compatibility for OpenPifPaf v0.12.5 (#34)

Author: Sven Kreiss

README.md

@@ -83,6 +83,14 @@ the browser cache for static assets and autoreload when source files change.
 # Citation
 
 ```
+@article{kreiss2021openpifpaf,
+  title = {{OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association}},
+  author = {Sven Kreiss and Lorenzo Bertoni and Alexandre Alahi},
+  journal = {arXiv preprint arXiv:2103.02440},
+  month = {March},
+  year = {2021}
+}
+
 @InProceedings{kreiss2019pifpaf,
   author = {Kreiss, Sven and Bertoni, Lorenzo and Alahi, Alexandre},
   title = {PifPaf: Composite Fields for Human Pose Estimation},

js/src/visualization.ts

@@ -47,6 +47,7 @@ export class Visualization {
 
     draw(image: Blob, data) {
         if (data === null) return;
+        console.log(data);
         const annotations = data.annotations;
 
         // adjust height of output canvas

openpifpafwebdemo/handlers/human_poses.py

@@ -32,12 +32,13 @@ async def post(self):  # pylint: disable=arguments-differ
         if not key.validate(channel_id):
             return
 
-        annotations = self.application.processor.single_image(image, resize=resize)
-        await self.finish(json.dumps({'channel': channel_id, 'annotations': annotations}))
+        out_data = self.application.processor.single_image(image, resize=resize)
+        out_data['channel'] = channel_id
+        await self.finish(json.dumps(out_data))
 
         channel_name = 'channel:{}'.format(channel_id)
         LOG.info('publishing to %s', channel_name)
-        self.application.signal.emit(channel_name, annotations)
+        self.application.signal.emit(channel_name, out_data)
 
     def options(self):
         self.set_default_headers()

openpifpafwebdemo/processor.py

@@ -10,18 +10,19 @@
 LOG = logging.getLogger(__name__)
 
 
-class Processor(object):
+class Processor:
     def __init__(self, width_height, args):
         self.width_height = width_height
 
         # load model
-        model_cpu, _ = openpifpaf.network.factory_from_args(args)
+        model_cpu, _ = openpifpaf.network.Factory().factory()
         self.model = model_cpu.to(args.device)
         head_metas = [hn.meta for hn in model_cpu.head_nets]
         self.processor = openpifpaf.decoder.factory(head_metas)
         self.device = args.device
 
     def single_image(self, image_bytes, *, resize=True):
+        start = time.perf_counter()
         im = PIL.Image.open(io.BytesIO(image_bytes)).convert('RGB')
 
         if resize:
@@ -30,26 +31,29 @@ def single_image(self, image_bytes, *, resize=True):
                 target_wh = (target_wh[1], target_wh[0])
             if im.size[0] != target_wh[0] or im.size[1] != target_wh[1]:
                 LOG.warning('have to resize image to %s from %s', target_wh, im.size)
-                im = im.resize(target_wh, PIL.Image.BICUBIC)
+                im = im.resize(target_wh, PIL.Image.BILINEAR)
         width_height = im.size
 
-        start = time.time()
         preprocess = openpifpaf.transforms.EVAL_TRANSFORM
         processed_image = preprocess(im, [], None)[0]
-        LOG.debug('preprocessing time: %.3f', time.time() - start)
+        preprocessing_time = time.perf_counter() - start
 
         image_tensors_batch = torch.unsqueeze(processed_image.float(), 0)
         pred_anns = self.processor.batch(self.model, image_tensors_batch, device=self.device)[0]
 
-        json_data = [(ann
+        json_anns = [(ann
                       .rescale((1.0 / width_height[0], 1.0 / width_height[1]))
                       .json_data(coordinate_digits=5))
                      for ann in pred_anns]
-        for d in json_data:
+        for d in json_anns:
             d['keypoints'] = list(zip(
                 d['keypoints'][0::3],
                 d['keypoints'][1::3],
                 d['keypoints'][2::3],
             ))
-            d['width_height'] = width_height
-        return json_data
+
+        return {
+            'annotations': json_anns,
+            'width_height': width_height,
+            'preprocessing_ms': round(preprocessing_time * 1000.0, 1),
+        }

openpifpafwebdemo/server.py

@@ -21,6 +21,8 @@
 from . import handlers
 from .signal import Signal
 
+LOG = logging.getLogger(__name__)
+
 
 async def grep_static(dest, url='http://127.0.0.1:5000'):
     http_client = tornado.httpclient.AsyncHTTPClient()
@@ -40,7 +42,8 @@ def cli():
     )
 
     openpifpaf.decoder.cli(parser)
-    openpifpaf.network.cli(parser)
+    openpifpaf.logger.cli(parser)
+    openpifpaf.network.Factory.cli(parser)
 
     parser.add_argument('--disable-cuda', action='store_true',
                         help='disable CUDA')
@@ -51,8 +54,6 @@ def cli():
                         help='directory in which to create a static version of this page')
     parser.add_argument('--demo-password', default=None,
                         help='password that allows better performance for a demo')
-    parser.add_argument('--debug', default=False, action='store_true',
-                        help='debug messages and autoreload')
     parser.add_argument('--google-analytics',
                         help='provide a google analytics id to inject analytics code')
 
@@ -76,19 +77,17 @@ def cli():
 
     args = parser.parse_args()
 
-    # log
-    logging.basicConfig(level=logging.INFO if not args.debug else logging.DEBUG)
-
     # configure
-    openpifpaf.network.configure(args)
+    openpifpaf.logger.configure(args, LOG)
+    openpifpaf.network.Factory.configure(args)
 
     # config
-    logging.debug('host=%s, port=%d', args.host, args.port)
-    logging.debug('Python %s, OpenPifPafWebDemo %s', sys.version, __version__)
+    LOG.debug('host=%s, port=%d', args.host, args.port)
+    LOG.debug('Python %s, OpenPifPafWebDemo %s', sys.version, __version__)
     if args.host in ('localhost', '127.0.0.1'):
-        logging.info('Open http://%s:%d in a web browser.', args.host, args.port)
+        LOG.info('Open http://%s:%d in a web browser.', args.host, args.port)
     if args.host != '0.0.0.0':
-        logging.info('Access is restricted by IP address. Use --host=0.0.0.0 to allow all.')
+        LOG.info('Access is restricted by IP address. Use --host=0.0.0.0 to allow all.')
 
     # add args.device
     args.device = torch.device('cpu')
@@ -109,7 +108,7 @@ def main():
     args = cli()
     width_height = (int(640 * args.resolution // 16) * 16 + 1,
                     int(480 * args.resolution // 16) * 16 + 1)
-    logging.debug('target width and height = %s', width_height)
+    LOG.debug('target width and height = %s', width_height)
     processor_singleton = Processor(width_height, args)
 
     static_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'static')
@@ -123,7 +122,7 @@ def main():
     tornado.autoreload.watch('openpifpafwebdemo/static/frontend.js')
     tornado.autoreload.watch('openpifpafwebdemo/static/clientside.js')
 
-    if args.debug:
+    if LOG.getEffectiveLevel() == logging.DEBUG:
         version = '{}-{}'.format(
             __version__,
             ''.join(random.choices(string.ascii_lowercase + string.digits, k=4))
@@ -185,7 +184,7 @@ def main():
                 'keyfile': os.path.join(module_dir, 'test', 'test.key'),
             }
 
-        logging.info('Open https://%s:%d in a web browser.', args.host, args.ssl_port)
+        LOG.info('Open https://%s:%d in a web browser.', args.host, args.ssl_port)
         app.listen(args.ssl_port, ssl_options=ssl_ctx)
 
     try:

setup.py

@@ -17,7 +17,7 @@
     include_package_data=True,
 
     install_requires=[
-        'openpifpaf>=0.12b2',
+        'openpifpaf>=0.12.5',
         'tornado>=6',
     ],
     extras_require={