The model selection parameter should now be easier to understand and two less useful models have been removed to save space. Width, height and FPS are now written to log file and both width and height are also transmitted over the network. The video output function has been changed to use a lossless codec, but videos need to be converted to a more common format before importing them to Vegas now.

tracker.py

@@ -88,7 +88,7 @@ def worker_thread(session, input, crop_info, queue, input_name, idx, tracker):
     queue.put((session, conf, lms, crop_info, idx))
 
 class Tracker():
-    def __init__(self, width, height, model_type=1, threshold=0.4, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, pnp_quality=1, model_dir=None, no_gaze=False):
+    def __init__(self, width, height, model_type=3, threshold=0.4, max_faces=1, discard_after=5, scan_every=3, bbox_growth=0.0, max_threads=4, silent=False, pnp_quality=1, model_dir=None, no_gaze=False):
         options = onnxruntime.SessionOptions()
         options.inter_op_num_threads = 1
         options.intra_op_num_threads = max_threads
@@ -96,7 +96,12 @@ class Tracker():
         options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
         options.log_severity_level = 3
         self.model_type = model_type
-        self.models = ["snv2_opt.onnx", "snv2_opt_b.onnx", "snv2_opt_fast.onnx"]
+        self.models = [
+            "snv2_opt_fast.onnx",
+            "mnv3_opt_fast.onnx",
+            "snv2_opt_b.onnx",
+            "mnv3_opt_b.onnx"
+        ]
         model = self.models[self.model_type]
         model_base_path = resolve(os.path.join("models"))
         if model_dir is None:
@@ -243,7 +248,7 @@ class Tracker():
         self.scan_every = scan_every
         self.bbox_growth = bbox_growth
         self.silent = silent
-        self.res = 224. if self.model_type < 2 else 112.
+        self.res = 224. if self.model_type != 0 else 112.
         self.res_i = int(self.res)
         self.no_gaze = no_gaze
 
@@ -337,7 +342,7 @@ class Tracker():
         #im = (im - mean) / std
         im = np.expand_dims(im, 0)
         im = np.transpose(im, (0,3,1,2))
-        if self.model_type > 1:
+        if self.model_type == 0:
             im = im.mean(1)
             im = np.expand_dims(im, 1)
         return im