Initial Commit

DenseNet20_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7a7deb953774a8f8ef09fe617c93889be9dcc4003f1a9775f0a9a71376e0294
+size 87387592

app.py

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+import streamlit as st
+import os
+import torch
+from paddleocr import PaddleOCR
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array, load_img
+import numpy as np
+from datetime import datetime, timedelta
+import re
+from ultralytics import YOLO
+import pandas as pd
+
+# Initialize models
+ocr = PaddleOCR(lang='en')
+fruit_model = load_model('DenseNet20_model.h5')
+brand_model = YOLO('b.pt')
+
+# Class names for fruit freshness classification
+class_names = {
+    0: 'Banana_Bad',
+    1: 'Banana_Good',
+    2: 'Fresh',
+    3: 'FreshCarrot',
+    4: 'FreshCucumber',
+    5: 'FreshMango',
+    6: 'FreshTomato',
+    7: 'Guava_Bad',
+    8: 'Guava_Good',
+    9: 'Lime_Bad',
+    10: 'Lime_Good',
+    11: 'Rotten',
+    12: 'RottenCarrot',
+    13: 'RottenCucumber',
+    14: 'RottenMango',
+    15: 'RottenTomato',
+    16: 'freshBread',
+    17: 'rottenBread'
+}
+
+# Helper functions
+def preprocess_image(image_path):
+    img = load_img(image_path, target_size=(128, 128))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array = img_array / 255.0
+    return img_array
+
+def extract_expiry_dates(text):
+    expiry_date_patterns = [
+        r'USE BY (\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})',
+        r'BEST BEFORE (\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})',
+        r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{4})',
+        r'(\d{1,2}[\/\-]\d{1,2}[\/\-]\d{2})',
+        r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{4})',
+        r'(\d{1,2}\s*[A-Za-z]{3,}\s*\d{2})',
+        r'(\d{4}[\/\-]\d{1,2}[\/\-]\d{1,2})',
+        r'(\d{4}[A-Za-z]{3,}\d{1,2})',
+        r'(\d{1,2}[A-Za-z]{3,}\d{4})',
+        r'Best before (\d+) months'
+    ]
+    dates = []
+    for pattern in expiry_date_patterns:
+        matches = re.findall(pattern, text, re.IGNORECASE)
+        dates.extend(matches)
+    return dates
+
+# Streamlit app
+st.title("Image Processing Application")
+
+# User choice for processing
+task_choice = st.radio("Choose a task", ("Text and Brand Detection", "Freshness Detection"))
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Ensure the uploads directory exists
+    if not os.path.exists("uploads"):
+        os.makedirs("uploads")
+
+    # Save the uploaded file
+    image_path = os.path.join("uploads", uploaded_file.name)
+    with open(image_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    # Display the uploaded image
+    st.image(image_path, caption='Uploaded Image', use_container_width=True)
+
+    if task_choice == "Text and Brand Detection":
+        # Text Extraction
+        st.header("Text Extraction")
+        result = ocr.ocr(image_path)
+        text = ' '.join([line[1][0] for line in result[0]])
+        st.write("Extracted Text:")
+        st.text(text)
+
+        # Expiry Date Extraction
+        st.header("Expiry Date Extraction")
+        expiry_dates = extract_expiry_dates(text)
+        if expiry_dates:
+            st.write("Expiry Dates Found:")
+            for date in expiry_dates:
+                st.text(date)
+        else:
+            st.write("No expiry dates found.")
+
+        # Brand Prediction
+        st.header("Brand Prediction")
+        results = brand_model(image_path)
+        detected_brands = []
+        for result in results:
+            boxes = result.boxes.data.cpu().numpy()
+            for box in boxes:
+                class_id = int(box[5])
+                confidence = box[4]  # Assuming the confidence score is at index 4
+                detected_brands.append((result.names[class_id], confidence))
+        
+        # Count occurrences and average confidence of each brand
+        brand_data = {}
+        for brand, confidence in detected_brands:
+            if brand in brand_data:
+                brand_data[brand]['count'] += 1
+                brand_data[brand]['total_confidence'] += confidence
+            else:
+                brand_data[brand] = {'count': 1, 'total_confidence': confidence}
+        
+        # Prepare data for display
+        brand_display_data = [
+            {'Object': brand, 'Count': data['count'], 'Average Confidence': data['total_confidence'] / data['count']}
+            for brand, data in brand_data.items()
+        ]
+        
+        # Display detected brands in a table with column names
+        st.write("Detected Brands and Counts:")
+        st.table(pd.DataFrame(brand_display_data))
+
+    elif task_choice == "Freshness Detection":
+        # Freshness Prediction
+        st.header("Freshness Prediction")
+        img_array = preprocess_image(image_path)
+        predictions = fruit_model.predict(img_array)
+        predicted_class = np.argmax(predictions, axis=1)[0]
+        st.write("Predicted Freshness:", class_names[predicted_class])

b.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d4bfbf90d2489df68ac124e67bc911aab931bb8ff7b7f224c40a30faee2000c2
+size 22467811

requirements.txt

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+streamlit
+torch
+paddleocr
+tensorflow
+numpy
+Pillow
+opencv-python-headless
+paddlepaddle
+ultralytics

yolov5s.pt

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+version https://git-lfs.github.com/spec/v1
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+size 14808437