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best.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b1c7e7aa260c6f1e34b1c8c0d4c3bdc35b6f40f75f59e3cf593ba58aaff690d3
+size 6253849

flaskapp.py

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+import base64
+from flask import Flask, request, jsonify, render_template
+import os
+from flask_cors import CORS, cross_origin
+# -
+# import streamlit as st
+from ultralytics import YOLO
+import numpy as np
+from PIL import Image
+import requests
+from io import BytesIO
+import cv2
+import io
+
+def calculate_score(results):
+        labels = {0: u'bathtub', 1: u'c', 2: u'geyser', 3: u'mirror', 4: u'showerhead', 5: u'sink', 6: u'toilet', 7: u'towel', 8: u'washbasin', 9: u'wc', 10: u'none'}
+        scores = {0: 70,  # Bathtub
+                       1: 50,  # 'c' idk wtf is this
+                       2: 60,  # Geyser is imp
+                       3: 80,  # Mirrors are op
+                       4: 60,  # Showerhead is ok, but not imp when shitting
+                       5: 90,  # Sink is a S+
+                       6: 100,  # Not imp
+                       7: 40,  # Towels
+                       8: 80,  # Washbasin
+                       9: 100,  # 'wc'
+                       10: 0}  # 'none'
+        score = 0
+        for key, value in labels.items():
+            if value in results:
+                score = score + scores[key]
+
+        score = (score*100.0)/730.0
+        return score
+
+app = Flask(__name__)
+CORS(app)
+        
+
+def decodeImage(imgstring, fileName):
+    imgdata = base64.b64decode(imgstring)
+    with open(fileName, 'wb') as f:
+        f.write(imgdata)
+        f.close()
+
+
+def encodeImageIntoBase64(croppedImagePath):
+    with open(croppedImagePath, "rb") as f:
+        return base64.b64encode(f.read())
+
+
+@app.route("/", methods = ['GET'])
+@cross_origin()
+def home():
+    html_content = "<h1>SERVER UP!</h1>"
+    return html_content, 200, {'Content-Type': 'text/html'}
+    
+
+@app.route('/api/process-images', methods=['POST'])
+def process_images():
+    try:
+        if 'images' not in request.files:
+            return jsonify({"error": "No images uploaded"}), 400
+        
+        images = request.files.getlist('images')
+
+        model = YOLO('best.pt')
+        
+        for image in images:
+            image_file = image
+            image_file.save('input.png')  # Example: Save image as 'uploaded_image.png'
+
+
+        processed_images = []
+        for image in images:
+            # Read image file
+            img = Image.open(image)
+            # Perform processing (example: resizing)
+            img_resized = img.resize((100, 100))  # Resize the image to 100x100 (example)
+            # Convert processed image to bytes
+            buffered = BytesIO()
+            img_resized.save(buffered, format="JPEG")
+            processed_images.append(buffered.getvalue())
+        
+        return jsonify({"processed_images": processed_images}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+
+
+@app.route('/api/process-image', methods=['POST'])
+def process_image():
+    try:
+        if 'image' not in request.files:
+            return jsonify({"error": "No image uploaded"}), 400
+        
+        image_file = request.files['image']
+        image_file.save('input.png')  # Example: Save image as 'uploaded_image.png'
+
+        model = YOLO("best.pt")
+        Img = Image.open(image_file)
+
+        results = model(Img)
+
+        class_id = results[0].boxes.cls.numpy()
+        labels = {0: u'bathtub', 1: u'c', 2: u'geyser', 3: u'mirror', 4: u'showerhead', 5: u'sink', 6: u'toilet', 7: u'towel', 8: u'washbasin', 9: u'wc', 10: u'none'}
+        
+        classes = set()
+        for i in class_id:
+            classes.add(labels[i])
+        
+        return jsonify({"Facilities_Detected": list(classes), "Toilet_Score":calculate_score(classes)}), 200
+    except Exception as e:
+        return jsonify({"error": str(e)}), 500
+    
+
+
+@app.route('/c', methods=['POST'])
+def processor_image():
+# try:
+    if 'image' not in request.files:
+        return jsonify({"error": "No image uploaded"}), 400
+    
+    image_file = request.files.getlist('image')
+    print(image_file)
+
+    total_list = set()
+    
+    for image in image_file:
+    
+        model = YOLO("best.pt")
+        image.save('input.png')  # Example: Save image as 'uploaded_image.png'
+    # image_file.save('input.png')  # Example: Save image as 'uploaded_image.png'
+
+        Img = Image.open(image)
+        results = model(Img)
+
+        class_id = results[0].boxes.cls.numpy()
+        labels = {0: u'bathtub', 1: u'c', 2: u'geyser', 3: u'mirror', 4: u'showerhead', 5: u'sink', 6: u'toilet', 7: u'towel', 8: u'washbasin', 9: u'wc', 10: u'none'}
+        
+        # classes = set()
+        for i in class_id:
+            total_list.add(labels[i])
+
+        # total_list.append(classes)
+    print(total_list)
+    return jsonify({"Facilities_Detected": list(total_list), "Toilet_Score":calculate_score(set(total_list))}), 200
+# except Exception as e:
+#     return jsonify({"error": str(e)}), 500
+
+
+
+app.run(host='0.0.0.0', port=1000)

requirements.txt

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+ultralytics
+# streamlit
+opencv-contrib-python-headless
+flask_cors
+flask