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Diffstat (limited to 'random241sensor.py')
| -rwxr-xr-x | random241sensor.py | 186 |
1 files changed, 0 insertions, 186 deletions
diff --git a/random241sensor.py b/random241sensor.py deleted file mode 100755 index e9525e4..0000000 --- a/random241sensor.py +++ /dev/null @@ -1,186 +0,0 @@ -#!/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 = 15.0 -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 |