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Merge pull request #179 from lucidcode/feature/camera-sensor-interface
Upgrade to camera sensor interface
2 parents b13ff0e + 75a4dfa commit 9e2bed1

4 files changed

Lines changed: 443 additions & 463 deletions

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‎release/firmware.bin‎

65.3 KB
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‎software/face.py‎

Lines changed: 124 additions & 140 deletions
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import sensor
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import image
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import math
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import ml
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from ml.utils import NMS
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class face_detection:
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def __init__(self, config, comms):
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self.config = config
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self.comms = comms
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self.has_face = False
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self.face_cascade = image.HaarCascade("/rom/haarcascade_frontalface.cascade", stages=self.config.get('FaceStages'))
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self.face_object = [0, 0, 1, 1]
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self.face_angle = 0
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self.correct_angle = False
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self.ml_model = None
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self.detector = ""
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self.extra_fb = sensor.alloc_extra_fb(sensor.width(), sensor.height(), sensor.GRAYSCALE)
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def detect(self, img, global_variance):
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if not self.config.get('TrackFace') and not self.config.get('TensorFlow'):
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self.face_object = [0, 0, img.width(), img.height()]
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self.has_face = False
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return
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if global_variance >= self.config.get('TossThreshold'):
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self.face_object = [0, 0, img.width(), img.height()]
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self.has_face = False
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if self.config.get('TensorFlow'):
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if self.ml_model == None:
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self.ml_model = ml.Model('/rom/fomo_face_detection.tflite')
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for i, detection_list in enumerate(self.ml_model.predict([img], callback=self.post_process)):
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if i == 0 or len(detection_list) == 0:
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continue
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for (x, y, w, h), score in detection_list:
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self.face_object = (x - w, y - h, w * 4, h * 4)
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self.detector = "TensorFlow"
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self.has_face = True
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return
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if not self.config.get('TrackFace'):
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return
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self.face_angle = 0
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face_objects = img.find_features(self.face_cascade, threshold=self.config.get('FaceThreshold'), scale_factor=self.config.get('FaceScaleFactor'))
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if len(face_objects) == 0 and self.config.get('FaceAngles'):
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for angle in self.config.get('FaceAngles'):
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self.extra_fb.replace(img)
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self.extra_fb.rotation_corr(x_rotation=0.0, y_rotation=0.0, z_rotation=angle)
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face_objects = self.extra_fb.find_features(self.face_cascade, threshold=self.config.get('FaceThreshold'), scale_factor=self.config.get('FaceScaleFactor'))
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if face_objects:
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self.face_angle = angle
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break
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if face_objects:
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self.face_object = face_objects[0]
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self.detector = "HaarCascade"
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self.has_face = True
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if self.config.get('TrackEyes'):
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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)]
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if self.correct_angle:
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eyes_x = self.face_object[0] + int(self.face_object[2] * 1/5)
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eyes_y = self.face_object[1] + int(self.face_object[3] * 1/5)
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eyes_width = self.face_object[2] - int(self.face_object[2] * 1/5)
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eyes_height = int(self.face_object[3] * 2/5)
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self.face_object = [eyes_x, eyes_y, eyes_width, eyes_height]
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def draw_region(self, img):
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if not self.config.get('DrawFaceRegion'):
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return
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if self.face_object[2] == img.width():
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return
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if self.detector == "TensorFlow":
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img.draw_rectangle(self.face_object, color=(70, 130, 180))
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if self.detector == "HaarCascade":
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img.draw_rectangle(self.face_object)
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if self.correct_angle:
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face_x = self.face_object[0]
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face_y = self.face_object[1]
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face_width = self.face_object[2]
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face_height = self.face_object[3]
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theta = math.radians(self.face_angle * -1)
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offset = (0, 0)
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rect = [
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(face_x, face_y),
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(face_x, face_y + face_height),
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(face_x + face_width, face_y + face_height),
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(face_x + face_width, face_y)
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]
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rotated_rect = [translate(rotate(xy, theta), offset) for xy in rect]
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img.draw_line((rotated_rect[0][0], rotated_rect[0][1], rotated_rect[1][0], rotated_rect[1][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[1][0], rotated_rect[1][1], rotated_rect[2][0], rotated_rect[2][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[2][0], rotated_rect[2][1], rotated_rect[3][0], rotated_rect[3][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[3][0], rotated_rect[3][1], rotated_rect[0][0], rotated_rect[0][1]), color=(220, 220, 0))
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img.draw_string(face_x, face_y + face_height - 10, str(self.face_angle) + "^", color=(70, 130, 180), mono_space=False)
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def post_process(self, model, inputs, outputs):
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n, oh, ow, oc = model.output_shape[0]
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nms = NMS(ow, oh, inputs[0].roi)
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threshold_list = [(self.config.get('FaceConfidence'), 255)]
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for i in range(oc):
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img = image.Image(outputs[0][0, :, :, i] * 255)
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blobs = img.find_blobs(
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threshold_list, x_stride=1, area_threshold=1, pixels_threshold=1
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)
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for b in blobs:
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rect = b.rect()
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x, y, w, h = rect
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score = (
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img.get_statistics(thresholds=threshold_list, roi=rect).l_mean() / 255.0
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)
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nms.add_bounding_box(x, y, x + w, y + h, score, i)
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return nms.get_bounding_boxes()
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def rotate(xy, theta):
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cos_theta, sin_theta = math.cos(theta), math.sin(theta)
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return (
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int(xy[0] * cos_theta - xy[1] * sin_theta),
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int(xy[0] * sin_theta + xy[1] * cos_theta)
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)
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def translate(xy, offset):
140-
return xy[0] + offset[0], xy[1] + offset[1]
1+
import csi
2+
import image
3+
import math
4+
import ml
5+
from ml.postprocessing.edgeimpulse import Fomo
6+
from ml.utils import NMS
7+
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class face_detection:
9+
def __init__(self, config, comms, sensor):
10+
self.config = config
11+
self.comms = comms
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self.sensor = sensor
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self.has_face = False
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self.face_cascade = image.HaarCascade("/rom/haarcascade_frontalface.cascade", stages=self.config.get('FaceStages'))
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self.face_object = [0, 0, 1, 1]
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self.face_angle = 0
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self.correct_angle = False
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self.ml_model = None
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self.detector = ""
20+
21+
self.extra_fb = image.Image(self.sensor.width(), self.sensor.height(), csi.GRAYSCALE)
22+
23+
def detect(self, img, global_variance):
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if not self.config.get('TrackFace') and not self.config.get('TensorFlow'):
25+
self.face_object = [0, 0, img.width(), img.height()]
26+
self.has_face = False
27+
return
28+
29+
if global_variance >= self.config.get('TossThreshold'):
30+
self.face_object = [0, 0, img.width(), img.height()]
31+
self.has_face = False
32+
33+
if self.config.get('TensorFlow'):
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if self.ml_model == None:
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self.ml_model = ml.Model('/rom/fomo_face_detection.tflite', postprocess=Fomo(threshold=self.config.get('FaceConfidence')))
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for i, detection_list in enumerate(self.ml_model.predict([img])):
38+
if i == 0 or len(detection_list) == 0:
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continue
40+
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for (x, y, w, h), score in detection_list:
42+
self.face_object = (x - w, y - h, w * 4, h * 4)
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self.detector = "TensorFlow"
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self.has_face = True
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return
46+
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if not self.config.get('TrackFace'):
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return
49+
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self.face_angle = 0
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face_objects = img.find_features(self.face_cascade, threshold=self.config.get('FaceThreshold'), scale_factor=self.config.get('FaceScaleFactor'))
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if len(face_objects) == 0 and self.config.get('FaceAngles'):
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for angle in self.config.get('FaceAngles'):
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self.extra_fb.replace(img)
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self.extra_fb.rotation_corr(x_rotation=0.0, y_rotation=0.0, z_rotation=angle)
57+
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face_objects = self.extra_fb.find_features(self.face_cascade, threshold=self.config.get('FaceThreshold'), scale_factor=self.config.get('FaceScaleFactor'))
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if face_objects:
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self.face_angle = angle
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break
62+
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if face_objects:
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self.face_object = face_objects[0]
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self.detector = "HaarCascade"
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self.has_face = True
67+
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if self.config.get('TrackEyes'):
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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)]
70+
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if self.correct_angle:
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eyes_x = self.face_object[0] + int(self.face_object[2] * 1/5)
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eyes_y = self.face_object[1] + int(self.face_object[3] * 1/5)
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eyes_width = self.face_object[2] - int(self.face_object[2] * 1/5)
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eyes_height = int(self.face_object[3] * 2/5)
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self.face_object = [eyes_x, eyes_y, eyes_width, eyes_height]
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def draw_region(self, img):
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if not self.config.get('DrawFaceRegion'):
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return
81+
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if self.face_object[2] == img.width():
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return
84+
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if self.detector == "TensorFlow":
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img.draw_rectangle(self.face_object, color=(70, 130, 180))
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if self.detector == "HaarCascade":
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img.draw_rectangle(self.face_object)
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if self.correct_angle:
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face_x = self.face_object[0]
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face_y = self.face_object[1]
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face_width = self.face_object[2]
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face_height = self.face_object[3]
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theta = math.radians(self.face_angle * -1)
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offset = (0, 0)
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rect = [
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(face_x, face_y),
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(face_x, face_y + face_height),
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(face_x + face_width, face_y + face_height),
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(face_x + face_width, face_y)
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]
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rotated_rect = [self.translate(self.rotate(xy, theta), offset) for xy in rect]
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img.draw_line((rotated_rect[0][0], rotated_rect[0][1], rotated_rect[1][0], rotated_rect[1][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[1][0], rotated_rect[1][1], rotated_rect[2][0], rotated_rect[2][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[2][0], rotated_rect[2][1], rotated_rect[3][0], rotated_rect[3][1]), color=(220, 220, 0))
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img.draw_line((rotated_rect[3][0], rotated_rect[3][1], rotated_rect[0][0], rotated_rect[0][1]), color=(220, 220, 0))
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img.draw_string(face_x, face_y + face_height - 10, str(self.face_angle) + "^", color=(70, 130, 180), mono_space=False)
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def rotate(self, xy, theta):
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cos_theta, sin_theta = math.cos(theta), math.sin(theta)
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return (
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int(xy[0] * cos_theta - xy[1] * sin_theta),
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int(xy[0] * sin_theta + xy[1] * cos_theta)
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)
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def translate(self, xy, offset):
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return xy[0] + offset[0], xy[1] + offset[1]

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