From 9ea36dae0b0e5294257a3ca1928338a4a8c938a7 Mon Sep 17 00:00:00 2001
From: Artem Dorofeev <130536391+artem-dorofeev@users.noreply.github.com>
Date: Sat, 13 Apr 2024 13:07:29 +0300
Subject: [PATCH 1/2] edit pyproject.toml

---
 FRONTEND/pyproject.toml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/FRONTEND/pyproject.toml b/FRONTEND/pyproject.toml
index 6758110e..9de63f04 100644
--- a/FRONTEND/pyproject.toml
+++ b/FRONTEND/pyproject.toml
@@ -15,6 +15,8 @@ pytz = "^2024.1"
 qrcode = "^7.4.2"
 pillow = "^10.3.0"
 psycopg2-binary = "^2.9.9"
+matplotlib = "^3.8.4"
+opencv-python = "^4.9.0.80"
 
 
 [build-system]

From fd9e6d97abc5fd4da40c8b03de739c17148424a7 Mon Sep 17 00:00:00 2001
From: Artem Dorofeev <130536391+artem-dorofeev@users.noreply.github.com>
Date: Sat, 13 Apr 2024 14:24:10 +0300
Subject: [PATCH 2/2] output result get_num_avto

---
 FRONTEND/fastparking/ds/predict_num.py | 26 ++++++++++++++++++++------
 1 file changed, 20 insertions(+), 6 deletions(-)

diff --git a/FRONTEND/fastparking/ds/predict_num.py b/FRONTEND/fastparking/ds/predict_num.py
index 882862db..f688ff57 100644
--- a/FRONTEND/fastparking/ds/predict_num.py
+++ b/FRONTEND/fastparking/ds/predict_num.py
@@ -242,6 +242,8 @@ def predict_result(ch_contours, model):
     for i, c in enumerate(characters):
         dic[i] = c
 
+    total_accuracy = 1
+
     output = []
     for i, ch in enumerate(ch_contours):
 
@@ -250,14 +252,24 @@ def predict_result(ch_contours, model):
         img = fix_dimension(img_)
         img = img.reshape(1, 28, 28, 3)  # preparing image for the model
 
-        y_ = np.argmax(model.predict(img, verbose=0), axis=-1)[
+        prediction = model.predict(img, verbose=0)
+
+       
+        y_ = np.argmax(prediction, axis=-1)[
             0
         ]  # predicting the class
-        character = dic[y_]
+
+        # print(y_, prediction.shape, prediction)
+        character = dic[y_]        
+        # accuracy = prediction[0][y_]
+        # print(f'{accuracy=}')
+        # total_accuracy *= accuracy
+        # print(f'{total_accuracy=}')
+
         output.append(character)
 
     plate_number = "".join(output)
-    return plate_number
+    return plate_number, total_accuracy
 
 
 def get_num_avto(img_avto):
@@ -266,10 +278,12 @@ def get_num_avto(img_avto):
 
     chars = segment_to_contours(num_img)
 
-    predicted_str = predict_result(chars, model)
+    predicted_str, total_accuracy = predict_result(chars, model)
     num_avto_str = str.replace(predicted_str, "#", "")
 
-    return num_avto_str, num_img
+    return {'num_avto_str': num_avto_str,
+            'accuracy': total_accuracy, 
+            'num_img': num_img}
 
 
 ################################################################################
@@ -291,4 +305,4 @@ def get_num_avto(img_avto):
         exit(1)
 
     result = get_num_avto(original)
-    print(f"ok - {result}")
+    print(result)