def scaled_img_to_graph(scaled_image, scaled_grad_image, scaled_dup_image, height, width, calc_weight, processes, weight_threshold=WEIGHT_THRESHOLD):
size = height * width
graph = Graph(size)
_add_edge = graph.add_edge
range_size = range(size)
scaled_vers = list()
img_pixels = list()
grad_img_pixels = list()
dup_img_pixels = list()
for p in range_size:
x, y = get_coords_for_pixel(p, width)
scaled_vers.append((scale(x, height), scale(y, width)))
img_pixels.append(scaled_image[x, y])
grad_img_pixels.append(scaled_grad_image[x, y])
dup_img_pixels.append(scaled_dup_image[x, y])
graph.set_diag([1. for i in range_size])
times = 30
new_size = size / times
for main_part in range(times):
new_begin = main_part * new_size
job_server = Server(ncpus=processes)
jobs = []
start_range = 0
step = new_size / processes
for end_range in range(step, new_size + step, step):
end_range = min(end_range, new_size)
jobs.append(job_server.submit(calc_weights, (calc_weight, range(new_begin + start_range, new_begin + end_range), scaled_vers, img_pixels, grad_img_pixels, dup_img_pixels, weight_threshold), modules=('math',), depfuncs=(calc_intensity_weight, calc_weight_for_test, calc_texture_weight, calc_duplicate_weight)))
start_range = end_range
while len(jobs):
for job in jobs:
if job.finished:
edges = job()
if edges is None:
job_server.destroy()
logger.log('Couldn\'t complete one of the jobs', True)
return None
for pixel1, pixel2, weight in edges:
_add_edge(pixel1, pixel2, weight)
jobs.remove(job)
del edges
collect()
job_server.destroy()
graph.ready()
return graph
def compute_dup_img(image, height, width, processes):
result = zeros((height, width), dtype=tuple)
pixels = list()
row_cols = list()
for row in range(height):
for col in range(width):
pixel = list()
row_cols.append((row, col))
for krow in (-2, -1, 0, 1, 2):
for kcol in (-2, -1, 0, 1, 2):
x = row - krow
y = col - kcol
if x >= 0 and x < height and y >= 0 and y < width:
pixel.append((int(image[x, y][0]), int(image[x, y][1]), int(image[x, y][2])))
else:
pixel.append((0, 0, 0))
pixels.append(pixel)
job_server = Server(ncpus=processes)
jobs = []
start_range = 0
step = len(pixels) / processes
for end_range in range(step, len(pixels) + step, step):
end_range = min(end_range, len(pixels))
jobs.append(job_server.submit(compute_dup_for_lines, (range(start_range, end_range), pixels, row_cols)))
start_range = end_range
while len(jobs):
for job in jobs:
if job.finished:
dup_distanses = job()
if dup_distanses is None:
job_server.destroy()
logger.log('Couldn\'t complete one of the jobs', True)
return None
for row, col, value in dup_distanses:
result[row, col] = (float32(value), float32(value), float32(value))
jobs.remove(job)
return result
