From 206c2a6877fd795212e03f809e9a78f94ee8dc14 Mon Sep 17 00:00:00 2001
From: "lamonov.pavel" <pavel.lamonov@sbps.ru>
Date: Mon, 21 Jul 2025 19:03:10 +0300
Subject: [PATCH] =?UTF-8?q?=D0=94=D0=BE=D0=B1=D0=B0=D0=B2=D0=B8=D0=BB=20?=
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---
 scripts/rec_validation.py   | 27 +++++++++++++++++++++++++++
 scripts/score_validation.py | 27 +++++++++++++++++++++++++++
 2 files changed, 54 insertions(+)
 create mode 100644 scripts/rec_validation.py
 create mode 100644 scripts/score_validation.py

diff --git a/scripts/rec_validation.py b/scripts/rec_validation.py
new file mode 100644
index 0000000..ea1b4fb
--- /dev/null
+++ b/scripts/rec_validation.py
@@ -0,0 +1,27 @@
+import os
+from paddleocr import TextRecognition
+
+ocr = TextRecognition(
+    model_name="PP-OCRv5_server_rec"
+    # model_dir="output/PP-OCRv5_server_rec_vin"
+)
+
+with open("train_data/val.txt", "r") as label_file:
+    lines = label_file.readlines()
+    total = len(lines)
+    matches, mismatches = 0, 0
+    for idx, line in enumerate(lines, start=1):
+        file_name, label = line.split("\t")
+        label = label.strip()
+        path = os.path.join("train_data", os.path.join("images", file_name))
+        result = ocr.predict(path)
+        print(f"{idx}/{total} ", end="")
+        if result[0]["rec_text"] and result[0]["rec_text"] == label:
+            print(f"match {label}")
+            matches += 1
+        else:
+            print(f"mismatch {label}")
+            mismatches += 1
+
+print(f"{matches} matches of {total}")
+print(f"{mismatches} mismatches of {total}")
diff --git a/scripts/score_validation.py b/scripts/score_validation.py
new file mode 100644
index 0000000..6c17724
--- /dev/null
+++ b/scripts/score_validation.py
@@ -0,0 +1,27 @@
+import os
+from paddleocr import PaddleOCR
+from typing import Optional
+
+ocr = PaddleOCR(rec_model_dir="output/vin_rec_inference")
+
+def get_best_accuracy(ocr: PaddleOCR, file_path: str) -> Optional[tuple[str, float]]:
+    result = ocr.predict(file_path)
+    texts_with_scores = [item for item in filter(
+        lambda x: len(x[0]) == 17,
+        zip(result[0]["rec_texts"], result[0]["rec_scores"])
+    )]
+    if texts_with_scores:
+        return max(
+            texts_with_scores,
+            key=lambda y: y[1]
+        )
+    else:
+        return None
+        
+
+with open("train_data/val.txt", "r") as label_file:
+    for line in label_file.readlines():
+        file_name, label = line.split("\t")
+        path = os.path.join("train_data", os.path.join("images", file_name))
+        print(file_name)
+        print(get_best_accuracy(ocr, path))