(f'{input_dir}/buffer/garment/garment.jpg')
+
+    # schp  - (1024, 784), (512, 384)
+    target_buffer_dir = f'{input_dir}/buffer/target'
+    main_schp(target_buffer_dir)
+
+
+    # openpose 
+    output_openpose_buffer_dir = '/opt/ml/user_db/openpose/buffer'
+    os.makedirs(output_openpose_buffer_dir, exist_ok=True)
+    main_openpose(target_buffer_dir, output_openpose_buffer_dir)
+
+    # ladi-vton 
+    output_ladi_buffer_dir = '/opt/ml/user_db/ladi/buffer'
+    db_dir = '/opt/ml/user_db'
+    os.makedirs(output_ladi_buffer_dir, exist_ok=True)
+    main_ladi(db_dir, output_ladi_buffer_dir)
+
+    return None
+    ## return값 
+    ## output dir
+
+    inference_result = predict_from_image_byte(model=model, image_bytes=image_bytes, config=config)
+    product = InferenceImageProduct(result=inference_result)
+    products.append(product)
 
     new_order = Order(products=products)
     orders.append(new_order)