<reponame>gigwegbe/image_classification_with_openmv_camera<filename>ei-opemv-openmv-v0/ei_image_classification.py
# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, lcd, ulab

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 (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.set_framesize(sensor.LCD)
sensor.skip_frames(time=2000)          # Let the camera adjust.

#sensor.skip_frames()
lcd.init()

net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()
    # default settings just do one detection... change them to search the image...
    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()))
        #print(obj.output())
        obj_arr = ulab.array(obj.output())
        obj_arr_arg = ulab.numerical.argmax(obj_arr)
        label_text = labels[obj_arr_arg]
        img.draw_string(0,0,label_text)
        lcd.display(img)

# Edge Impulse - OpenMV Image Classification Example

import sensor, image, time, os, tf, lcd, ulab

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 (320x240)
sensor.set_windowing((240, 240))       # Set 240x240 window.
sensor.set_framesize(sensor.LCD)
sensor.skip_frames(time=2000)          # Let the camera adjust.

#sensor.skip_frames()
lcd.init()

net = "trained.tflite"
labels = [line.rstrip('\n') for line in open("labels.txt")]

clock = time.clock()
while(True):
    clock.tick()

    img = sensor.snapshot()
    # default settings just do one detection... change them to search the image...
    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()))
        #print(obj.output())
        obj_arr = ulab.array(obj.output())
        obj_arr_arg = ulab.numerical.argmax(obj_arr)
        label_text = labels[obj_arr_arg]
        img.draw_string(0,0,label_text)
        lcd.display(img)
# Edge Impulse - OpenMV Image Classification Example # Reset and initialize the sensor. # Set pixel format to RGB565 (or GRAYSCALE) # Set frame size to QVGA (320x240) # Set 240x240 window. # Let the camera adjust. #sensor.skip_frames() # default settings just do one detection... change them to search the image... #print("**********\nPredictions at [x=%d,y=%d,w=%d,h=%d]" % obj.rect()) # This combines the labels and confidence values into a list of tuples #print(obj.output())