Upload 4 files

app.py

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+import streamlit as st
+import matplotlib as plt
+import cv2
+import numpy as np
+from PIL import Image #Image Processing
+harcascade = "haarcascade_russian_plate_number.xml"
+min_area = 500
+count = 0
+import easyocr
+
+
+st.image("anpr.jpg",use_column_width="always")
+st.markdown("<h1 style='text-align: center; color: green;'>SnapText 😎</h1>", unsafe_allow_html=True)
+
+# st.title(':blue[SnapText]')
+st.subheader('Welcome to :blue[SnapText ] !! ')
+st.subheader("Upload license plate photos of vehicles, and let us extract and rewrite the text for you. ")
+st.divider()
+uploaded_file = st.file_uploader("Upload your file here...", type=['jpg'])
+# path="plate_0.jpg"
+# img = cv.imread(uploaded_file)
+if uploaded_file is not None:
+    with st.spinner("Extracting Text and License PLate"):
+        file_bytes = np.asarray(bytearray(uploaded_file.read()),dtype=np.uint8)
+        img=cv2.imdecode(file_bytes,1)
+        st.image(img,caption="Uploaded Raw Image")
+        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        plate_cascade = cv2.CascadeClassifier(harcascade)
+        plates = plate_cascade.detectMultiScale(img_gray, 1.1, 4)
+        for (x, y, w, h) in plates:
+            area = w * h
+
+            if area > min_area:
+                cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
+                cv2.putText(img, "Number Plate", (x, y - 5), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (255, 0, 255), 2)
+
+                img_roi = img[y: y + h, x: x + w]
+
+                # Save the cropped and annotated image
+                cv2.imwrite("plate_0.jpg", img_roi)
+                
+                count += 1
+                image = Image.open('plate_0.jpg')
+                st.image(image, caption='Extracted License Plate')
+                reader = easyocr.Reader(['en'])
+                img = cv2.imread('plate_0.jpg')
+                result = reader.readtext(img)
+                st.success("Extracted Text: "+ result[0][-2].upper())
+
+
+
+
+
+
+
+
+
+
+
+
+

haarcascade_russian_plate_number.xml

@@ -0,0 +1,2656 @@
+<?xml version="1.0"?>
+<opencv_storage>
+<cascade>
+  <stageType>BOOST</stageType>
+  <featureType>HAAR</featureType>
+  <height>20</height>
+  <width>60</width>
+  <stageParams>
+    <boostType>GAB</boostType>
+    <minHitRate>9.9500000476837158e-001</minHitRate>
+    <maxFalseAlarm>5.0000000000000000e-001</maxFalseAlarm>
+    <weightTrimRate>9.4999999999999996e-001</weightTrimRate>
+    <maxDepth>1</maxDepth>
+    <maxWeakCount>100</maxWeakCount></stageParams>
+  <featureParams>
+    <maxCatCount>0</maxCatCount>
+    <featSize>1</featSize>
+    <mode>ALL</mode></featureParams>
+  <stageNum>20</stageNum>
+  <stages>
+    <!-- stage 0 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.3110191822052002e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            8.2346975803375244e-001 -3.7503159046173096e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 1 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.1759783029556274e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+        <_>
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+          <leafValues>
+            -7.8442335128784180e-001 3.4929576516151428e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 2 -->
+    <_>
+      <maxWeakCount>6</maxWeakCount>
+      <stageThreshold>-1.7856997251510620e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+          <leafValues>
+            7.1812576055526733e-001 -3.0354043841362000e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -2.6844096183776855e-001 7.6771658658981323e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 3 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-1.1837021112442017e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
+            0 -1 202 -1.3291766867041588e-002</internalNodes>
+          <leafValues>
+            4.5248869061470032e-001 -5.8849954605102539e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
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+        <_>
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+          <leafValues>
+            5.8126109838485718e-001 -3.0813106894493103e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            2.6361095905303955e-001 -7.2226840257644653e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -8.7153857946395874e-001 1.9438524544239044e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -3.6150649189949036e-001 5.2065432071685791e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            5.9945529699325562e-001 -2.8344830870628357e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -3.0688971281051636e-001 5.7261526584625244e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 4 -->
+    <_>
+      <maxWeakCount>8</maxWeakCount>
+      <stageThreshold>-1.4687808752059937e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
+          <internalNodes>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            -2.1032968163490295e-001 7.8646916151046753e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -4.3683132529258728e-001 4.0298295021057129e-001</leafValues></_>
+        <_>
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+          <leafValues>
+            -2.0461565256118774e-001 8.2858163118362427e-001</leafValues></_>
+        <_>
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+          <leafValues>
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+        <_>
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+          <leafValues>
+            2.3294737935066223e-001 -6.5612471103668213e-001</leafValues></_></weakClassifiers></_>
+    <!-- stage 5 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-1.1029583215713501e+000</stageThreshold>
+      <weakClassifiers>
+        <_>
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+        <_>
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+        <_>
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+        <_>
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+    <!-- stage 6 -->
+    <_>
+      <maxWeakCount>9</maxWeakCount>
+      <stageThreshold>-9.0274518728256226e-001</stageThreshold>
+      <weakClassifiers>
+        <_>
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+    <!-- stage 7 -->
+    <_>
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+      <stageThreshold>-1.4518526792526245e+000</stageThreshold>
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+    <!-- stage 8 -->
+    <_>
+      <maxWeakCount>11</maxWeakCount>
+      <stageThreshold>-1.3153649568557739e+000</stageThreshold>
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+    <!-- stage 9 -->
+    <_>
+      <maxWeakCount>13</maxWeakCount>
+      <stageThreshold>-1.4625015258789063e+000</stageThreshold>
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+        <_>
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+    <!-- stage 10 -->
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+    <!-- stage 11 -->
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+    <!-- stage 12 -->
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+    <!-- stage 13 -->
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+    <!-- stage 14 -->
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+    <!-- stage 15 -->
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+    <!-- stage 16 -->
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requirements.txt

@@ -0,0 +1,6 @@
+streamlit
+matplotlib
+numpy
+Pillow
+opencv-python
+easyocr