working changes done

app.py

@@ -19,7 +19,7 @@ from extractor import get_card_xy, get_digit
 # MODEL_PATH = "runs/train/exp/weights/best.pt"
 
 def main():
-    st.title("Card number extractor")
+    st.title("Card number detector")
 
     # Use st.camera to capture images from the user's camera
     img_file_buffer = st.camera_input(label='Please, take a photo of a card', key='card')
@@ -54,19 +54,18 @@ def main():
             st.image('card_image.jpg', caption=f"{display_text}", use_column_width=True)
         else:
             # cropped_image = gray[y1:y2, x1:x2]
-            cropped_image = resized_image[y1:y2, x1:x2]
-            cropped_image = cv2.resize(cropped_image, (128, 128))
-            cv2.imwrite('card_number_image.jpg', cropped_image)
+            # # cropped_image = resized_image[y1:y2, x1:x2]
+            # cropped_image = cv2.resize(cropped_image, (128, 128))
+            # cv2.imwrite('card_number_image.jpg', cropped_image)
             
-            extracted_digit = get_digit(
-                model_path="card_number_extractor.tflite", 
-                image_path='card_number_image.jpg', 
-                threshold=0.4
-            )
+            # extracted_digit = get_digit(
+            #     model_path="card_number_extractor.tflite", 
+            #     image_path='card_number_image.jpg', 
+            #     threshold=0.4
+            # )
 
-            display_text = f'Here is the zoomed card number: {extracted_digit}'
-            # display_text = 'No number so far'
-            st.image('card_number_image.jpg', caption=f"{display_text}", use_column_width=True)
+            # display_text = f'Here is the zoomed card number: {extracted_digit}'
+            # st.image('card_number_image.jpg', caption=f"{display_text}", use_column_width=True)
 
             image = Image.open('card_image.jpg')
             image_resized = image.resize((640, 640))

extractor.py

@@ -2,7 +2,7 @@ import tensorflow as tf
 import numpy as np
 from PIL import Image
 import cv2
-import streamlit as st
+# import streamlit as st
 
 def get_card_xy(model_path, image_path):
     #model_path = 'odo_detector.tflite'
@@ -12,8 +12,6 @@ def get_card_xy(model_path, image_path):
     input_details = interpreter.get_input_details()
     output_details = interpreter.get_output_details()
 
-    st.write(f"{input_details}")
-
     # Obtain the height and width of the corresponding image from the input tensor
     image_height = input_details[0]['shape'][2] # 640
     image_width = input_details[0]['shape'][3] # 640
@@ -28,8 +26,6 @@ def get_card_xy(model_path, image_path):
     image_np = np.moveaxis(image_np, -1, 0)
     image_np = image_np[np.newaxis, :]
 
-    st.write(f"{image_np.shape}")
-
     # inference
     interpreter.set_tensor(input_details[0]['index'], image_np)
     interpreter.invoke()
@@ -60,15 +56,10 @@ def get_card_xy(model_path, image_path):
         else:
             pass
             
-    # x1 = int((x_center - width / 2) * image_width)
-    # y1 = int((y_center - height / 2) * image_height)
-    # x2 = int((x_center + width / 2) * image_width)
-    # y2 = int((y_center + height / 2) * image_height)
-
-    x1 = int((x_center - width / 2) * 640)
-    y1 = int((y_center - height / 2) * 640)
-    x2 = int((x_center + width / 2) * 640)
-    y2 = int((y_center + height / 2) * 640)
+    x1 = int((x_center - width / 2) * image_width)
+    y1 = int((y_center - height / 2) * image_height)
+    x2 = int((x_center + width / 2) * image_width)
+    y2 = int((y_center + height / 2) * image_height)
 
     # draw.rectangle([x1, y1, x2, y2], outline="red", width=2)
     # text = f"Class: {class_name}, Score: {final_score:.2f}"