Update streamlit_app.py

streamlit_app.py

@@ -1,53 +1,53 @@
-import streamlit as st
-import cv2
-import numpy as np
-import tempfile
-from PIL import Image
-from ultralytics import YOLO
-
-def process_lines(image_path):
-    thickness = 3
-
-    image = cv2.imread(image_path)
-    result = image.copy()
-
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-
-    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
-    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80, minLineLength=40, maxLineGap=40)
-    line_mask = np.zeros_like(gray)
-
-    if lines is not None:
-        for line in lines:
-            x1, y1, x2, y2 = line[0]
-            cv2.line(line_mask, (x1, y1), (x2, y2), (255, 255, 255), thickness=3)
-
-    return line_mask
-
-def detect_text(image_path):
-    model = YOLO(r"C:\Users\Tectoro\OneDrive - Tectoro\Desktop\tippan\best 3.pt")
-    results = model.predict(image_path)
-    annotated_image = results[0].plot()
-    return annotated_image
-
-st.title("Line and Text Extraction")
-st.sidebar.header("Upload an Image")
-
-uploaded_file = st.sidebar.file_uploader("Choose an image file", type=["png", "jpg", "jpeg", "tif"])
-
-if st.sidebar.button("Process Image"):
-    if uploaded_file is not None:
-        with tempfile.NamedTemporaryFile(delete=False, suffix=uploaded_file.name) as temp_file:
-            temp_file.write(uploaded_file.read())
-            temp_file_path = temp_file.name
-
-        line_mask = process_lines(temp_file_path)
-        text_extracted=detect_text(temp_file_path)
-
-        st.subheader("Line Mask")
-        st.image(line_mask, channels="GRAY")
-        
-        st.subheader("Text Detection")
-        st.image(cv2.cvtColor(text_extracted, cv2.COLOR_BGR2RGB))
-    else:
-        st.sidebar.error("Please upload an image file before clicking 'Process Image'.")
+import streamlit as st
+import cv2
+import numpy as np
+import tempfile
+from PIL import Image
+from ultralytics import YOLO
+
+def process_lines(image_path):
+    thickness = 3
+
+    image = cv2.imread(image_path)
+    result = image.copy()
+
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
+    lines = cv2.HoughLinesP(edges, 1, np.pi / 180, threshold=80, minLineLength=40, maxLineGap=40)
+    line_mask = np.zeros_like(gray)
+
+    if lines is not None:
+        for line in lines:
+            x1, y1, x2, y2 = line[0]
+            cv2.line(line_mask, (x1, y1), (x2, y2), (255, 255, 255), thickness=3)
+
+    return line_mask
+
+def detect_text(image_path):
+    model = YOLO("best 3.pt")
+    results = model.predict(image_path)
+    annotated_image = results[0].plot()
+    return annotated_image
+
+st.title("Line and Text Extraction")
+st.sidebar.header("Upload an Image")
+
+uploaded_file = st.sidebar.file_uploader("Choose an image file", type=["png", "jpg", "jpeg", "tif"])
+
+if st.sidebar.button("Process Image"):
+    if uploaded_file is not None:
+        with tempfile.NamedTemporaryFile(delete=False, suffix=uploaded_file.name) as temp_file:
+            temp_file.write(uploaded_file.read())
+            temp_file_path = temp_file.name
+
+        line_mask = process_lines(temp_file_path)
+        text_extracted=detect_text(temp_file_path)
+
+        st.subheader("Line Mask")
+        st.image(line_mask, channels="GRAY")
+        
+        st.subheader("Text Detection")
+        st.image(cv2.cvtColor(text_extracted, cv2.COLOR_BGR2RGB))
+    else:
+        st.sidebar.error("Please upload an image file before clicking 'Process Image'.")