From 29d861e10548e25339e24cfd2db1e3a9fc748e41 Mon Sep 17 00:00:00 2001 From: David Runge Date: Sat, 15 Feb 2014 01:32:28 +0100 Subject: Adding random241*.py files to subdirectory --- entropy_harvester/random241.py | 68 +++++++++++++ entropy_harvester/random241arg.py | 25 +++++ entropy_harvester/random241osc.py | 32 ++++++ entropy_harvester/random241sensor.py | 186 +++++++++++++++++++++++++++++++++++ 4 files changed, 311 insertions(+) create mode 100755 entropy_harvester/random241.py create mode 100755 entropy_harvester/random241arg.py create mode 100755 entropy_harvester/random241osc.py create mode 100755 entropy_harvester/random241sensor.py (limited to 'entropy_harvester') diff --git a/entropy_harvester/random241.py b/entropy_harvester/random241.py new file mode 100755 index 0000000..f5268d5 --- /dev/null +++ b/entropy_harvester/random241.py @@ -0,0 +1,68 @@ +#!/usr/bin/python2 + +import cv +import time +import numpy as np +import logging +import random241arg as arg +import random241sensor as sensor +import random241osc as osc + +showStream = False +stop_key = 0 +capture = True +camNumber = 1 +time_delta = 60 * 60 / 2 +logging.basicConfig(filename='random241.log', + format='%(asctime)s %(message)s', + filemode='w', level=logging.INFO) +last_time = time.time() + +# Read parameters +params = arg.read_params() +# Define which cam to use, etc. +#cam = sensor.capture(params['-c'], showStream) + +# Get frame and size information from camera +cam = cv.CaptureFromCAM(camNumber) +frame = cv.QueryFrame(cam) +if frame is not None: + frame_size = cv.GetSize(frame) + logging.info('Grabbing random numbers from: %dx%dpx.', + frame_size[0], frame_size[1]) + + # Get matrix with values from frame + mat = cv.GetMat(frame) + frame_values = np.asarray(mat) + # Create grayscale image + gray_values = sensor.bgr2gray(frame_values) + + # Define the time to run the test + time_delta = time_delta + time.time() + + # Setup OSC + #osc.connect_to_server('beagleclone', 57120) + osc.connect_to_server('127.0.0.1', 57120) + + # Main loop for accessing the camera and calculating random numbers from it + #while True: + while time_delta > time.time(): +# last_time = time.time() + img = cv.QueryFrame(cam) + # Get a numpy array with rgb values + mat_from_frame = sensor.frame_to_mat(img) + gray_mat = sensor.bgr2gray(mat_from_frame) + randomness = sensor.harvest_entropy(gray_mat) + if randomness is not None: + delta = time.time() - last_time + last_time = time.time() + osc.send_msg(delta, randomness) + if showStream: + while stop_key != ord("q"): + cv.ShowImage("Americium 241", img) + key = cv.WaitKey(2) + + #time.sleep(5) + #random241sensor.set_capture(False) +else: + logging.error('Connect camera %d first!', camNumber) diff --git a/entropy_harvester/random241arg.py b/entropy_harvester/random241arg.py new file mode 100755 index 0000000..4bd00d8 --- /dev/null +++ b/entropy_harvester/random241arg.py @@ -0,0 +1,25 @@ +#!/usr/bin/python2 + +from sys import argv + + +def read_params(): + # Checks the given parameters + info = """Use the program as following: + random241.py [option] ... + Options: + -c number of cam to use (starts with first found camera 0 (default)) + -r remote address to send the output to (standard 192.168.0.7) + """ + default = {'-c': "1", '-r': "192.168.0.7"} + parameters = default.copy() + if len(argv) != 3: + for i in xrange(1, len(argv) - 1, 2): + if argv[i] in parameters: + parameters[argv[i]] = argv[i + 1] + else: + print info + return 0 + return parameters + else: + return default diff --git a/entropy_harvester/random241osc.py b/entropy_harvester/random241osc.py new file mode 100755 index 0000000..1e06917 --- /dev/null +++ b/entropy_harvester/random241osc.py @@ -0,0 +1,32 @@ +#import OSC as osc +import liblo as osc +import logging + +# Declare an empty target +target = None + + +# Connect to the server +def connect_to_server(hostname, port): + global target + if (hostname or port) is None: + target = osc.Adress('127.0.0.1', 57121, osc.UDP) + else: + try: + target = osc.Address(hostname, port, osc.UDP) + except osc.AddressError, err: + logging.error(err) + + +# Send a osc_message to the server +def send_msg(time_delta, randomness): + global target + # if the message is not empty and longer than 1 + if randomness is not None and len(randomness) > 1: + msg = osc.Message("/random") + msg.add(time_delta) + msg.add(randomness[0], randomness[1]) + try: + osc.send(target, msg) + except: + logging.error('OSC: Sending of message failed.') diff --git a/entropy_harvester/random241sensor.py b/entropy_harvester/random241sensor.py new file mode 100755 index 0000000..93b7276 --- /dev/null +++ b/entropy_harvester/random241sensor.py @@ -0,0 +1,186 @@ +#!/usr/bin/python2 + +import logging +import cv +import numpy as np +import time + +# Bool to define wether to capture the cam or not +capture = True +# Bool to define wether to show the capture stream or not +showStream = True +white_threshold = 17.0 +checked = np.zeros((1, 1), dtype=np.int) +mat = np.zeros((1, 1)) +clusters = [] +balances = [] + + +def capture(camNumber, showStream): + # Open stream for that camera + logging.info('Capture from camera #%d', camNumber) + cam = cv.CaptureFromCAM(int(camNumber)) + # Stream to output window as long as it is active + return cam + while capture: + stream = cv.QueryFrame(cam) + if showStream: + cv.ShowImage("Americium 241", stream) + + +def set_capture(onOrOff): + if onOrOff == bool: + global capture + capture = onOrOff + + +def frame_to_mat(img): + cv.Smooth(img, img, cv.CV_GAUSSIAN, 3, 0) + mat = cv.GetMat(img) + frame_values = np.asarray(mat) + return frame_values + + +# Convert a bgr matrix to grayscale +def bgr2gray(mat): + b, g, r = mat[:, :, 0], mat[:, :, 1], mat[:, :, 2] + gray = 0.1140 * b + 0.5870 * g + 0.2989 * r + return gray + + +# Find a white dot in the black input matrix +def harvest_entropy(mat_input): + global mat + global checked + global clusters + global balances + mat = mat_input.copy() + if np.ndim(mat) >= 2: + # Create array to hold the already checked pixels + checked = np.zeros((len(mat), len(mat[0])), dtype=np.int) + # Traverse the grayscale values in search of a bright pixel + for i in range(0, len(mat) - 1): + for j in range(0, len(mat[0]) - 1): + # Check if it hasn't been checked yet + if (checked[i][j] != 1): + # Find clusters, if the pixel is above threshold + if (mat[i][j] >= white_threshold): + #print "Hit above white threshold" + # Add a new cluster to the list of clusters + cluster = [] + clusters.append(cluster) + # Find the rest of the cluster + find_cluster(i, j) + #print "Number at: %dx%dpx : %s" % (j, i, mat[i][j]) + checked[i][j] = 1 + # If there's one or more clusters, calculate its or their balance point + if len(clusters) > 0: + balance_point = cluster_to_balance_point() + logging.info('%s, %s', balance_point[1], balance_point[0]) + #print balance_point + # Empty the global clusters variable again + del clusters[:] + balances.append([time.time(), balance_point]) + mean = mean_balances() + logging.info('%s, %s (balance mean)', mean[1], mean[0]) + floats = coordinate_to_float(balance_point[0], balance_point[1]) + logging.info('%s, %s (float)', floats[1], floats[0]) + #return balance_point + return floats + else: + logging.error('Input matrix has wrong dimension!') + + +# Find cluster around a non-black pixel +def find_cluster(x, y): + global checked + global mat + global clusters + # Append the current white dot to the last cluster + dot = np.array([x, y, mat[x][y]]) + clusters[len(clusters) - 1].append(dot) + # Search for surrounding white dots now + # Search one pixel further right + if (len(mat) - 1 >= (x + 1)) and (mat[x + 1][y] >= white_threshold) \ + and (checked[x + 1][y] != 1): + find_cluster(x + 1, y) + # Search one pixel further right and down + if (len(mat) - 1 >= (x + 1)) and (len(mat[0]) - 1 >= y + 1) and \ + (mat[x + 1][y + 1] >= white_threshold) \ + and (checked[x + 1][y] != 1): + find_cluster(x + 1, y + 1) + # Search one pixel further down + if (len(mat[0]) - 1 >= y + 1) and \ + (mat[x][y + 1] >= white_threshold) and (checked[x][y + 1] != 1): + find_cluster(x, y + 1) + # Search one pixel further down and further left + if (len(mat[0]) - 1 >= y + 1) and x - 1 >= 0 \ + and (mat[x - 1][y + 1] >= white_threshold) \ + and (checked[x - 1][y + 1] != 1): + find_cluster(x - 1, y + 1) + # Add this pixel to the list of checked pixels + checked[x][y] = 1 + + +# Create balance point from cluster +# TODO: Make possible to choose only most significant cluster +def cluster_to_balance_point(): + global clusters + cluster_balances = [] + x_balance = 0.0 + y_balance = 0.0 + for cluster in clusters: + mean_x = 0.0 + mean_y = 0.0 + sum_total = 0.0 + for dot in cluster: + # Calculate X balance (x * intensity) + mean_x = mean_x + dot[0] * dot[2] + # Calculate Y balance (y * intensity) + mean_y = mean_y + dot[1] * dot[2] + # Calculate Y total (all intensity summed up) + sum_total = sum_total + dot[2] + # Add up the balances and put them into a list + cluster_x_balance = mean_x / sum_total + cluster_y_balance = mean_y / sum_total + cluster_balances.append([cluster_x_balance, cluster_y_balance]) + # If it's more than one cluster, balance between them + if len(cluster_balances) > 1: + logging.info('Balancing between a couple of clusters.') + total_cluster_x_balance = 0.0 + total_cluster_y_balance = 0.0 + for balance in cluster_balances: + total_cluster_x_balance = total_cluster_x_balance + balance[0] + total_cluster_y_balance = total_cluster_y_balance + balance[1] + x_balance = total_cluster_x_balance / float(len(cluster_balances)) + y_balance = total_cluster_y_balance / float(len(cluster_balances)) + else: + logging.info('Balancing between one cluster.') + x_balance = cluster_x_balance + y_balance = cluster_y_balance + return [x_balance, y_balance] + + +# Displays the mean balance calculated from all balances +def mean_balances(): + global balances + mean_balance = [0.0, 0.0] + for balance in balances: + mean_balance[0] = mean_balance[0] + balance[1][0] + mean_balance[1] = mean_balance[1] + balance[1][1] + mean_balance[0] = mean_balance[0] / float(len(balances)) + mean_balance[1] = mean_balance[1] / float(len(balances)) + return mean_balance + + +# Calculates float value between 0.0 and 1.0 from coordinate +# TODO: insert on-the-fly mean_balance as parameter +def coordinate_to_float(x, y): + global mat + width = float(len(mat)) + height = float(len(mat[0])) +# balance_dim = [width / 2, height / 2] + floatx = x / width + floaty = y / height + return [floatx, floaty] +# TODO: Function to calculate floats from mean_balance on the fly -- cgit v1.2.3-70-g09d2