From dbf5f6ab78654b2a16ab9c0472c9f21f0c467aac Mon Sep 17 00:00:00 2001
From: Cowwan <CowwanVT@gmail.com>
Date: Thu, 8 Feb 2024 22:49:58 -0500
Subject: [PATCH] fixed an issue that caused the face tracker to crash if I
 left the frame added a second filter on face detection that requires the
 faces to be a certain size

---
 eyes.py          | 17 ++++++++++++-----
 facedetection.py |  5 ++---
 tracker.py       |  2 +-
 3 files changed, 15 insertions(+), 9 deletions(-)

diff --git a/eyes.py b/eyes.py
index 8db04fe..71d25e2 100644
--- a/eyes.py
+++ b/eyes.py
@@ -2,14 +2,18 @@ import numpy as np
 import math
 import cv2
 
-def clamp_to_im(pt, w, h): #8 times per frame, but that only accounts for 0.005ms
-    x=max(pt[0],0)
-    y=max(pt[1],0)
+def clamp_to_im(pt, w, h):
+    x = pt[0]
+    y = pt[1]
+    if x < 0:
+        x = 0
+    if y < 0:
+        y = 0
     if x >= w:
         x = w-1
     if y >= h:
         y = h-1
-    return (int(x),int(y+1))
+    return (int(x), int(y+1))
 
 def rotate(origin, point, a): #runs 22 times per frame
     x, y = point - origin
@@ -54,15 +58,18 @@ class Eye():
         #so I just don't if it's a relatively small angle
         if math.degrees(a) > 7.5 and math.degrees(a) < 352.5:
             im = rotate_image(im, a, self.outerPoint)
-
         im = im[int(y1):int(y2), int(x1):int(x2)]
+
+
         if np.prod(im.shape) < 1:
             self.image = None
             self.info = None
+            return
 
         if self.index == 1:
             im = cv2.flip(im, 1)
         scale = [(x2 - x1)/ 32., (y2 - y1)/ 32.]
+
         im = cv2.resize(im, (32, 32), interpolation=cv2.INTER_CUBIC)
         im = im.astype(np.float32) * self.std + self.mean
         im = np.expand_dims(im, 0)
diff --git a/facedetection.py b/facedetection.py
index 7663fba..f3349a1 100644
--- a/facedetection.py
+++ b/facedetection.py
@@ -9,9 +9,8 @@ def detect_faces(frame, model, detection_threshold ):
         outputs = outputs[0]
         maxpool = maxpool[0]
         mask = outputs[0] == maxpool[0]
-        mask2 = outputs[1] > 0.2
-        mask = mask.astype(int) * mask2.astype(int)
-        outputs[0] = outputs[0] * mask.astype(int)
+        mask2 = outputs[1] > 0.15
+        outputs[0] = outputs[0] * mask.astype(int) * mask2.astype(int)
         faceLocation = np.argmax(outputs[0].flatten())
         x = faceLocation % 56
         y = faceLocation // 56
diff --git a/tracker.py b/tracker.py
index 9b1bf77..b4de59d 100644
--- a/tracker.py
+++ b/tracker.py
@@ -48,7 +48,7 @@ class Models():
         self.gazeTracker = onnxruntime.InferenceSession(os.path.join(model_base_path, "mnv3_gaze32_split_opt.onnx"), sess_options=options, providers=providersList)
 
 class Tracker():
-    def __init__(self, width, height, featureType, threads, model_type=3, detection_threshold=0.5, threshold=0.6, silent=False):
+    def __init__(self, width, height, featureType, threads, model_type=3, detection_threshold=0.6, threshold=0.6, silent=False):
 
         self.detection_threshold = detection_threshold
         self.EyeTracker = eyes.EyeTracker()