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mask.py
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mask.py
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#-------------------------------------------------------------------------------
# Name: mask.py
# Purpose: This file supports quality assurance for sensor frames.
#
# Author: Roman Graf
#
# Created: 18.09.2014
# Copyright: (c) GrafR 2014
# Licence: Apache 2.0
#-------------------------------------------------------------------------------
#!/usr/bin/env python
"""
========================
Sensor Live Demo
========================
"""
import numpy
import ImageDraw
import ImageFont
import timeit
import math
import os, string
import subprocess
from config import *
##if WIN_OS not in OS: import compare_screenshots
# init all pixels array
all_pixels = numpy.ndarray((1600, 256))
clusters = []
areas = dict()
final_res_clusters = []
pixels = []
compare_count = 0
PIXEL_DISTANCE = PIXEL_DISTANCE1
LOG = LOG1
THUMBNAILS = THUMBNAILS1
Y_STEP = Y_STEP1
HORIZ_CLUSTER_FILTER_RATIO = HORIZ_CLUSTER_FILTER_RATIO1
FINAL_CLUSTER_DISTANCE_X = FINAL_CLUSTER_DISTANCE_X1
FINAL_CLUSTER_DISTANCE_Y = FINAL_CLUSTER_DISTANCE_Y1
OBJECT_DISTANCE = OBJECT_DISTANCE1
from Tkinter import *
import Image, ImageTk
import os, sys
import time
from multiprocessing import Process
class Viewer:
def __init__(self, master):
self.top = master
self.index = 0
self.is_auto_mode = True
# this variable contains previous image
self.prev_image = None
self.prev_image_title = None
print "viewer load ..."
filename = 'draw.png'
filename1 = 'imgdraw.png'
filename2 = 'mask_orig.png'
filename_final = 'final.png'
if not os.path.exists(filename):
print "Unable to find %s" % filename
self.top.quit()
self.final_image_title = Label(text=os.path.basename(filename_final))
self.final_image_title.pack()
im_final = Image.open(filename_final)
if RESIZE == 1: im_final = im_final.resize((X_FRAME, Y_FRAME), Image.ANTIALIAS)
if im_final.format == "SPIDER":
im_final = im_final.convert2byte()
self.final_image = ImageTk.PhotoImage(im_final)
self.lblfinal = Label(master, image=self.final_image)
self.lblfinal.pack(side='top')
self.title = Label(text=os.path.basename(filename))
self.title.pack()
im = Image.open(filename)
if RESIZE == 1: im = im.resize((X_FRAME, Y_FRAME), Image.ANTIALIAS)
if im.format == "SPIDER":
im = im.convert2byte()
self.size = im.size
self.tkimage = ImageTk.PhotoImage(im)
im2 = Image.open(filename2)
if RESIZE == 1: im2 = im2.resize((X_FRAME, Y_FRAME), Image.ANTIALIAS)
if im2.format == "SPIDER":
im2 = im2.convert2byte()
self.size = im2.size
self.prev_image = ImageTk.PhotoImage(im2)
self.lbl = Label(master, image=self.tkimage)
self.lbl.pack(side='top')
im1 = Image.open(filename1)
if RESIZE == 1: im1 = im1.resize((X_FRAME, Y_FRAME), Image.ANTIALIAS)
if im1.format == "SPIDER":
im1 = im1.convert2byte()
self.prev_image1_title = Label(text=os.path.basename(filename1))
self.prev_image1_title.pack()
self.prev_image1 = ImageTk.PhotoImage(im1)
self.lbl1 = Label(master, image=self.prev_image1)
self.lbl1.pack(side='top')
self.prev_image_title = Label(text=os.path.basename(filename2))
self.prev_image_title.pack()
self.lbl3 = Label(master, image=self.prev_image)
self.lbl3.pack(side='top')
# image doesn't appear unless put Image.open in separate function?
# and need to use tkimage.paste, not ImageTk.PhotoImage
def getImage(self, filename):
im = Image.open(filename)
if RESIZE == 1: im = im.resize((X_FRAME, Y_FRAME), Image.ANTIALIAS)
if im.format == "SPIDER":
im = im.convert2byte()
if im.size != self.size:
print "all images must be same dimensions:"
f1 = os.path.basename(self.files[0])
f2 = os.path.basename(filename)
print "%s: %s, %s : %s" % (f1, str(self.size),f2, str(im.size))
self.top.quit()
return im
########################################################
def run_command(command):
p = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
return iter(p.stdout.readline, b'')
def run_command_win(command):
output = subprocess.Popen(command,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT).communicate()
valid_lines = [ line for line in output[0].split('\r\n') ]
return valid_lines
def to_greyscale(arr):
if len(arr.shape) == 3:
return average(arr, -1)
else:
return arr
# This method compares two images employing PSNR metric and image magick tool
# note that the analyzed images must be of the same size
def compare_psnr(img_path_1, img_path_2, psnr_threshold=PSNR_THRESHOLD):
# print "\ncompare PSNR input1: ", img_path_1, ", input2: ", img_path_2
diff_img_name = 'difference.png'
similarity = ''
try:
if os.path.exists(img_path_1) and os.path.exists(img_path_2):
img1 = Image.open(img_path_1)
img2 = Image.open(img_path_2)
width1, height1 = img1.size
width2, height2 = img2.size
# adjust sizes if necessary
if width1 != width2 or height1 != height2:
# print "width1: ", width1, ", width2: ", width2, ", height1: ", height1, ", height2: ", height2
img2 = img2.resize((width1, height1), Image.ANTIALIAS)
global THUMBNAILS
if THUMBNAILS == 1: img2.thumbnail((36, 36))
img2.save(img_path_2)
if WIN_OS in OS:
for line in run_command_win(['compare', '-metric', METRIC, img_path_1, img_path_2, diff_img_name]):
similarity = line.strip()
break
else:
for line in run_command(['sudo', 'compare', '-metric', METRIC, img_path_1, img_path_2, diff_img_name]):
similarity = line.strip()
global LOG
if LOG == 1:
global compare_count
print "run compare command: ", compare_count
compare_count += 1
except Exception, e:
print "Error:", e, " Please check if image magick tool is installed and passed image paths are correct!"
psnr_msg = "DIFFERENT"
if 'INF' in similarity or float(similarity) >= psnr_threshold:
#print img_path_1, ' and ', img_path_2, ' are similar'
psnr_msg = "SIMILAR"
# print "psnr: ", similarity, ", msg: ", psnr_msg
return similarity, psnr_threshold, psnr_msg
def match_cluster(x, y, width, height, idx):
global clusters
global PIXEL_DISTANCE
global areas
x_min = x
y_min = y
x_max = x
y_max = y
is_merged = False
is_cluster_exist = False
areas_list = []
i = idx-AREA_RANGE
while i <= idx:
if i >= 0 and len(areas[i]) > 0:
areas_list.extend(areas[i])
i += 1
for cluster in areas_list:
# check if current pixel is outside of any existing cluster
if abs(x-cluster[LEFT_UP][X_AXIS]) >= PIXEL_DISTANCE and abs(x-cluster[RIGHT_DOWN][X_AXIS]) >= PIXEL_DISTANCE and abs(y-cluster[LEFT_UP][Y_AXIS]) >= PIXEL_DISTANCE and abs(y-cluster[RIGHT_DOWN][Y_AXIS]) >= PIXEL_DISTANCE:
continue
left_up = cluster[LEFT_UP]
right_down = cluster[RIGHT_DOWN]
# check if current pixel is already inside other cluster
if x >= left_up[X_AXIS] and x <= right_down[X_AXIS] and y >= left_up[Y_AXIS] and y <= right_down[Y_AXIS]:
is_cluster_exist = True
is_merged = True
break
# if current pixel is in the near of current cluster
diff_x_min = abs(x - left_up[X_AXIS])
diff_y_min = abs(y - left_up[Y_AXIS])
diff_x_max = abs(x - right_down[X_AXIS])
diff_y_max = abs(y - right_down[Y_AXIS])
if diff_x_min < PIXEL_DISTANCE and diff_x_min > 0 and x_min >=0 and left_up[X_AXIS] >= 0 and diff_y_min < PIXEL_DISTANCE:
if x_min > left_up[X_AXIS]: x_min = left_up[X_AXIS]
cluster[LEFT_UP][X_AXIS] = x_min
is_merged = True
if diff_y_min < PIXEL_DISTANCE and diff_y_min > 0 and y_min >=0 and left_up[Y_AXIS] >= 0 and diff_x_min < PIXEL_DISTANCE:
if y_min > left_up[Y_AXIS]: y_min = left_up[Y_AXIS]
cluster[LEFT_UP][Y_AXIS] = y_min
is_merged = True
# calculate max right point
if diff_x_max < PIXEL_DISTANCE and diff_x_max > 0 and x_max < width and right_down[X_AXIS] < width and diff_y_max < PIXEL_DISTANCE:
if x_max < right_down[X_AXIS]: x_max = right_down[X_AXIS]
cluster[RIGHT_DOWN][X_AXIS] = x_max
is_merged = True
if diff_y_max < PIXEL_DISTANCE and diff_y_max and y_max < height and right_down[Y_AXIS] < height and diff_x_max < PIXEL_DISTANCE:
if y_max < right_down[Y_AXIS]: y_max = right_down[Y_AXIS]
cluster[RIGHT_DOWN][Y_AXIS] = y_max
is_merged = True
return x_min, y_min, x_max, y_max, is_merged
def compare_to_prev_final_clusters_ext(merge, left_up_x, left_up_y, right_down_x, right_down_y):
if len(final_res_clusters) > 0:
global LOG
global FINAL_CLUSTER_DISTANCE_X
global FINAL_CLUSTER_DISTANCE_Y
prev_cluster = final_res_clusters[-1]
prev_left_up_x = prev_cluster[LEFT_UP][X_AXIS]
prev_left_up_y = prev_cluster[LEFT_UP][Y_AXIS]
prev_right_down_x = prev_cluster[RIGHT_DOWN][X_AXIS]
prev_right_down_y = prev_cluster[RIGHT_DOWN][Y_AXIS]
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("*** prev cluster: prev_lu_x=%d, prev_lu_y=%d, prev_rd_x=%d, prev_rd_y=%d\n"%(prev_left_up_x, prev_left_up_y, prev_right_down_x, prev_right_down_y))
x_left_diff = abs(left_up_x - prev_left_up_x)
x_right_diff = abs(right_down_x - prev_right_down_x)
y_diff = abs(prev_right_down_y - left_up_y)
y_up_diff = abs(left_up_y - prev_left_up_y)
y_down_diff = abs(right_down_y - prev_right_down_y)
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("x_left_diff=%d, x_right_diff=%d, y_diff=%d, y_up_diff=%d, y_down_diff=%d\n"%(x_left_diff, x_right_diff, y_diff, y_up_diff, y_down_diff))
if x_left_diff <= FINAL_CLUSTER_DISTANCE_X and x_right_diff <= FINAL_CLUSTER_DISTANCE_X:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("prev cluster distance ok - combine with new cluster\n")
# combine with prev cluster
if prev_left_up_x < left_up_x:
left_up_x = prev_left_up_x
if prev_right_down_x > right_down_x:
right_down_x = prev_right_down_x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
merge = True
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("check if cluster in prev cluster result: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
# if cluster is inside of previous cluster - merge
if left_up_x >= prev_left_up_x and prev_right_down_x >= right_down_x and left_up_y >= prev_left_up_y and prev_right_down_y >= right_down_y:
# combine with prev cluster
if prev_left_up_x < left_up_x:
left_up_x = prev_left_up_x
if prev_right_down_x > right_down_x:
right_down_x = prev_right_down_x
left_up_y = prev_left_up_y
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("inside cluster - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the right side - merge
if left_up_x >= prev_left_up_x and right_down_x >= prev_right_down_x and x_right_diff <= FINAL_CLUSTER_DISTANCE_X and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y and y_down_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
# right_down_x is larger then prev right down x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the right side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the upper side - merge
if left_up_x >= prev_left_up_x and right_down_x <= prev_right_down_x and right_down_y <= prev_right_down_y and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
right_down_x = prev_right_down_x
right_down_y = prev_right_down_y
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the upper side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the right up edge - merge
if left_up_x >= prev_left_up_x and right_down_y <= prev_right_down_y and x_right_diff <= FINAL_CLUSTER_DISTANCE_X and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
right_down_y = prev_right_down_y
if right_down_x < prev_right_down_x:
right_down_x = prev_right_down_x
if left_up_y > prev_left_up_y:
left_up_y = prev_left_up_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the right up edge - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the right down edge - merge
if left_up_x >= prev_left_up_x and left_up_y >= prev_left_up_y and x_right_diff <= FINAL_CLUSTER_DISTANCE_X and y_down_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
left_up_y = prev_left_up_y
if right_down_x < prev_right_down_x:
right_down_x = prev_right_down_x
if right_down_y < prev_right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the right down edge - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the down side - merge
if left_up_x >= prev_left_up_x and right_down_x <= prev_right_down_x and left_up_y >= prev_left_up_y and y_down_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
right_up_x = prev_right_down_x
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the down side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster in the middle on Y axis - merge
if left_up_x >= prev_left_up_x and right_down_x >= prev_right_down_x and left_up_y >= prev_left_up_y and left_up_y <= prev_right_down_y and right_down_y >= prev_left_up_y and right_down_y <= prev_right_down_y and x_right_diff <= FINAL_CLUSTER_DISTANCE_X:
# combine with prev cluster
if left_up_x > prev_left_up_x:
left_up_x = prev_left_up_x
if right_down_x < prev_right_down_x:
right_down_x = prev_right_down_x
left_up_y = prev_left_up_y
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster in the middle on Y axis - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster in the middle on Y axis with small overlapp on Y axis - merge
if left_up_x >= prev_left_up_x and right_down_x >= prev_right_down_x and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y and left_up_y <= prev_left_up_y and right_down_y >= prev_left_up_y and right_down_y <= prev_right_down_y and x_right_diff <= FINAL_CLUSTER_DISTANCE_X:
# combine with prev cluster
if left_up_x > prev_left_up_x:
left_up_x = prev_left_up_x
if right_down_x < prev_right_down_x:
right_down_x = prev_right_down_x
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster in the middle on Y axis with small overlapp on Y axis - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster in the middle on X axis - merge
if left_up_x >= prev_left_up_x and right_down_x <= prev_right_down_x and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y and right_down_y < prev_right_down_y:
# combine with prev cluster
left_up_x = prev_left_up_x
right_down_x = prev_right_down_x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster in the middle on X axis - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is almost inside of previous cluster on the left side - merge
if left_up_x >= prev_left_up_x and right_down_x >= prev_right_down_x and x_left_diff <= FINAL_CLUSTER_DISTANCE_X and y_up_diff <= FINAL_CLUSTER_DISTANCE_Y and y_down_diff <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
# right_down_x is larger then prev right down x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the left side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster is in the near to the right related to previous cluster - merge
global OBJECT_DISTANCE
if left_up_x >= prev_left_up_x and right_down_x >= prev_right_down_x and left_up_x >= prev_right_down_x and left_up_x - prev_right_down_x <= OBJECT_DISTANCE:
# combine with prev cluster
left_up_x = prev_left_up_x
# right_down_x is larger then prev right down x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the left side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
# if cluster has small overlapp on the right side from previous cluster - merge
if left_up_x <= prev_right_down_x and prev_right_down_x - left_up_x <= OBJECT_DISTANCE and abs(left_up_y - prev_left_up_y) <= FINAL_CLUSTER_DISTANCE_Y and abs(right_down_y - prev_right_down_y) <= FINAL_CLUSTER_DISTANCE_Y:
# combine with prev cluster
left_up_x = prev_left_up_x
# right_down_x is larger then prev right down x
if prev_left_up_y < left_up_y:
left_up_y = prev_left_up_y
if prev_right_down_y > right_down_y:
right_down_y = prev_right_down_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("almost inside cluster on the left side - combine with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge = True
return merge, left_up_x, left_up_y, right_down_x, right_down_y
def dump_clusters(name, clusters):
global LOG
for i, cluster in list(enumerate(clusters)):
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("dump %s[%d] cluster: [%d, %d], [%d, %d]\n"%(name, i, cluster[LEFT_UP][X_AXIS], cluster[LEFT_UP][Y_AXIS], cluster[RIGHT_DOWN][X_AXIS], cluster[RIGHT_DOWN][Y_AXIS]))
def calculate_final_clusters():
global clusters
global final_res_clusters
global LOG
idx = 0
final_idx = 0
while idx < len(clusters):
cluster = clusters[idx]
if LOG == 1: print "analyse res_cluster", cluster
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("\ncalculate_final_clusters: idx=%d, final_idx=%d\n"%(idx, final_idx))
final_idx, shift, agg_cluster = evaluate_cluster(cluster, idx, final_idx)
if agg_cluster != None:
# filter long horizontal clusters mostly representing borders
final_res_clusters.append(agg_cluster)
idx = shift
def calculate_final_clusters_v1(width):
global clusters
global final_res_clusters
global LOG
idx = 0
final_idx = 0
# separate frame into subframes (X axis) in order to increase performance
global areas
for cluster in clusters:
if LOG == 1: print "analyse res_cluster", cluster
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("\ncalculate_final_clusters: idx=%d, final_idx=%d\n"%(idx, final_idx))
final_area_idx = int(cluster[LEFT_UP][X_AXIS]/SUB_CLUSTER_SIZE)
final_idx, shift, agg_cluster = evaluate_cluster(cluster, final_area_idx, final_idx)
if agg_cluster != None:
# filter long horizontal clusters mostly representing borders
final_res_clusters.append(agg_cluster)
def create_final_cluster(left_up_x, left_up_y, right_down_x, right_down_y, final_idx):
init_cluster = None
global final_res_clusters
global MIN_FINAL_CLUSTER_SIZE
global LOG
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cfc: lu_x=%d; lu_y=%d; rd_x=%d; rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
text_file.write("cfc: difference on x between cur and agg clusters exceeds threshold\n")
if left_up_x >= 0 and left_up_y >= 0 and right_down_x - left_up_x >= MIN_FINAL_CLUSTER_SIZE and right_down_y - left_up_y >= MIN_FINAL_CLUSTER_SIZE:
merge = False
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cfc: the final cluster in not too small, check previous final clusters\n")
text_file.write("cfc: points before compare with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge, left_up_x, left_up_y, right_down_x, right_down_y = compare_to_prev_final_clusters_ext(merge, left_up_x, left_up_y, right_down_x, right_down_y)
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("result after compare with prev cluster: merge=%r, lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(merge, left_up_x, left_up_y, right_down_x, right_down_y))
agg_cluster = []
agg_cluster.append([left_up_x, left_up_y])
agg_cluster.append([right_down_x, right_down_y])
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cfc: final cluster size:%d\n"%(len(final_res_clusters)))
if merge == True:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("final_res_cluster[-1]\n")
final_res_clusters[-1] = agg_cluster
else:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cfc: init_cluster=agg_cluster\n")
init_cluster = agg_cluster
final_idx += 1
if LOG == 1:
with open("log.txt", "a") as text_file:
if merge == True:
text_file.write("merge with previous cluster\n")
text_file.write("cfc: [%d] #### create final cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(final_idx, left_up_x, left_up_y, right_down_x, right_down_y))
return init_cluster, final_idx
def evaluate_cluster(init_cluster, init_idx, final_idx):
global clusters
global PIXEL_DISTANCE
global LOG
global FINAL_CLUSTER_DISTANCE_X
global FINAL_CLUSTER_DISTANCE_Y
global MIN_FINAL_CLUSTER_SIZE
if LOG ==1: dump_clusters("final clusters", final_res_clusters)
left_up_x = init_cluster[LEFT_UP][X_AXIS]
left_up_y = init_cluster[LEFT_UP][Y_AXIS]
right_down_x = init_cluster[RIGHT_DOWN][X_AXIS]
right_down_y = init_cluster[RIGHT_DOWN][Y_AXIS]
idx = init_idx
for cluster in clusters[init_idx::]:
#print "analyse res_cluster", cluster
# read current cluster points
cur_left_up_x = cluster[LEFT_UP][X_AXIS]
cur_left_up_y = cluster[LEFT_UP][Y_AXIS]
cur_right_down_x = cluster[RIGHT_DOWN][X_AXIS]
cur_right_down_y = cluster[RIGHT_DOWN][Y_AXIS]
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("aggregation point: lu_x=%d; lu_y=%d; rd_x=%d; rd_y=%d;\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
text_file.write("[%d] current point: clu_x=%d; clu_y=%d; crd_x=%d; crd_y=%d\n"%(init_idx, cur_left_up_x, cur_left_up_y, cur_right_down_x, cur_right_down_y))
x_diff = abs(cur_left_up_x - left_up_x)
x_diff_end = abs(cur_right_down_x - right_down_x)
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("check that final cluster distance is not too large : x_diff=%d, x_diff_end=%d\n"%(x_diff, x_diff_end))
# remove too small clusters
if right_down_x - left_up_x <= MIN_FINAL_CLUSTER_SIZE and right_down_y - left_up_y <= MIN_FINAL_CLUSTER_SIZE:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("final cluster is too small\n")
init_cluster = None
idx += 1
break
# check that difference on X axis do not exceed threshold
if x_diff < FINAL_CLUSTER_DISTANCE_X and x_diff_end < FINAL_CLUSTER_DISTANCE_X:
# take minimal left x value and maximal right x value for the cluster
if cur_left_up_x < left_up_x:
left_up_x = cur_left_up_x
if cur_right_down_x > right_down_x:
right_down_x = cur_right_down_x
# if next cluster under the prev cluster - check that the distance not too big
if cur_left_up_y >= right_down_y:
if cur_left_up_y - right_down_y <= FINAL_CLUSTER_DISTANCE_Y:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("next cluster under the prev cluster distance ok, extend cluster on y axis\n")
# extend cluster in Y axis
right_down_y = cur_right_down_y
else: # upper side of the next cluster is inside of previous
# check if down side of the next cluster is lower then from prev cluster
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("upper side of the next cluster is inside of previous or the next cluster is to the right\n")
if cur_right_down_y >= right_down_y:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("down side of the next cluster is lower then from prev cluster\n")
right_down_y = cur_right_down_y
# current cluster is higher then aggregation cluster - set the highest left up point
if cur_left_up_y <= left_up_y and abs(left_up_y - cur_left_up_y) <= FINAL_CLUSTER_DISTANCE_Y: # check that y distance OK
left_up_y = cur_left_up_y
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cluster distance on x axis is in threshold - new points: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
# in the case of the last cluster - add new cluster
if cluster == clusters[-1]:
init_cluster, final_idx = create_final_cluster(left_up_x, left_up_y, right_down_x, right_down_y, final_idx)
else:
init_cluster = None # for inner cluster
else:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("difference on x between cur and agg clusters exceeds threshold\n")
if left_up_x >= 0 and left_up_y >= 0 and right_down_x - left_up_x >= MIN_FINAL_CLUSTER_SIZE and right_down_y - left_up_y >= MIN_FINAL_CLUSTER_SIZE:
merge = False
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("the final cluster in not too small, check previous final clusters\n")
text_file.write("points before compare with prev cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
merge, left_up_x, left_up_y, right_down_x, right_down_y = compare_to_prev_final_clusters_ext(merge, left_up_x, left_up_y, right_down_x, right_down_y)
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("result after compare with prev cluster: merge=%r, lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(merge, left_up_x, left_up_y, right_down_x, right_down_y))
agg_cluster = []
agg_cluster.append([left_up_x, left_up_y])
agg_cluster.append([right_down_x, right_down_y])
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("final cluster size:%d\n"%(len(final_res_clusters)))
if merge == True:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("final_res_cluster[-1]\n")
final_res_clusters[-1] = agg_cluster
else:
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("init_cluster=agg_cluster\n")
init_cluster = agg_cluster
final_idx += 1
if LOG == 1:
with open("log.txt", "a") as text_file:
if merge == True:
text_file.write("merge with previous cluster\n")
text_file.write("[%d] #### create final cluster: lu_x=%d, lu_y=%d, rd_x=%d, rd_y=%d\n"%(final_idx, left_up_x, left_up_y, right_down_x, right_down_y))
else:
init_cluster = None
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("## cluster not created: lu_x=%d, lu_y%d, rd_x=%d, rd_y=%d\n"%(left_up_x, left_up_y, right_down_x, right_down_y))
break
idx += 1
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("return final_idx=%d, idx=%d\n"%(final_idx, idx))
text_file.write("final cluster size at the end of loop: %d\n"%(len(final_res_clusters)))
return final_idx, idx, init_cluster
def get_edges(orig_img):
segmentation(orig_img, MASK_THRESHOLD)
root = Tk()
root.title('Live Demo Sensor ' + orig_img)
app = Viewer(root)
root.mainloop()
def segmentation(orig_img, mask_value=MASK_THRESHOLD, pixel_distance=PIXEL_DISTANCE1, cluster_distance_x=CLUSTER_DISTANCE_X,
cluster_distance_y=CLUSTER_DISTANCE_Y, final_cluster_distance_x=FINAL_CLUSTER_DISTANCE_X1,
final_cluster_distance_y=FINAL_CLUSTER_DISTANCE_Y1, min_cluster_size=MIN_CLUSTER_SIZE1, min_final_cluster_size=MIN_FINAL_CLUSTER_SIZE1,
horiz_cluster_filter_ratio=HORIZ_CLUSTER_FILTER_RATIO1, psnr=PSNR_THRESHOLD, x_step=X_STEP, y_step=Y_STEP1, search_offset=SEARCH_OFFSET,
debug_mode=LOG1, search_image_path=SEARCH_IMAGE_PATH, thumbnails=THUMBNAILS1, object_distance=OBJECT_DISTANCE1, filter_x=FILTER_X1, filter_y=FILTER_Y1):
try:
return segmentation_ext(orig_img, mask_value, pixel_distance, cluster_distance_x, cluster_distance_y, final_cluster_distance_x,
final_cluster_distance_y, min_cluster_size, min_final_cluster_size, horiz_cluster_filter_ratio,
psnr, x_step, y_step, search_offset, debug_mode, search_image_path, thumbnails, object_distance, filter_x, filter_y)
except Exception, e:
print "Unexpected error:", sys.exc_info()[0], e
if LOG == 1:
with open("log.txt", "a") as text_file:
text_file.write("cfc: error: %s\n"%(sys.exc_info()[0]))
time.sleep(1)
print "Image file is not completed. Try again."
segmentation(orig_img, mask_value, pixel_distance, cluster_distance_x, cluster_distance_y, final_cluster_distance_x,
final_cluster_distance_y, min_cluster_size, min_final_cluster_size, horiz_cluster_filter_ratio,
psnr, x_step, y_step, search_offset, debug_mode, search_image_path, thumbnails, object_distance)
def paint_rectangle_lines(img, cluster, color):
#print "res cluster: ", cluster
#with open("log.txt", "a") as text_file:
# text_file.write("cluster ID: idx=%d, cluster=%s\n"%(i, cluster))
img.line((cluster[LEFT_UP][X_AXIS], cluster[LEFT_UP][Y_AXIS], cluster[RIGHT_DOWN][X_AXIS], cluster[LEFT_UP][Y_AXIS]), color, width=3)
img.line((cluster[LEFT_UP][X_AXIS], cluster[LEFT_UP][Y_AXIS], cluster[LEFT_UP][X_AXIS], cluster[RIGHT_DOWN][Y_AXIS]), color, width=3)
img.line((cluster[RIGHT_DOWN][X_AXIS], cluster[RIGHT_DOWN][Y_AXIS], cluster[RIGHT_DOWN][X_AXIS], cluster[LEFT_UP][Y_AXIS]), color, width=3)
img.line((cluster[RIGHT_DOWN][X_AXIS], cluster[RIGHT_DOWN][Y_AXIS], cluster[LEFT_UP][X_AXIS], cluster[RIGHT_DOWN][Y_AXIS]), color, width=3)
def paint_rectangle(img, clusters, color):
for i, cluster in list(enumerate(clusters)):
paint_rectangle_lines(img, cluster, color)
def segmentation_ext(orig_img, mask_value=MASK_THRESHOLD, pixel_distance=PIXEL_DISTANCE1, cluster_distance_x=CLUSTER_DISTANCE_X,
cluster_distance_y=CLUSTER_DISTANCE_Y, final_cluster_distance_x=FINAL_CLUSTER_DISTANCE_X1,
final_cluster_distance_y=FINAL_CLUSTER_DISTANCE_Y1, min_cluster_size=MIN_CLUSTER_SIZE1, min_final_cluster_size=MIN_FINAL_CLUSTER_SIZE1,
horiz_cluster_filter_ratio=HORIZ_CLUSTER_FILTER_RATIO1, psnr_threshold=PSNR_THRESHOLD, x_step=X_STEP, y_step=Y_STEP1, search_offset=SEARCH_OFFSET,
debug_mode=LOG1, search_image_path=SEARCH_IMAGE_PATH, thumbnails=THUMBNAILS1, object_distance=OBJECT_DISTANCE1, filter_x=FILTER_X1, filter_y=FILTER_Y1):
print "reading: ", orig_img
start_time = timeit.default_timer()
global PIXEL_DISTANCE
PIXEL_DISTANCE = pixel_distance
global THUMBNAILS
THUMBNAILS = thumbnails
global Y_STEP
Y_STEP = y_step
global FINAL_CLUSTER_DISTANCE_X
FINAL_CLUSTER_DISTANCE_X = final_cluster_distance_x
global MIN_CLUSTER_SIZE
MIN_CLUSTER_SIZE = min_cluster_size
global MIN_FINAL_CLUSTER_SIZE
MIN_FINAL_CLUSTER_SIZE = min_final_cluster_size
global OBJECT_DISTANCE
OBJECT_DISTANCE = object_distance
global FILTER_X
FILTER_X = filter_x
global FILTER_Y
FILTER_Y = filter_y
global LOG
LOG = debug_mode
im0 = Image.open(orig_img)
if LOG == 1: im3 = Image.open(orig_img) # just for log
# take only 0 and 255 values
mask = im0.point(lambda i: (i == 0 or i == MAX_VALUE) and MAX_VALUE)
if LOG == 1: mask.save("mask_orig.png")
if LOG == 1: print "size ", im0.size
global all_pixels
all_pixels = mask.load()
width, height = im0.size
global clusters
global pixels
x = 0
idx = -LAST_CLUSTERS
# separate frame into subframes (X axis) in order to increase performance
global areas
i = 0
while i < width/SUB_CLUSTER_SIZE:
tmp = []
areas[i] = tmp
i += 1
while x < width:
y = 0
while y < height:
cpixel = all_pixels[x, y]
if cpixel[0] == MAX_VALUE:
area_idx = int(x/SUB_CLUSTER_SIZE)
x_min, y_min, x_max, y_max, is_merged = match_cluster(x, y, width, height, area_idx)
if is_merged == False:
cluster = []
cluster.append([x_min, y_min])
cluster.append([x_max, y_max])
clusters.append(cluster)
areas[area_idx].append(cluster)
idx += 1
y += 1
x += x_step
# filter out very thin clusters
for cluster in list(clusters):
if abs(cluster[LEFT_UP][X_AXIS]-cluster[RIGHT_DOWN][X_AXIS]) <= FILTER_X or abs(cluster[LEFT_UP][Y_AXIS]-cluster[RIGHT_DOWN][Y_AXIS]) <= FILTER_Y:
clusters.remove(cluster)
if LOG ==1: dump_clusters("init clusters", clusters)
print "preclustering count", len(clusters)
t1 = timeit.default_timer()
print "t1", t1 - start_time
imgdraw = ImageDraw.Draw(mask)
# iterate clusters
if LOG == 1:
paint_rectangle(imgdraw, clusters, "green")
print "clusters count: ", len(clusters)
draw = ImageDraw.Draw(im0)