def adjust_image(filename, debug):
image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
if image.shape[1] > image.shape[0]:
image = numpy.rot90(image, 3)
imblur = cv2.blur(image, (50, 50))
thresh, imthresh = cv2.threshold(imblur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contours, heirarchy = cv2.findContours(imthresh, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
cont_im = image.copy()
cv2.drawContours(cont_im, contours, -1, 255, 3)
if debug == 2:
opencv_fft.dbg_save("/tmp/bw.png", image)
opencv_fft.dbg_save("/tmp/blur.png", imblur)
opencv_fft.dbg_save("/tmp/thresh.png", imthresh)
opencv_fft.dbg_save("/tmp/contour.png", cont_im)
return image, imblur, thresh, imthresh, contours, heirarchy, cont_im
def adjust_image(filename,debug):
image = cv2.imread(filename,cv2.IMREAD_GRAYSCALE)
if image.shape[1] > image.shape[0]:
image = numpy.rot90(image,3)
imblur = cv2.blur(image,(50,50))
thresh, imthresh = cv2.threshold(imblur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
contours, heirarchy = cv2.findContours(imthresh,cv2.RETR_LIST,cv2.CHAIN_APPROX_SIMPLE)
cont_im = image.copy()
cv2.drawContours(cont_im,contours,-1,255,3)
if debug == 2:
opencv_fft.dbg_save('/tmp/bw.png',image)
opencv_fft.dbg_save('/tmp/blur.png',imblur)
opencv_fft.dbg_save('/tmp/thresh.png',imthresh)
opencv_fft.dbg_save('/tmp/contour.png',cont_im)
return image, imblur,thresh,imthresh,contours,heirarchy,cont_im
def box_light_fft(filename, debug):
# Form Contours of lights to be encompassed
image, imblur, thresh, imthresh, contours, heirarchy, cont_im = adjust_image(filename, debug)
center_im = image.copy()
centers = []
radii = []
# compute boxes around found transmitters using Bounding Box method
rects, boxs, centers, number_of_transmitters = find_first_transmitters(contours)
if debug == 2:
box_im = image.copy()
for box in boxs:
cv2.drawContours(box_im, [box], 0, (255, 255, 0), 10)
opencv_fft.dbg_save("/tmp/box.png", box_im)
if number_of_transmitters == 0:
return exit_box_algorithm(image.shape)
estimated_frequencies = []
windowed_size = 100
average_window = 50
avg_threshold = 2 # what the hell are these
expanded_trans_list = []
final_trans_list = []
new_centers = []
new_boxes = []
# expand list of transmitters, breaking blobs of light using center line
for i in xrange(number_of_transmitters):
slope1, slope2 = find_slope(boxs, i)
image_col = image[:, centers[i][1]]
avg_threshold = []
top_boundary, bottom_boundary = find_horizontal_bound(
image_col, centers, i, average_window, avg_threshold, image
)
if top_boundary == 1:
top_boundary = boxs[i][2][1]
bottom_boundary = boxs[i][0][1]
# Run Check for angle of box
if abs(slope1) < 0.2: # i can move this figure after more testing
expanded_trans_list, new_centers, new_boxes = side_fft(
expanded_trans_list,
new_centers,
new_boxes,
centers,
i,
top_boundary,
bottom_boundary,
image,
average_window,
boxs,
imthresh,
)
print("Sideways box")
continue
avg_threshold = 2 # can play!
layers = add_layers(centers[i], top_boundary, bottom_boundary)
temp_trans_list = []
cord = []
# Find frequencies along layered points of the transmitter in question
for k in range(len(layers)):
point = layers[k]
image_row = image[point[0]]
left_boundary, right_boundary = find_vertical_bound(
image_row, point, i, average_window, avg_threshold, image
)
j = left_boundary
span = 100
freq_list = []
freq_list = freq_in_row(j, span, image_row, left_boundary, right_boundary, freq_list)
val = len(expanded_trans_list)
expanded_trans_list, cord = seperate_transmitters(
freq_list, expanded_trans_list, left_boundary, point, i, cord, k
)
best_diff = 100000
best_b = 0
b = []
# if there were two boxes found, Split box in two. otherwise keep original.
if len(cord) >= 3:
b, best_b = find_intercept(slope1, slope2, cord, layers, boxs, best_diff, i)
top_cord, bottom_cord = find_box_intercepts(slope1, slope2, b[best_b], boxs, i)
box1 = create_box(bottom_cord, boxs[i][1], boxs[i][2], top_cord)
box2 = create_box(boxs[i][0], bottom_cord, top_cord, boxs[i][3])
# create new algorithm for center of mass calculation. what type of box am i
new_boxes.append(box1)
new_boxes.append(box2)
slidx1, slidy1, slidx2, slidy2 = shift_out(box1, box2, slope2)
slidx3, slidy3 = shift_vert(boxs, slope1, i)
mid_topx, mid_topy, mid_botx, mid_boty = find_box_mids(boxs[i])
sign = find_surroundings(mid_topx, mid_topy, mid_botx, mid_boty, imthresh, image, slope1)
# modify the individual box centers taking account of surroundings
add_new_centers(new_centers, slidx1, slidy1, slidx2, slidy2, slidx3, slidy3, box1, box2, sign)
else:
# If single box, add original center and box
new_boxes.append(boxs[i])
new_centers.append(centers[i])
print("New Centers: " + str(new_centers))
finbox_im = image.copy()
if True:
for box in new_boxes:
box = numpy.array(box)
cv2.drawContours(finbox_im, [box], 0, (255, 255, 0), 10)
cv2.imwrite("/home/noah/lab/final_box.png", finbox_im)
opencv_fft.dbg_save("/temp/final_box.png", finbox_im)
freqs = []
new_trans_list = []
# Edit the bounds of each center found, for 2nd fft run
for i in range(len(new_centers)): # this could be made more accurate
expanded_trans_list[i].x = new_centers[i][1]
expanded_trans_list[i].y = new_centers[i][0]
edit_light_bounds(expanded_trans_list[i], new_centers[i])
final_trans_list = []
# Determine Frequencies of all Transmitters
spot = 0
for i in xrange(len(expanded_trans_list)):
image_row = image[expanded_trans_list[i].y]
if expanded_trans_list[i].right - expanded_trans_list[i].left > 100:
y = image_row[expanded_trans_list[i].left + 50 : expanded_trans_list[i].right]
else:
y = image_row[expanded_trans_list[i].left : expanded_trans_list[i].right]
continue
peak_freq = find_peak(y)
flag = False
noise = 300 # 300 is very noise, can i tone this down?
# erase transmitters if same frequency as another
for j in xrange(len(estimated_frequencies)):
if estimated_frequencies[j] < peak_freq + noise and estimated_frequencies[j] > peak_freq - noise:
print("Multiple signals with same frequency")
flag = True
if flag == False:
final_trans_list.append(expanded_trans_list[i])
estimated_frequencies.append(peak_freq)
spot = spot + 1
if len(final_trans_list) < 3:
return exit_box_algorithm(image.shape)
print("Estimated Frequencies: " + str(estimated_frequencies))
center_list = []
radii_list = []
for i in xrange(len(final_trans_list)):
point = []
point.append(final_trans_list[i].y)
point.append(final_trans_list[i].x)
center_list.append(point)
# the radii list is meaningless right now. what is it used for?
radii_list.append((final_trans_list[i].right - final_trans_list[i].left) / 2)
circle_im = finbox_im.copy()
for i in xrange(len(final_trans_list)):
cv2.circle(circle_im, (final_trans_list[i].x, final_trans_list[i].y), 10, (255, 255, 0), -1, 8)
opencv_fft.dbg_save("/home/noah/lab/circle.png", circle_im)
centers = numpy.array(center_list)
radii = numpy.array(radii_list)
estimated_frequencies = numpy.array(estimated_frequencies)
return (centers, radii, estimated_frequencies, image.shape, True)
def box_light_fft(filename,debug):
#Form Contours of lights to be encompassed
image, imblur,thresh,imthresh,contours,heirarchy,cont_im = adjust_image(filename,debug)
center_im = image.copy()
centers = []
radii = []
#compute boxes around found transmitters using Bounding Box method
rects ,boxs, centers, number_of_transmitters = find_first_transmitters(contours)
if debug == 2:
box_im = image.copy()
for box in boxs:
cv2.drawContours(box_im,[box],0,(255,255,0),10)
opencv_fft.dbg_save('/tmp/box.png',box_im)
if number_of_transmitters == 0:
return exit_box_algorithm(image.shape)
estimated_frequencies = []
windowed_size = 100
average_window = 50
avg_threshold = 2 #what the hell are these
expanded_trans_list = []
final_trans_list = []
new_centers = []
new_boxes = []
#expand list of transmitters, breaking blobs of light using center line
for i in xrange(number_of_transmitters):
slope1,slope2 = find_slope(boxs,i)
image_col = image[:,centers[i][1]]
avg_threshold = []
top_boundary, bottom_boundary = find_horizontal_bound(image_col,centers,i,average_window,avg_threshold,image)
if top_boundary == 1:
top_boundary = boxs[i][2][1]
bottom_boundary = boxs[i][0][1]
#Run Check for angle of box
if abs(slope1) < .2:#i can move this figure after more testing
expanded_trans_list, new_centers, new_boxes = side_fft(expanded_trans_list,new_centers,new_boxes, centers,i,top_boundary,bottom_boundary,image,average_window,boxs,imthresh)
print ("Sideways box")
continue
avg_threshold = 2#can play!
layers = add_layers(centers[i],top_boundary,bottom_boundary)
temp_trans_list = []
cord = []
#Find frequencies along layered points of the transmitter in question
for k in range(len(layers)):
point = layers[k]
image_row = image[point[0]]
left_boundary, right_boundary = find_vertical_bound(image_row,point,i,average_window,avg_threshold,image)
j = left_boundary
span = 100
freq_list = []
freq_list = freq_in_row(j,span,image_row,left_boundary,right_boundary,freq_list)
val = len(expanded_trans_list)
expanded_trans_list,cord = seperate_transmitters(freq_list,expanded_trans_list,left_boundary,point,i,cord,k)
best_diff = 100000
best_b = 0
b = []
#if there were two boxes found, Split box in two. otherwise keep original.
if len(cord) >= 3:
b, best_b = find_intercept(slope1,slope2,cord,layers,boxs,best_diff,i)
top_cord, bottom_cord = find_box_intercepts(slope1,slope2,b[best_b],boxs,i)
box1 = create_box(bottom_cord,boxs[i][1],boxs[i][2],top_cord)
box2 = create_box(boxs[i][0],bottom_cord,top_cord,boxs[i][3])
#create new algorithm for center of mass calculation. what type of box am i
new_boxes.append(box1)
new_boxes.append(box2)
slidx1,slidy1,slidx2,slidy2 = shift_out(box1,box2,slope2)
slidx3, slidy3 = shift_vert(boxs,slope1,i)
mid_topx,mid_topy,mid_botx,mid_boty = find_box_mids(boxs[i])
sign = find_surroundings(mid_topx,mid_topy,mid_botx,mid_boty,imthresh,image,slope1)
#modify the individual box centers taking account of surroundings
add_new_centers(new_centers,slidx1,slidy1,slidx2,slidy2,slidx3,slidy3,box1,box2,sign)
else:
#If single box, add original center and box
new_boxes.append(boxs[i])
new_centers.append(centers[i])
print ("New Centers: " + str(new_centers))
finbox_im = image.copy()
if True:
for box in new_boxes:
box = numpy.array(box)
cv2.drawContours(finbox_im,[box],0,(255,255,0),10)
cv2.imwrite('/home/noah/lab/final_box.png',finbox_im)
opencv_fft.dbg_save('/temp/final_box.png',finbox_im)
freqs = []
new_trans_list = []
#Edit the bounds of each center found, for 2nd fft run
for i in range(len(new_centers)):#this could be made more accurate
expanded_trans_list[i].x = new_centers[i][1]
expanded_trans_list[i].y = new_centers[i][0]
edit_light_bounds(expanded_trans_list[i],new_centers[i])
final_trans_list = []
#Determine Frequencies of all Transmitters
spot = 0
for i in xrange(len(expanded_trans_list)):
image_row = image[expanded_trans_list[i].y]
if expanded_trans_list[i].right - expanded_trans_list[i].left > 100:
y = image_row[expanded_trans_list[i].left + 50:expanded_trans_list[i].right]
else:
y = image_row[expanded_trans_list[i].left:expanded_trans_list[i].right]
continue
peak_freq = find_peak(y)
flag = False
noise = 300 #300 is very noise, can i tone this down?
#erase transmitters if same frequency as another
for j in xrange(len(estimated_frequencies)):
if estimated_frequencies[j] < peak_freq + noise and estimated_frequencies[j] > peak_freq - noise:
print ("Multiple signals with same frequency")
flag = True
if flag == False:
final_trans_list.append(expanded_trans_list[i])
estimated_frequencies.append(peak_freq)
spot = spot + 1
if len(final_trans_list) < 3:
return exit_box_algorithm(image.shape)
print ("Estimated Frequencies: " + str(estimated_frequencies))
center_list = []
radii_list = []
for i in xrange(len(final_trans_list)):
point= []
point.append(final_trans_list[i].y)
point.append(final_trans_list[i].x)
center_list.append(point)
#the radii list is meaningless right now. what is it used for?
radii_list.append((final_trans_list[i].right - final_trans_list[i].left)/2)
circle_im = finbox_im.copy()
for i in xrange(len(final_trans_list)):
cv2.circle(circle_im, (final_trans_list[i].x,final_trans_list[i].y),10,(255,255,0),-1,8)
opencv_fft.dbg_save('/home/noah/lab/circle.png',circle_im)
centers = numpy.array(center_list)
radii = numpy.array(radii_list)
estimated_frequencies = numpy.array(estimated_frequencies)
return (centers,radii,estimated_frequencies, image.shape,True)
