Add filename and title export to Excel; update hf_process to build DataFrame from title + sensors; improve file name handling

app.py

@@ -28,6 +28,9 @@ def draw_boxes(img_rgb, sensors):
 def hf_process(img):
 
     cfg = load_config("configs/config.yaml")
+    
+    # Имя файла (если доступно)
+    filename = getattr(img, "name", "uploaded_image.png")
 
     # Запуск пайплайна
     title, sensors = process_single_image(img, color_ranges=cfg["colors"])
@@ -36,8 +39,19 @@ def hf_process(img):
     boxed = draw_boxes(img, sensors)
 
     # --- DataFrame сенсоров ---
-    print("SENSORS:", sensors)
-    df = pd.DataFrame(sensors)[["text", "score", "x", "y", "w", "h"]]
+    df = pd.DataFrame([
+        {
+            "filename": filename,
+            "title": title,
+            "text": s["text"],
+            "score": s["score"],
+            "x": s["x"],
+            "y": s["y"],
+            "w": s["w"],
+            "h": s["h"]
+        }
+        for s in sensors
+    ])
 
     # --- Excel на скачивание ---
     tmp = NamedTemporaryFile(delete=False, suffix=".xlsx")
@@ -46,8 +60,8 @@ def hf_process(img):
     return (
         boxed,
         title,
-        df,
-        tmp.name  # путь к скачиваемому файлу
+        tmp.name,  # путь к скачиваемому файлу
+        df
     )
 
 

src/ocr_utils_demo.py

@@ -63,7 +63,6 @@ def ocr_sensors(rois: list[np.ndarray]):
     for roi in rois:
         try:
             ocr_res = paddle_ocr.ocr(roi, det=False, cls=False)
-            print(f"Результат оцифровки: {ocr_res}") # для отладки
         except Exception as e:
             print(f"⚠ Ошибка OCR.ocr: {e}")
             results.append({"text": "?", "score": 0.0})
@@ -89,18 +88,4 @@ def ocr_sensors(rois: list[np.ndarray]):
             "score": float(score) if score else 0.0
         })
 
-    return results
-
-"""
-
-    for out in ocr_results:
-        
-        texts = out.get("rec_texts", ["?"])
-        scores = out.get("rec_scores", [0.0])
-
-        # --- распаковка ---
-        text = texts[0] if texts else "?"
-        score = scores[0] if scores else 0.0
-
-        results.append({"text": text, "score": round(score, 2)})
-"""
+    return results

src/pipeline_hf.py

@@ -31,23 +31,19 @@ def process_single_image(img: np.ndarray, color_ranges: dict):
         mask = cur if mask is None else cv2.bitwise_or(mask, cur)
 
     contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-    print("Contours found:", len(contours)) # для отладки
 
     rois, positions = [], []
     for cnt in contours:
         x, y, ww, hh = cv2.boundingRect(cnt)
-        print("Contour bbox:", x, y, ww, hh) # для отладки
         if ww < 90 or hh < 17:
             continue
         if hh / ww > 1.5:
             continue
 
-        print("ROI accepted:", ww, hh) # для отладки
         hh_clamped = min(hh, 17)
         roi = img[y:y + hh_clamped, x:x + ww]
         rois.append(roi)
         positions.append((x, y, ww, hh))
-        print("ROIs after filtering:", len(rois)) # для отладки
 
     # ---------- 3. OCR сенсоров ----------
     sensors = []