Add a progress bar

main.py

@@ -1,6 +1,7 @@
 import os
 import multiprocessing
-from flask import Flask, request, render_template
+import threading
+from flask import Flask, request, render_template, jsonify
 from timelapse import process_faces
 
 app = Flask(__name__)
@@ -17,43 +18,67 @@ LANDMARK_MODEL = "shape_predictor_68_face_landmarks.dat"
 LEFT_EYE_POS = (0.35, 0.45)
 AVAILABLE_CORES = multiprocessing.cpu_count()
 
+# Global progress dictionary – only one job at a time is assumed here
+progress_info = {"completed": 0, "total": 0, "status": "idle"}
+
+def update_progress(current, total):
+    progress_info["completed"] = current
+    progress_info["total"] = total
+    progress_info["status"] = "running" if current < total else "done"
+
+def background_process(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers):
+    try:
+        progress_info["status"] = "running"
+        process_faces(
+            api_key=API_KEY,
+            base_url=BASE_URL,
+            person_id=person_id,
+            output_folder=OUTPUT_FOLDER,
+            padding_percent=padding_percent,
+            resize_width=resize_size,
+            resize_height=resize_size,
+            min_face_width=face_resolution_threshold,
+            min_face_height=face_resolution_threshold,
+            pose_threshold=pose_threshold,
+            desired_left_eye=LEFT_EYE_POS,
+            max_workers=max_workers,
+            face_detect_model_path=FACE_DETECT_MODEL,
+            landmark_model_path=LANDMARK_MODEL,
+            progress_callback=update_progress
+        )
+    except Exception as e:
+        progress_info["status"] = f"error: {e}"
+    else:
+        progress_info["status"] = "done"
+
+@app.route("/progress")
+def progress():
+    return jsonify(progress_info)
+
 @app.route("/", methods=["GET", "POST"])
 def index():
     result = None
     error = None
-    # Create max_workers_options as a list of numbers from 1 to AVAILABLE_CORES
+    # Create max_workers_options as a list from 1 to AVAILABLE_CORES
     max_workers_options = list(range(1, AVAILABLE_CORES + 1))
     if request.method == "POST":
         try:
-            api_key = API_KEY
-            base_url = BASE_URL
-            output_folder = OUTPUT_FOLDER
-
-            # Get user input from the form
             person_id = request.form["person_id"]
             padding_percent = float(request.form.get("padding_percent", 30)) / 100
             resize_size = int(request.form.get("resize_size", 512))
             face_resolution_threshold = int(request.form.get("face_resolution_threshold", 128))
             pose_threshold = float(request.form.get("pose_threshold", 25))
             max_workers = int(request.form.get("max_workers", 1))  # default is 1
-
+            # Reset progress info before starting
-            processed_files = process_faces(
+            progress_info["completed"] = 0
-                api_key=api_key,
+            progress_info["total"] = 0
-                base_url=base_url,
+            progress_info["status"] = "idle"
-                person_id=person_id,
+            # Start the processing in a background thread
-                output_folder=output_folder,
+            threading.Thread(
-                padding_percent=padding_percent,
+                target=background_process,
-                resize_width=resize_size,
+                args=(person_id, padding_percent, resize_size, face_resolution_threshold, pose_threshold, max_workers)
-                resize_height=resize_size,
+            ).start()
-                min_face_width=face_resolution_threshold,
+            result = "Processing started. Please wait and watch the progress bar below."
-                min_face_height=face_resolution_threshold,
-                pose_threshold=pose_threshold,
-                desired_left_eye=LEFT_EYE_POS,
-                max_workers=max_workers,
-                face_detect_model_path=FACE_DETECT_MODEL,
-                landmark_model_path=LANDMARK_MODEL
-            )
-            result = f"Finished processing. {len(processed_files)} images saved in '{output_folder}'."
         except Exception as e:
             error = f"Error processing request: {e}"
     return render_template("index.html", result=result, error=error, max_workers_options=max_workers_options)

templates/index.html

@@ -65,6 +65,14 @@
         .result {
             color: #080;
         }
+        #progressContainer {
+            margin-top: 20px;
+            text-align: center;
+        }
+        progress {
+            width: 100%;
+            height: 25px;
+        }
     </style>
 </head>
 <body>
@@ -107,6 +115,30 @@
         {% if result %}
             <p class="result">{{ result }}</p>
         {% endif %}
+        <!-- Progress Bar -->
+        <div id="progressContainer">
+            <progress id="progressBar" value="0" max="100"></progress>
+            <p id="progressText">0%</p>
+        </div>
     </div>
+    <script>
+        function fetchProgress() {
+            fetch('/progress')
+                .then(response => response.json())
+                .then(data => {
+                    const progressBar = document.getElementById("progressBar");
+                    const progressText = document.getElementById("progressText");
+                    if (data.total > 0) {
+                        progressBar.max = data.total;
+                        progressBar.value = data.completed;
+                        const percent = Math.floor((data.completed / data.total) * 100);
+                        progressText.textContent = percent + "%";
+                    }
+                })
+                .catch(err => console.error('Error fetching progress:', err));
+        }
+        // Poll every 1 second
+        setInterval(fetchProgress, 1000);
+    </script>
 </body>
 </html>

timelapse.py

@@ -1,5 +1,4 @@
 # timelapse.py
-
 import os
 import io
 import requests
@@ -16,7 +15,6 @@ import logging
 class TqdmLoggingHandler(logging.Handler):
     def __init__(self, level=logging.NOTSET):
         super().__init__(level)
-
     def emit(self, record):
         try:
             msg = self.format(record)
@@ -31,7 +29,6 @@ formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s', datef
 tqdm_handler.setFormatter(formatter)
 logger.addHandler(tqdm_handler)
 
-
 def get_assets_with_person(api_key, base_url, person_id):
     headers = {
         'Content-Type': 'application/json',
@@ -63,19 +60,16 @@ def get_assets_with_person(api_key, base_url, person_id):
         payload["page"] = data['assets'].get('nextPage')
     return all_assets
 
-
 def download_asset(api_key, base_url, asset_id):
     headers = {'x-api-key': api_key}
     response = requests.get(f'{base_url}/assets/{asset_id}/original', headers=headers)
     response.raise_for_status()
     return response.content
 
-
 def format_timestamp(timestamp):
     dt = datetime.fromisoformat(timestamp.replace("Z", "+00:00"))
     return dt.strftime("%Y%m%d_%H%M%S")
 
-
 def crop_face_from_metadata(image, face_data, padding_percent):
     face_img_width = face_data.get("imageWidth")
     face_img_height = face_data.get("imageHeight")
@@ -95,7 +89,6 @@ def crop_face_from_metadata(image, face_data, padding_percent):
     new_y2 = min(y2 + padding, img_height)
     return image.crop((new_x1, new_y1, new_x2, new_y2))
 
-
 def get_head_pose(shape, img_size):
     image_points = np.array([
         (shape.part(30).x, shape.part(30).y),
@@ -131,22 +124,18 @@ def get_head_pose(shape, img_size):
     pitch, yaw, roll = [float(angle) for angle in eulerAngles]
     return pitch, yaw, roll
 
-
 def align_face(image, predictor, detector, desired_face_width, desired_face_height,
                desired_left_eye, pose_threshold):
     image_np = np.array(image)
     gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY)
-
     detections = detector(gray)
     if not detections:
         logger.info("No face detected in the crop. Discarding.")
         return None
-
     if hasattr(detections[0], "rect"):
         rect = detections[0].rect
     else:
         rect = detections[0]
-
     shape = predictor(gray, rect)
     img_size = (image_np.shape[1], image_np.shape[0])
     pitch, yaw, roll = get_head_pose(shape, img_size)
@@ -181,12 +170,10 @@ def align_face(image, predictor, detector, desired_face_width, desired_face_heig
     )
     return Image.fromarray(aligned_face_np)
 
-
 def process_asset_worker(asset, api_key, base_url, person_id, output_folder,
                          padding_percent, min_face_width, min_face_height,
                          resize_width, resize_height, pose_threshold, desired_left_eye,
                          cnn_model_path, predictor_model_path):
-
     detector = dlib.cnn_face_detection_model_v1(cnn_model_path)
     local_predictor = dlib.shape_predictor(predictor_model_path)
     try:
@@ -224,11 +211,9 @@ def process_asset_worker(asset, api_key, base_url, person_id, output_folder,
     aligned_face.save(filename)
     return filename
 
-
 def process_asset_wrapper(asset, process_args):
     return process_asset_worker(asset, *process_args)
 
-
 def process_faces(
     api_key,
     base_url,
@@ -243,25 +228,27 @@ def process_faces(
     desired_left_eye=(0.35, 0.45),
     max_workers=1,
     face_detect_model_path="mmod_human_face_detector.dat",
-    landmark_model_path="shape_predictor_68_face_landmarks.dat"
+    landmark_model_path="shape_predictor_68_face_landmarks.dat",
+    progress_callback=None  # New optional parameter
 ):
     os.makedirs(output_folder, exist_ok=True)
-
     assets = get_assets_with_person(api_key, base_url, person_id)
     logger.info(f"Found {len(assets)} assets containing the person.")
-
+    total_assets = len(assets)
+    if progress_callback:
+        progress_callback(0, total_assets)
     process_args = (
         api_key, base_url, person_id, output_folder,
         padding_percent, min_face_width, min_face_height,
         resize_width, resize_height, pose_threshold, desired_left_eye,
         face_detect_model_path, landmark_model_path
     )
-
+    results = []
     with concurrent.futures.ProcessPoolExecutor(max_workers=max_workers) as executor:
-        results = list(tqdm(
+        for result in tqdm(executor.map(process_asset_wrapper, assets, [process_args]*total_assets), total=total_assets):
-            executor.map(process_asset_wrapper, assets, [process_args] * len(assets)),
+            results.append(result)
-            total=len(assets)
+            if progress_callback:
-        ))
+                progress_callback(len(results), total_assets)
     processed_files = [r for r in results if r is not None]
     logger.info(f"Finished processing. {len(processed_files)} images saved.")
     return processed_files