Update app.py

app.py

@@ -75,7 +75,8 @@ def custom_nms(preds, iou_threshold=0.3):
             filtered_preds.append(pred)
     return filtered_preds
 
-def process_image(job_id, image_path, object_type):
+# Note the added multiplier parameter.
+def process_image(job_id, image_path, object_type, multiplier):
     try:
         jobs[job_id]['progress'] = 10
         # Load image
@@ -87,8 +88,8 @@ def process_image(job_id, image_path, object_type):
 
         jobs[job_id]['progress'] = 20
         img_height, img_width = image.shape[:2]
-        # Compute dynamic thickness based on image size.
-        thickness = max(2, int(min(img_width, img_height) / 300))
+        # Compute dynamic thickness based on image size and multiplier.
+        thickness = max(2, int(min(img_width, img_height) / 300)) * multiplier
         detection_info = []
 
         if object_type == "box":
@@ -128,22 +129,18 @@ def process_image(job_id, image_path, object_type):
             jobs[job_id]['progress'] = 60
 
             # (Optional) Detect ArUco marker for measurement
-            # IMPORTANT: Set this to your marker's real-world side length in centimeters.
             marker_real_width_cm = 10.0  
             try:
                 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                 aruco_dict = cv2.aruco.getPredefinedDictionary(cv2.aruco.DICT_6X6_250)
-                # Use the newer DetectorParameters_create if available
                 if hasattr(cv2.aruco, 'DetectorParameters_create'):
                     aruco_params = cv2.aruco.DetectorParameters_create()
                 else:
                     aruco_params = cv2.aruco.DetectorParameters()
                 corners, ids, _ = cv2.aruco.detectMarkers(gray, aruco_dict, parameters=aruco_params)
                 if ids is not None and len(corners) > 0:
-                    # Use the first detected marker as reference
                     marker_corners = corners[0].reshape((4, 2))
                     cv2.aruco.drawDetectedMarkers(image, corners, ids)
-                    # Compute the distance between consecutive corners and take the average
                     side_lengths = []
                     for i in range(4):
                         pt1 = marker_corners[i]
@@ -160,7 +157,6 @@ def process_image(job_id, image_path, object_type):
                 x1, y1, x2, y2 = pred["box"]
                 label = pred["class"]
                 confidence = pred["confidence"]
-                # Use dynamic thickness here
                 cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), thickness)
                 if conversion_factor is not None:
                     box_width_pixels = x2 - x1
@@ -201,7 +197,6 @@ def process_image(job_id, image_path, object_type):
                         ymax = int(row['ymax'])
                         conf = row['confidence']
                         label = row['name']
-                        # Use dynamic thickness here as well
                         cv2.rectangle(image, (xmin, ymin), (xmax, ymax), (255, 0, 0), thickness)
                         text = f"{label} ({conf:.2f})"
                         (text_width, text_height), baseline = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
@@ -334,8 +329,9 @@ upload_template = '''
   <!-- The file upload form (submission handled via AJAX) -->
   <form id="uploadForm">
     <input type="file" name="file" accept="image/*" required>
-    <!-- Hidden field to pass the selected object type -->
+    <!-- Hidden fields to pass the selected object type and device multiplier -->
     <input type="hidden" name="object_type" value="{{ object_type }}">
+    <input type="hidden" name="multiplier" id="multiplier" value="1">
     <button type="submit">Analyze Image</button>
   </form>
   <div id="resultContainer">
@@ -392,6 +388,9 @@ upload_template = '''
     }
     document.addEventListener("DOMContentLoaded", () => {
       setTimeout(typeEffect, 500);
+      // Detect if the device is mobile and update the thickness multiplier accordingly.
+      var isMobile = /Mobi|Android/i.test(navigator.userAgent);
+      document.getElementById("multiplier").value = isMobile ? 2 : 1;
     });
     // AJAX-based submission and progress polling
     const uploadForm = document.getElementById("uploadForm");
@@ -415,7 +414,6 @@ upload_template = '''
           .then(progData => {
             const progress = progData.progress;
             document.getElementById("progressBar").style.width = progress + "%";
-            // Update dynamic phrases based on progress
             if (progress < 10) {
               document.getElementById("progressText").textContent = "Starting up... 🛠️";
             } else if (progress < 30) {
@@ -431,12 +429,10 @@ upload_template = '''
             }
             if (progress >= 100) {
               clearInterval(progressInterval);
-              // Once done, fetch the result and update the page
               fetch("{{ url_for('result') }}?job_id=" + jobId)
               .then(response => response.json())
               .then(resultData => {
                 document.getElementById("progressOverlay").style.display = "none";
-                // Replace the result container with the new result
                 document.getElementById("resultContainer").innerHTML = `
                   <div class="content-wrapper">
                     <img src="data:image/jpeg;base64,${resultData.image_data}" alt="Processed Image" class="result-img">
@@ -482,25 +478,20 @@ upload_template = '''
 # 4. Flask Routes
 #########################################
 
-# Landing page
 @app.route('/')
 def landing():
     return render_template_string(landing_template)
 
-# Traditional upload page (in case you want to support non-AJAX)
 @app.route('/upload', methods=['GET'])
 def upload():
     object_type = request.args.get('object_type', '').lower()
     if object_type not in {"person", "car", "box"}:
         flash("Please select a valid object type.")
         return redirect(url_for('landing'))
-    # In this version the AJAX version is preferred.
     return render_template_string(upload_template, object_type=object_type)
 
-# Endpoint to start analysis (called via AJAX)
 @app.route('/analyze', methods=['POST'])
 def analyze():
-    # Save uploaded file
     if 'file' not in request.files:
         return jsonify({"error": "No file provided."}), 400
     file = request.files['file']
@@ -509,23 +500,23 @@ def analyze():
     object_type = request.form.get('object_type', '').lower()
     if object_type not in {"person", "car", "box"}:
         return jsonify({"error": "Invalid object type."}), 400
+    # Retrieve the multiplier sent from the client
+    try:
+        multiplier = int(request.form.get('multiplier', 1))
+    except ValueError:
+        multiplier = 1
     upload_path = "uploaded.jpg"
     try:
         file.save(upload_path)
     except Exception as e:
         return jsonify({"error": "Error saving file."}), 500
 
-    # Create a unique job id and initialize job progress
     job_id = str(uuid.uuid4())
     jobs[job_id] = {"progress": 0, "result": None}
-    # Start background thread to process image
-    thread = threading.Thread(target=process_image, args=(job_id, upload_path, object_type))
+    thread = threading.Thread(target=process_image, args=(job_id, upload_path, object_type, multiplier))
     thread.start()
-    # Optionally remove the uploaded file after starting the thread
-    # (In a real system, you might want to keep it until processing is done.)
     return jsonify({"job_id": job_id})
 
-# Endpoint for client to poll progress
 @app.route('/progress', methods=['GET'])
 def progress():
     job_id = request.args.get('job_id', '')
@@ -533,14 +524,12 @@ def progress():
         return jsonify({"progress": 0})
     return jsonify({"progress": jobs[job_id].get("progress", 0)})
 
-# Endpoint for client to fetch final result
 @app.route('/result', methods=['GET'])
 def result():
     job_id = request.args.get('job_id', '')
     if job_id not in jobs or jobs[job_id].get("result") is None:
         return jsonify({"error": "Result not available."}), 404
     result = jobs[job_id]["result"]
-    # Clean up job if desired
     del jobs[job_id]
     return jsonify(result)
 
@@ -549,5 +538,4 @@ def result():
 #########################################
 
 if __name__ == '__main__':
-    # Run in threaded mode so background threads can work
     app.run(host="0.0.0.0", port=7860, threaded=True)