Added the main logic for image processing and uploading and downloading images from an S3 Bucket

1 files changed, 77 insertions, 0 deletions

diff --git a/backend/app.py b/backend/app.py
new file mode 100644
index 0000000..4ed4954
--- /dev/null
+++ b/backend/app.py
@@ -0,0 +1,77 @@
+import os
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+import boto3
+import cv2
+import tempfile
+import numpy as np
+
+app = Flask(__name__)
+CORS(app)
+
+# Configure AWS S3
+s3 = boto3.client('s3')
+BUCKET_NAME_ORIGINAL = "original-images-rapid-macaw"
+BUCKET_NAME_PROCESSED = "processed-images-rapid-macaw"
+
+@app.route('/upload', methods=['POST'])
+def upload_file():
+    if 'image' not in request.files:
+        return jsonify({'error': 'No file provided'}), 400
+    
+    file = request.files['image']
+    operation = request.form.get('operation', 'edge_detection')  # Default to edge detection
+    if file and allowed_file(file.filename):
+        # Save the file temporarily
+        temp_file = tempfile.NamedTemporaryFile(delete=False)
+        file.save(temp_file.name)
+
+        # Upload to S3 original bucket
+        with open(temp_file.name, "rb") as img_data:
+            s3.put_object(Bucket=BUCKET_NAME_ORIGINAL, Key=file.filename, Body=img_data, ContentType="image/png")
+
+        # Fetch the image from the original bucket
+        original_img_obj = s3.get_object(Bucket=BUCKET_NAME_ORIGINAL, Key=file.filename)
+        original_img_data = original_img_obj['Body'].read()
+
+        # Process the image
+        processed_image_path = process_image(original_img_data, operation)
+
+        # Upload processed image to S3 processed bucket
+        processed_filename = f"processed_{file.filename}"
+        with open(processed_image_path, "rb") as processed_img_data:
+            s3.put_object(Bucket=BUCKET_NAME_PROCESSED, Key=processed_filename, Body=processed_img_data, ContentType="image/png")
+
+        # Clean up temporary files
+        os.remove(temp_file.name)
+        os.remove(processed_image_path)
+
+        processed_file_url = f'https://{BUCKET_NAME_PROCESSED}.s3.amazonaws.com/{processed_filename}'
+
+        return jsonify({'message': 'File processed and uploaded successfully', 'processed_file': processed_file_url}), 200
+    else:
+        return jsonify({'error': 'Invalid file type'}), 400
+
+def allowed_file(filename):
+    return '.' in filename and filename.rsplit('.', 1)[1].lower() in {'png', 'jpg', 'jpeg'}
+
+def process_image(image_data, operation):
+    # Convert image data to numpy array
+    nparr = np.frombuffer(image_data, np.uint8)
+    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
+
+    processed_img = img
+
+    if operation == 'edge_detection':
+        processed_img = cv2.Canny(img, 100, 200)
+    elif operation == 'color_inversion':
+        processed_img = cv2.bitwise_not(img)
+
+    # Save processed image to a temporary path
+    output_path = os.path.join(tempfile.gettempdir(), f'processed_image.png')
+    cv2.imwrite(output_path, processed_img)
+
+    return output_path
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000, debug=True)