def image_classification():
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (?x?)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
labels = ['3', '4', '0', 'other']
img = sensor.snapshot()
for obj in tf.classify('/model_demo.tflite',img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
img.draw_rectangle(obj.rect())
img.draw_string(obj.x()+3, obj.y()-1, labels[obj.output().index(max(obj.output()))], mono_space = False)
return labels[obj.output().index(max(obj.output()))]
def image_classification():
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (?x?)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
labels = ['3', '4', '0', 'other']
img = sensor.snapshot()
for obj in tf.classify('/model_demo.tflite',
img,
min_scale=1.0,
scale_mul=0.5,
x_overlap=0.0,
y_overlap=0.0):
img.draw_rectangle(obj.rect())
img.draw_string(obj.x() + 3,
obj.y() - 1,
labels[obj.output().index(max(obj.output()))],
mono_space=False)
RED_LED_PIN = 1
BLUE_LED_PIN = 3
sensor.reset() # Initialize the camera sensor.
sensor.set_pixformat(sensor.RGB565) # or sensor.GRAYSCALE
sensor.set_framesize(sensor.QVGA) # or sensor.QQVGA (or others)
sensor.skip_frames(time=2000) # Let new settings take affect.
pyb.LED(RED_LED_PIN).on()
sensor.skip_frames(time=2000) # Give the user time to get ready.
pyb.LED(RED_LED_PIN).off()
pyb.LED(BLUE_LED_PIN).on()
print("You're on camera!")
sensor.snapshot().save("example.jpg") # or "example.bmp" (or others)
pyb.LED(BLUE_LED_PIN).off()
print("Done! Reset the camera to see the saved image.")
return labels[obj.output().index(max(obj.output()))]
def predict_cough(img):
for obj in tf.classify(net,
img,
min_scale=1.0,
scale_mul=0.8,
x_overlap=0.5,
y_overlap=0.5):
print("**********\nPredictions Cough at [x=%d,y=%d,w=%d,h=%d]" %
obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
# draw a rectangle and the predicted label on screen
img.draw_rectangle(obj.rect())
img.draw_string(obj.x() + 3,
obj.y() - 1,
labels[obj.output().index(max(obj.output()))],
mono_space=False)
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...
# Setting x_overlap=-1 forces the window to stay centered in the ROI in the x direction always. If
# y_overlap is not -1 the method will search in all vertical positions.
# Setting y_overlap=-1 forces the window to stay centered in the ROI in the y direction always. If
# x_overlap is not -1 the method will serach in all horizontal positions.
# default settings just do one detection... change them to search the image...
for obj in tf.classify(mobilenet, img, min_scale=1.0, scale_mul=0.5, x_overlap=-1, y_overlap=-1):
print("**********\nTop 5 Detections at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
# and then sorts that list by the confidence values.
sorted_list = sorted(zip(labels, obj.output()), key = lambda x: x[1], reverse = True)
for i in range(5):
print("%s = %f" % (sorted_list[i][0], sorted_list[i][1]))
print(clock.fps(), "fps")
def Image_Classification_MNIST():
sensor.skip_frames(time = 2000)
img = sensor.snapshot()
img.save("Mission1_Snapshot.jpg")
for obj in tf.classify('/model_demo.tflite',img, min_scale=1.0, scale_mul=0.5, x_overlap=0.0, y_overlap=0.0):
return str(labels[obj.output().index(max(obj.output()))]))
sensor.set_pixformat(
sensor.RGB565) # 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.
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
clock = time.clock()
while (True):
clock.tick()
img = sensor.snapshot()
for obj in tf.classify(net,
img,
min_scale=1.0,
scale_mul=0.8,
x_overlap=0.5,
y_overlap=0.5):
print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect())
img.draw_rectangle(obj.rect())
# This combines the labels and confidence values into a list of tuples
predictions_list = list(zip(labels, obj.output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
print(clock.fps(), "fps")
def person_detection(data):
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QVGA)
scores = tf.classify("person_detection", sensor.snapshot())[0].output()
return ['unsure', 'person', 'no_person'][scores.index(max(scores))].encode()
import sensor, image, time, os, tf
sensor.reset() # Reset and initialize the sensor.
sensor.set_pixformat(
sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)
sensor.set_framesize(sensor.QVGA) # Set frame size to QVGA (?x?)
sensor.set_windowing((240, 240)) # Set 240x240 window.
sensor.skip_frames(time=2000) # Let the camera adjust.
labels = ['3', '4', '0', 'other']
img = sensor.snapshot()
for obj in tf.classify('model_demo.tflite',
img,
min_scale=1.0,
scale_mul=0.5,
x_overlap=0.0,
y_overlap=0.0):
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("This is : ", labels[obj.output().index(max(obj.output()))])
# Test Portenta - By: khairunnasulfahmi - Sun Nov 1 2020
import image, tf, os
def transfer(trigger, conf):
time_current = time.localtime()
time_s = '{:04d}-{:02d}-{:02d} {:02d}:{:02d}:{:02d}'.format(
time_current[0], time_current[1], time_current[2], time_current[3],
time_current[4], time_current[5])
data = {'data': str(time_s) + ", " + trigger + ", " + str(conf)}
print(data['data'])
# Load the custom trained tflite model
net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]
print("starting...")
img = image.Image("test_img.PPM", copy_to_fb=True)
obj = tf.classify(net, img)
predictions_list = list(zip(labels, obj[0].output()))
for i in range(len(predictions_list)):
print("%s = %f" % (predictions_list[i][0], predictions_list[i][1]))
