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scan_ribbon.py
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scan_ribbon.py
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import cv2
import numpy
import time
from matplotlib import pyplot as plt
import mido
# Image settings
camera = cv2.VideoCapture(1)
capture_interval = 0
#resize_multiplier = .05
resize_multiplier = 0.2
brightness_threshold = 180
skip_img_left = 25 # Ignore left side of image in percents
skip_img_right = 25 # Ignore right side of image in percents
max_display_rows = 10 # Maximum number of ribbon rows to display in pixels
# Note settings
note_min = 40
note_max = 100
delta_note = 12
note_multiplier = 4
note_repetition_threshold = 1
# MIDI settings
controller_delta = 64
attrib_coef = (127 - controller_delta) / brightness_threshold
lfo_id = 26
cutoff_id = 43
lfo_coef = 2
lfo_speed_id = 24
# Other settings
debug = False
old_notes = {}
note_counter={}
midi_devices = mido.get_output_names()
if len(midi_devices) == 1:
midi_device = midi_devices[0]
else:
print('Available MIDI output devices:')
for item in midi_devices:
print(midi_devices.index(item), item)
device_number = int(
input('Select MIDI output device: ')
)
midi_device = midi_devices[device_number]
print('Using MIDI device "' + midi_device + '"')
port = mido.open_output(midi_device)
def show_rgb_equalized(image):
channels = cv2.split(image)
eq_channels = []
for ch, color in zip(channels, ['B', 'G', 'R']):
eq_channels.append(cv2.equalizeHist(ch))
eq_image = cv2.merge(eq_channels)
# eq_image = cv2.cvtColor(eq_image, cv2.COLOR_BGR2RGB)
#return image
return eq_image
def process_notes(new_notes):
global old_notes
#if old_notes.keys() != new_notes.keys():
print(new_notes.keys())
for note, colors in new_notes.items():
if note_counter.get(note) is None:
note_counter[note] = 0
note_counter[note]=note_counter[note]+1;
if note_counter[note]<=note_repetition_threshold:
continue;
note_counter[note]=0
if (note not in old_notes):
note_normalized = int(
note_min + (note / image.shape[1]) * (note_max - note_min)
)
# note = note * note_multiplier + delta_note
note = note_normalized
print(note)
message = mido.Message('note_on', channel=1, note=note, velocity=127)
port.send(message)
controller_value = int(colors[0] * attrib_coef) + controller_delta
current_control = cutoff_id
message = mido.Message('control_change', channel=1, control=current_control, value=controller_value)
port.send(message)
controller_value = int(colors[1] * attrib_coef) + controller_delta
controller_value = int(controller_value/lfo_coef)
message = mido.Message('control_change', channel=1, control=lfo_id, value=controller_value)
port.send(message)
controller_value = int(colors[2] * attrib_coef) + controller_delta
controller_value = int(controller_value)
message = mido.Message('control_change', channel=1, control=lfo_id, value=controller_value)
port.send(message)
time.sleep(0.150)
for note, colors in new_notes.items():
if 0 <= note <= 127:
message = mido.Message('note_off', channel=1, note=note, velocity=127)
port.send(message)
old_notes = new_notes
while True:
# Capture image
retval, image = camera.read()
resized_dimendions = (
int(image.shape[0] * resize_multiplier),
int(image.shape[1] * resize_multiplier)
)
if debug:
cv2.imwrite('image01_original.png', image)
# Resize image
image = cv2.resize(image, resized_dimendions)
if debug:
cv2.imwrite('image02_resized.png', image)
# Get a few pixel strips from the image for display on a screen
image_part = []
for i in range(1,max_display_rows):
image_part.append(image[i])
# image = numpy.array([image[0]])
# Normalize lighting
image = numpy.array(image_part)
image = show_rgb_equalized(image)
if debug:
cv2.imwrite('image03_slice.png', image)
# Display image
# cv2.imshow('image',image)
# cv2.waitKey(1)
hor_width = len(image[0])
min_x = int(hor_width * skip_img_left / 100)
max_x = int(hor_width - hor_width * skip_img_right / 100)
# Show red rulers - notes are detected only between them
image[0:max_display_rows, min_x] = (0,0,255)
image[0:max_display_rows, max_x] = (0,0,255)
# Highlight the line below the reading line
image[1, 0:hor_width] = (0,255,0)
# Full screen
cv2.namedWindow("window", cv2.WND_PROP_FULLSCREEN)
cv2.setWindowProperty("window",cv2.WND_PROP_FULLSCREEN,cv2.WINDOW_FULLSCREEN)
cv2.imshow("window", image)
cv2.waitKey(1)
# Get a single row
image = numpy.array([image[0]])
# Print strip as numbers
notes_detected = {}
for number, colors in enumerate(image[0]):
if number >= min_x and \
number <= max_x and \
image[0][number][0] < brightness_threshold and \
image[0][number][1] < brightness_threshold and \
image[0][number][2] < brightness_threshold:
notes_detected[number] = (
image[0][number][0],
image[0][number][1],
image[0][number][2]
)
process_notes(notes_detected)
# Wait until next note has arrived
time.sleep(capture_interval)
# cv2.imshow('test',image)
# cv2.waitKey(0)