update make_depth

app.py

@@ -1013,18 +1013,55 @@ def preprocess_mask(mask_img: Image.Image) -> Image.Image:
     return Image.fromarray(m, mode="L").convert("RGB")
 
 
+# def make_depth(depth_path: str) -> Image.Image:
+#     global H, W
+#     if H is None or W is None:
+#         raise RuntimeError("Global H/W not set. Call run_one() first.")
+
+#     depth_img = _imread_or_raise(depth_path, 0)
+#     contours, _ = cv2.findContours(depth_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+#     filled_depth = depth_img.copy()
+#     cv2.drawContours(filled_depth, contours, -1, (255), thickness=cv2.FILLED)
+
+#     filled_depth = cv2.resize(filled_depth, (W, H), interpolation=cv2.INTER_AREA)
+#     filled_depth = _pad_or_crop_to_width_np(filled_depth, 1024, pad_value=0)
+
+#     inverted_image = ImageOps.invert(Image.fromarray(filled_depth))
+
+#     with torch.inference_mode():
+#         image_depth = depth_estimator(inverted_image)["depth"]
+
+#     if DEBUG_SAVE:
+#         image_depth.save("depth.png")
+
+#     return image_depth
+
 def make_depth(depth_path: str) -> Image.Image:
     global H, W
     if H is None or W is None:
         raise RuntimeError("Global H/W not set. Call run_one() first.")
 
-    depth_img = _imread_or_raise(depth_path, 0)
-    contours, _ = cv2.findContours(depth_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    depth_img = _imread_or_raise(depth_path, 0)  # grayscale
 
-    filled_depth = depth_img.copy()
-    cv2.drawContours(filled_depth, contours, -1, (255), thickness=cv2.FILLED)
+    # (선택) 입력이 완전한 0/255가 아니라면 이진화로 고정
+    _, depth_bin = cv2.threshold(depth_img, 127, 255, cv2.THRESH_BINARY)
+
+    # 컨투어 채우기가 "두꺼워 보임"의 원인일 수도 있어, 유지/제거 선택 가능
+    # 1) 채우기 유지 (holes 메우는 목적이라면)
+    contours, _ = cv2.findContours(depth_bin, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    filled_depth = np.zeros_like(depth_bin)
+    cv2.drawContours(filled_depth, contours, -1, 255, thickness=cv2.FILLED)
+
+    # 2) 채우기 제거하고 싶으면 위 3줄 대신 이걸 사용:
+    # filled_depth = depth_bin
+
+    # ✅ 마스크 리사이즈는 NEAREST (경계 번짐/팽창 느낌 방지)
+    filled_depth = cv2.resize(filled_depth, (W, H), interpolation=cv2.INTER_NEAREST)
+
+    # (선택) 리사이즈 후에도 0/255 강제
+    _, filled_depth = cv2.threshold(filled_depth, 127, 255, cv2.THRESH_BINARY)
 
-    filled_depth = cv2.resize(filled_depth, (W, H), interpolation=cv2.INTER_AREA)
     filled_depth = _pad_or_crop_to_width_np(filled_depth, 1024, pad_value=0)
 
     inverted_image = ImageOps.invert(Image.fromarray(filled_depth))
@@ -1038,6 +1075,7 @@ def make_depth(depth_path: str) -> Image.Image:
     return image_depth
 
 
+
 def _edges_from_parsing(parsing_img: Image.Image) -> np.ndarray:
     m = np.array(parsing_img.convert("L"), dtype=np.uint8)
     _, m_bin = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
@@ -1362,7 +1400,7 @@ def infer_web(person_fp, sketch_fp, style_fp, prompt, steps, seed, category):
 
 
 with gr.Blocks(title="VISTA Demo (HF Spaces)") as demo:
-    gr.Markdown("## VISTA Demo\nperson 필수, style/sketch(guide)는 선택입니다.")
+    gr.Markdown("## 첫 inference는 모델 로딩 때문에 오래 걸릴 수 있습니다.")
 
     category_toggle = gr.Radio(
         choices=["Dress", "Upper-body", "Lower-body"],