openmv/scripts/examples/25-Machine-Learning/tf_person_detection_search_whole_window.py
Kwabena W. Agyeman 6a9c0a370e Add support for TensorFlow Lite for Microcontrollers
This has been a long-time coming, but, it's finally here. TensorFlow lite
runs on the OpenMV Cam now. Better yet, a person detection model
is now built-into all OpenMV Cams too!

Our default code does image classification and supports multi-scale
object detection using a sliding window.

In a coming PR I will add mobilenet examples for the H7 with SDRAM
and image segmentation support.
2019-10-25 21:01:21 +02:00

44 lines
2.3 KiB
Python

# TensorFlow Lite Person Dection Example
#
# Google's Person Detection Model detects if a person is in view.
#
# In this example we slide the detector window over the image and get a list
# of activations. Note that use a CNN with a sliding window is extremely compute
# expensive so for an exhaustive search do not expect the CNN to be real-time.
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.GRAYSCALE) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (320x240)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
# Load the built-in person detection network (the network is in your OpenMV Cam's firmware).
net = tf.load('person_detection')
labels = ['unsure', 'person', 'no_person']
clock = time.clock()
while(True):
clock.tick()
img = sensor.snapshot()
# net.classify() will run the network on an roi in the image (or on the whole image if the roi is not
# specified). A classification score output vector will be generated for each location. At each scale the
# detection window is moved around in the ROI using x_overlap (0-1) and y_overlap (0-1) as a guide.
# If you set the overlap to 0.5 then each detection window will overlap the previous one by 50%. Note
# the computational work load goes WAY up the more overlap. Finally, for multi-scale matching after
# sliding the network around in the x/y dimensions the detection window will shrink by scale_mul (0-1)
# down to min_scale (0-1). For example, if scale_mul is 0.5 the detection window will shrink by 50%.
# Note that at a lower scale there's even more area to search if x_overlap and y_overlap are small...
# default settings just do one detection... change them to search the image...
for obj in net.classify(img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
print("**********\nDetections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
for i in range(len(obj.output())):
print("%s = %f" % (labels[i], obj.output()[i]))
img.draw_rectangle(obj.rect())
img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False)
print(clock.fps(), "fps")