Add Edge Impulse Faster Objects More Objects

Author: IAmCoder

release/firmware.bin

@@ -35,6 +35,7 @@ def __init__(self):
         self.default['CreateGifs'] = 1
         self.default['TrackFace'] = 1
         self.default['TrackEyes'] = 0
+        self.default['TensorFlow'] = 1
         self.default['FaceFeatures'] = 16
         self.default['DrawFaceRegion'] = 1
         self.default['FaceStages'] = 25

software/config.py

@@ -1,7 +1,8 @@
 import sensor
 import image
-import utime
 import math
+import ml
+from ml.utils import NMS
 
 class face_detection:
     def __init__(self, config, comms):
@@ -12,16 +13,35 @@ def __init__(self, config, comms):
         self.face_object = [0, 0, 1, 1]
         self.face_angle = 0
         self.correct_angle = False
+        self.ml_model = None
+        self.min_confidence = 0.4
+        self.threshold_list = [(math.ceil(self.min_confidence * 255), 255)]
 
         self.extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
 
-    def detect(self, img):
+    def detect(self, img, global_variance):
         if not self.config.get('TrackFace'):
             self.face_object = [0, 0, img.width(), img.height()]
             self.has_face = False
             return
 
-        now = utime.ticks_ms()
+        if global_variance >= self.config.get('TossThreshold'):
+            self.face_object = [0, 0, img.width(), img.height()]
+            self.has_face = False
+
+        if self.config.get('TensorFlow'):
+            if self.ml_model == None:
+                self.ml_model = ml.Model('fomo_face_detection')                
+        
+            for i, detection_list in enumerate(self.ml_model.predict([img], callback=self.post_process)):
+                if i == 0 or len(detection_list) == 0:
+                    continue
+
+                for (x, y, w, h), score in detection_list:
+                    self.face_object = (x - w, y - h, w * 4, h * 4)
+                    self.has_face = True
+                    return
+
         self.face_angle = 0
         face_objects = img.find_features(self.face_cascade, threshold=self.config.get('FaceThreshold'), scale_factor=self.config.get('FaceScaleFactor'))
 
@@ -38,6 +58,7 @@ def detect(self, img):
         if face_objects:
             self.face_object = face_objects[0]
             self.has_face = True
+
             if self.config.get('TrackEyes'):
                 self.face_object = [self.face_object[0], self.face_object[1] + int(self.face_object[3] * 1/5), self.face_object[2], int(self.face_object[3] * 2/5)]
 
@@ -47,9 +68,6 @@ def detect(self, img):
                     eyes_width = self.face_object[2] - int(self.face_object[2] * 1/5)
                     eyes_height = int(self.face_object[3] * 2/5)
                     self.face_object = [eyes_x, eyes_y, eyes_width, eyes_height]
-        else:
-            self.face_object = [0, 0, img.width(), img.height()]
-            self.has_face = False
 
     def draw_region(self, img):
         if not self.config.get('DrawFaceRegion'):
@@ -84,13 +102,30 @@ def draw_region(self, img):
             img.draw_line((rotated_rect[3][0], rotated_rect[3][1], rotated_rect[0][0], rotated_rect[0][1]), color=(220, 220, 0))
             img.draw_string(face_x, face_y + face_height - 10, str(self.face_angle) + "^", color=(70, 130, 180), mono_space=False)
 
-def rotate(xy, theta):
-    cos_theta, sin_theta = math.cos(theta), math.sin(theta)
-
-    return (
-        int(xy[0] * cos_theta - xy[1] * sin_theta),
-        int(xy[0] * sin_theta + xy[1] * cos_theta)
-    )
-
-def translate(xy, offset):
-    return xy[0] + offset[0], xy[1] + offset[1]
+    def post_process(self, model, inputs, outputs):
+        n, oh, ow, oc = model.output_shape[0]
+        nms = NMS(ow, oh, inputs[0].roi)
+        for i in range(oc):
+            img = image.Image(outputs[0][0, :, :, i] * 255)
+            blobs = img.find_blobs(
+                self.threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
+            )
+            for b in blobs:
+                rect = b.rect()
+                x, y, w, h = rect
+                score = (
+                    img.get_statistics(thresholds=self.threshold_list, roi=rect).l_mean() / 255.0
+                )
+                nms.add_bounding_box(x, y, x + w, y + h, score, i)
+        return nms.get_bounding_boxes()
+
+    def rotate(xy, theta):
+        cos_theta, sin_theta = math.cos(theta), math.sin(theta)
+
+        return (
+            int(xy[0] * cos_theta - xy[1] * sin_theta),
+            int(xy[0] * sin_theta + xy[1] * cos_theta)
+        )
+
+    def translate(xy, offset):
+        return xy[0] + offset[0], xy[1] + offset[1]

software/face.py

@@ -98,12 +98,7 @@ def monitor(self):
             try:
                 self.img = sensor.snapshot()
 
-                if self.config.get('TrackFace'):
-                    self.img.gamma(gamma=1.0, contrast=1.5, brightness=0.0)
-                else:
-                    time.sleep_ms(64)
-
-                self.face.detect(self.img)
+                self.face.detect(self.img, self.global_variance)
                 self.global_variance, self.variance = self.img.variance(self.extra_fb, self.config.get('PixelThreshold'), self.config.get('PixelRange'), self.face.face_object)
 
                 if self.variance > self.peak_variance:

software/inspec.py

@@ -10,6 +10,7 @@ def __init__(self, config, face):
     def detect(self, variance, global_variance):
         if global_variance >= self.config.get('TossThreshold'):
             self.eye_movements = 0
+            self.last_eye_movement = now + 1000 * 10
 
         now = utime.ticks_ms()
         if now - self.last_eye_movement > 1000 * 60:

software/rem.py

@@ -1 +1 @@
-version = '1.2.5'
+version = '1.2.6'