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image_difference.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
' image2data2'
__author__ = 'Jun Zhang'
import numpy as np
import math
import cv2
import queue
import os
import detect_bk as db
import kmean_fun as kf
import kmean_fun
def diff_list(list_1, list_2):
diff_list=[abs(float(list_1[i])-float(list_2[i])) for i in range(0,len(list_1))]
return max(diff_list)
def image_differene(image_file1,image_file2):
image_o = cv2.imread(image_file1)
image_p = cv2.imread(image_file2)
y_range, x_range, deepth = image_o.shape
y2_range, x2_range, deepth = image_p.shape
out = np.zeros((y_range, x_range, 3), dtype="uint8")
#test_file="test.jpg"
#print(y_range, x_range, y2_range, x2_range)
#this is the default names of colors
blacks = (0, 0, 0)
whites = (255, 255, 255)
greens = (0, 255, 0)
blues = (100, 100, 255)
r_list=[]
g_list=[]
b_list=[]
r2_list=[]
g2_list=[]
b2_list=[]
for x in range(0, x_range):
for y in range(0, y_range):
f=list(image_p[y,x])
a=f[0]
b=f[1]
c=f[2]
red = int(a)
green = int(b)
blue = int(c)
f2 = list(image_o[y, x])
if(diff_list(f,f2)>=40):
r_list.append(f2[0])
g_list.append(f2[1])
b_list.append(f2[2])
#cv2.rectangle(out, (x, y), (x, y), whites, -1)
else:
r2_list.append(f2[0])
g2_list.append(f2[1])
b2_list.append(f2[2])
#cv2.rectangle(out, (x, y), (x + 1, y + 1), (int(f2[0]),int(f2[1]),int(f2[2])), -1)
#print(r_list)
#print(g_list)
#print(b_list)
#print(min(r_list),max(r_list))
#print(min(g_list), max(g_list))
#print("b")
#print(min(b_list), max(b_list))
#print("b")
diff_v = [float(r_list[i]) - float(g_list[i]) for i in range(len(r_list))]
#print("rg")
c = [int(abs(ele)) for ele in diff_v]
#print(c)
#print( min(c),max(c))
diff_v = [float(r_list[i]) - float(b_list[i]) for i in range(len(r_list))]
#print("rb")
c = [int(abs(ele)) for ele in diff_v]
#print(c)
#print(min(c), max(c))
diff_v = [float(b_list[i]) - float(g_list[i]) for i in range(len(b_list))]
#print("bg")
c = [int(abs(ele)) for ele in diff_v]
#print(c)
#print(min(c), max(c))
#print(sum(r_list) / len(r_list),sum(g_list) / len(g_list),sum(b_list) / len(b_list))
#print(math.sqrt(np.var(r_list)),math.sqrt(np.var(g_list)),math.sqrt(np.var(b_list)))
#print(sum(r2_list) / len(r2_list), sum(g2_list) / len(g2_list), sum(b2_list) / len(b2_list))
#print(math.sqrt(np.var(r2_list)), math.sqrt(np.var(g2_list)), math.sqrt(np.var(b2_list)))
#cv2.imwrite(test_file, out)
return (r_list, g_list, b_list, r2_list, g2_list, b2_list)
def mark_image(image_in, image_out,r_list,g_list,b_list,alpha=1):
image_o = cv2.imread(image_in)
y_range, x_range, deepth = image_o.shape
out = np.zeros((y_range, x_range, 3), dtype="uint8")
whites = (255, 255, 255)
blacks = (0, 0, 0)
for x in range(0, x_range):
for y in range(0, y_range):
f = list(image_o[y, x])
a = f[0]
b = f[1]
c = f[2]
red = int(a)
green = int(b)
blue = int(c)
if(red>=sum(r_list) / len(r_list)-alpha*math.sqrt(np.var(r_list)) and red<=sum(r_list) / len(r_list)+alpha*math.sqrt(np.var(r_list))
and green>=sum(g_list) / len(g_list)-alpha*math.sqrt(np.var(g_list)) and green<=sum(g_list) / len(g_list)+alpha*math.sqrt(np.var(g_list))
and blue>=sum(b_list) / len(b_list)-alpha*math.sqrt(np.var(b_list)) and blue<=sum(b_list) / len(b_list)+alpha*math.sqrt(np.var(b_list))):
cv2.rectangle(out, (x, y), (x + 1, y + 1), whites, -1)
else:
cv2.rectangle(out, (x, y), (x + 1, y + 1), blacks, -1)
cv2.imwrite(image_out, out)
def mark_image_3(image_ref,image_in, image_out,r_list,g_list,b_list,alpha=1):
image_o = cv2.imread(image_in)
image_r= cv2.imread(image_ref)
y_range, x_range, deepth = image_o.shape
out = np.zeros((y_range, x_range, 3), dtype="uint8")
whites = (255, 255, 255)
blacks = (0, 0, 0)
for x in range(0, x_range):
for y in range(0, y_range):
f = list(image_r[y, x])
a = f[0]
b = f[1]
c = f[2]
red = int(a)
green = int(b)
blue = int(c)
f2 = list(image_o[y, x])
a2 = f2[0]
b2 = f2[1]
c2 = f2[2]
red2 = int(a2)
green2 = int(b2)
blue2 = int(c2)
if(red>=sum(r_list) / len(r_list)-alpha*math.sqrt(np.var(r_list)) and red<=sum(r_list) / len(r_list)+alpha*math.sqrt(np.var(r_list))
and green>=sum(g_list) / len(g_list)-alpha*math.sqrt(np.var(g_list)) and green<=sum(g_list) / len(g_list)+alpha*math.sqrt(np.var(g_list))
and blue>=sum(b_list) / len(b_list)-alpha*math.sqrt(np.var(b_list)) and blue<=sum(b_list) / len(b_list)+alpha*math.sqrt(np.var(b_list))):
cv2.rectangle(out, (x, y), (x + 1, y + 1), blacks, -1)
else:
cv2.rectangle(out, (x, y), (x + 1, y + 1), (red2,green2,blue2), -1)
cv2.imwrite(image_out, out)
def get_avg_std(r_list, g_list, b_list):
avg_r=sum(r_list) / len(r_list)
avg_g = sum(g_list) / len(g_list)
avg_b = sum(b_list) / len(b_list)
std_r=math.sqrt(np.var(r_list))
std_g = math.sqrt(np.var(g_list))
std_b = math.sqrt(np.var(b_list))
return (int(avg_r), int(avg_g), int(avg_b), int(std_r), int(std_g), int(std_b))
def get_cluster_avg_std(r_list, g_list, b_list,cluster_num=7, cutoff_threshold=0.65):
dataSet = []
counts=len(r_list)
max_count=20000
for index in range(counts):
dataSet.append([r_list[index],g_list[index],b_list[index]])
if (index >= max_count):
break;
total_count=index
dataSet = np.mat(dataSet)
cluster_num = 7
centroids, clusterAssment = kmean_fun.kmeans(dataSet, cluster_num)
cluster_array = np.squeeze(np.asarray(clusterAssment[:, 0]))
cluster_list=[]
cluster_ratio = []
for num in range(cluster_num):
#c = [1, 2, 2, 1, 1, 4, 5, 1]
#idx = [idx for (idx, val) in enumerate(c) if val == 1]
#print(idx)
idx = [idx for (idx, val) in enumerate(cluster_array) if val == num]
cluster_list.append(idx)
cluster_ratio.append(len(cluster_list[num])/total_count)
#print(cluster_list[num])
sorted_index=sorted(range(len(cluster_ratio)), key=cluster_ratio.__getitem__, reverse=True)
current_ratio=0
avg_r= []
avg_g = []
avg_b = []
std_r = []
std_g = []
std_b = []
for index_ in range(cluster_num):
hit_index=sorted_index[index_]
current_ratio=current_ratio+cluster_ratio[hit_index]
#print(current_ratio)
avg_r.append(int(sum([r_list[i] for i in cluster_list[hit_index]]) / len([r_list[i] for i in cluster_list[hit_index]])))
avg_g.append(int(sum([g_list[i] for i in cluster_list[hit_index]]) / len([g_list[i] for i in cluster_list[hit_index]])))
avg_b.append(int(sum([b_list[i] for i in cluster_list[hit_index]]) / len([b_list[i] for i in cluster_list[hit_index]])))
std_r.append(int(math.sqrt(np.var([r_list[i] for i in cluster_list[hit_index]]))))
std_g.append(int(math.sqrt(np.var([g_list[i] for i in cluster_list[hit_index]]))))
std_b.append(int(math.sqrt(np.var([b_list[i] for i in cluster_list[hit_index]]))))
if(current_ratio>=cutoff_threshold):
break
return (avg_r, avg_g, avg_b, std_r, std_g, std_b)
def image_differene_quick(image_file1,image_file2):
image_o = cv2.imread(image_file1)
image_p = cv2.imread(image_file2)
y_range, x_range, deepth = image_o.shape
y2_range, x2_range, deepth = image_p.shape
out = np.zeros((y_range, x_range, 3), dtype="uint8")
#test_file="test.jpg"
#print(y_range, x_range, y2_range, x2_range)
#this is the default names of colors
blacks = (0, 0, 0)
whites = (255, 255, 255)
greens = (0, 255, 0)
blues = (100, 100, 255)
r_list=[]
g_list=[]
b_list=[]
r2_list=[]
g2_list=[]
b2_list=[]
for x in range(0, x_range):
for y in range(0, y_range):
f=list(image_p[y,x])
a=f[0]
b=f[1]
c=f[2]
red = int(a)
green = int(b)
blue = int(c)
f2 = list(image_o[y, x])
if(diff_list(f,f2)>=40):
r_list.append(f2[0])
g_list.append(f2[1])
b_list.append(f2[2])
#cv2.rectangle(out, (x, y), (x, y), whites, -1)
return (r_list, g_list, b_list)