test

PTTTest/.idea/PTTTest.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="inheritedJdk" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+  <component name="TestRunnerService">
+    <option name="PROJECT_TEST_RUNNER" value="Unittests" />
+  </component>
+</module>
\ No newline at end of file

PTTTest/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6.0rc2 (A:\Users\Antje\AppData\Local\Programs\Python\Python36\python.exe)" project-jdk-type="Python SDK" />
+</project>
\ No newline at end of file

PTTTest/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/PTTTest.iml" filepath="$PROJECT_DIR$/.idea/PTTTest.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

PTTTest/picture.py

+from PIL import Image
+import numpy
+#import requests #great lib for connecting with a REST interface
+
+def image_test():
+    list=["IMG_3119.JPG","IMG_3162.JPG","IMG_3217.JPG","IMG_3512.JPG","test.jpg","test2.jpg","wagen.jpg"]
+    for item in list:
+        test_mean(item)
+
+
+def test_mean(img):
+    im = Image.open(img)
+    width, height = im.size
+    box_list = []       #later contains tuples for easier using of crop-function; used for getting 100x100 areas of the image
+    mean_list = []      #later comtains the tuples of the mean color of the 100x100 areas
+    counter_list = []     # later comtains the tuples of the mean color of the 100x100 areas
+
+    #generates tuples for the box_list;
+    for x in range(int(width/100)):
+        for y in range(int(height/100)):
+            box=(x*100,y*100,(x+1)*100,(y+1)*100)
+            box_list.append(box)
+
+    for item in box_list:
+        mean_list.append(box_mean(im.crop(item)))       #generates the mean_list
+
+    counter_list=rgb_count(mean_list)
+    max=rgb_max3(counter_list)
+
+
+    mean=box_mean(im)
+    #print(box_list)
+    #print(mean_list)
+    #print(counter_list)
+    print(im.getdata())
+    print(max)
+    max_list=[max[0][0],max[1][0],max[2][0]]
+    show_color(max_list)
+    #print(mean)
+
+#Helpfunction; returns the mean rgb valur of a given image (area)
+def box_mean(img_region):
+    r, g, b = img_region.split() #split in r,g, and b channel
+
+    #mean computing for each channel
+    meanvalr = int(numpy.mean(r))
+    meanvalg = int(numpy.mean(g))
+    meanvalb = int(numpy.mean(b))
+
+    #return as tuple
+    return ((meanvalr,meanvalg,meanvalb))
+
+
+def rgb_count(rgbList):
+    count_list=[]
+    first=rgbList.pop()
+    #first_element=[first,1] #form of the list elements: [(r,g,b),count]
+    count_list.append([first,1])
+
+    for item in rgbList:
+        found = False
+        #top=rgbList.pop()
+        for element in count_list:
+            if alike(item,element[0],25):#45 seems to be a good value
+                element[1] += 1
+                found=True
+                break
+        if not found:
+            count_list.append([item,1])
+            print(item)
+    return count_list
+
+# helpfunktion for better code reading; checks, if the rgb color t1 nearly t2. var is for the accepted difference; returns bool
+def alike(t1,t2,var):
+    bool0 = (t1[0] >= ((t2[0]) - var)) and (t1[0] <= ((t2[0]) + var))
+    bool1 = (t1[1] >= ((t2[1]) - var)) and (t1[1] <= ((t2[1]) + var))
+    bool2 = (t1[2] >= ((t2[2]) - var)) and (t1[2] <= ((t2[2]) + var))
+    return(bool0 & bool1 & bool2)
+
+
+def rgb_max3(list):
+    max_list=[]
+    for i in range(3):
+        max = list[0]
+        for item in list:
+            if item[1]>max[1]:
+                max=item
+        max_list.append(max)
+        list.remove(max)
+    return max_list
+
+def show_color(max_list):
+    print("maxlist=",max_list)
+    im=Image.open("test.jpg")
+    pic=im.crop((0,0,600,200))
+    pixels=pic.load()
+    for x in range(600):
+        for y in range(200):
+            if x<200:
+                pixels[x,y]=max_list[0]
+            if x>=200 and x<400:
+                pixels[x, y] = max_list[1]
+            if(x >= 400):
+                pixels[x,y]=max_list[2]
+    pic.show()
+
+
+#_________________________not needed anymore______________________________________________________________
+
+
+def image_boxing(img):
+    im=Image.open(img)
+    width, height = im.size  # image size
+    mean_list = []  # saves the mean rgb value of each area
+    box=()
+    x=0
+    y=0
+
+    #let the boxing begin (area of 50x50 pixels)
+    while (x<width):#wie rum läuft was?
+        while (y<height): #brechnung der einzelnen felder
+            if(x+50>=width&y+50>height):#fall für letztes feld
+                img_region = im.crop((x, y, (width - 1),(height-1)))
+
+                r, g, b = img_region.split()
+                meanvalr = int(numpy.mean(r))
+                meanvalg = int(numpy.mean(g))
+                meanvalb = int(numpy.mean(b))
+                addtuple=(meanvalr,meanvalg,meanvalb)
+
+                mean_list.append(addtuple)
+                break
+
+            if(x+50>=width):
+                img_region=im.crop((x,y,(width-1),(y+50)))
+
+                r, g, b = img_region.split()
+                meanvalr = int(numpy.mean(r))
+                meanvalg = int(numpy.mean(g))
+                meanvalb = int(numpy.mean(b))
+
+                addtuple = (meanvalr, meanvalg, meanvalb)
+                mean_list.append(addtuple)
+                y=y+50
+
+            if(y+50>=height):
+                img_region = im.crop((x, y, (x+50), (height-1)))
+
+                r, g, b = img_region.split()
+                meanvalr = int(numpy.mean(r))
+                meanvalg = int(numpy.mean(g))
+                meanvalb = int(numpy.mean(b))
+                addtuple = (meanvalr, meanvalg, meanvalb)
+
+                mean_list.append(addtuple)
+                x=x+50
+            else:
+                img_region = im.crop((x, y, (x+50), (y + 50)))
+
+                r, g, b = img_region.split()
+                meanvalr = int(numpy.mean(r))
+                meanvalg = int(numpy.mean(g))
+                meanvalb = int(numpy.mean(b))
+                addtuple = (meanvalr, meanvalg, meanvalb)
+
+                mean_list.append(addtuple)
+
+                y=y+50
+    print(mean_list)
+    print("should be:"+(width/50)*(height/50))
+    print("is:"+mean_list.count())
+
+def picture_to_colors():
+    im = Image.open("test.jpg")
+    pixels = list(im.getdata()) #gets the rgb values from all pixels of the image and puts them in a list
+    return pixels
+
+def show_Image(im):
+    test=Image.open(im)
+    test.show()
+
+def test_show():
+    img=Image.open("test.jpg")#loads image
+    width,height=img.size
+    print((width,height))
+
+    box = (0, 0, 50, 50) #tupel, das angibt, von wo bis wo die pixel genommen werden sollen
+    img_region = img.crop(box) #takes the defined area
+
+    #pixels = list(img_region.getdata())  # get pixels of the area
+    #print(pixels)
+
+    r,g,b=img_region.split()
+    meanvalr = int(numpy.mean(r))
+    meanvalg = int(numpy.mean(g))
+    meanvalb = int(numpy.mean(b))
+
+    img_region.show()
+
+    # pixels=list(img_region.getdata()) #get pixels of the area
+    # meanval=numpy.mean(pixels)
+    # print(pixels)
+    # print(meanval)
+    print((meanvalr, meanvalg, meanvalb))
+
+
+
+#elemente zu sortieren könnte sinnvoll sein
+def colorcounter(rgbList):
+    counterList=[]      #in this list all rgb values of the image get saved with their number of appearance in the image
+    for i in rgbList:
+        new_element=[i,rgbList.count(i)]
+        if new_element not in counterList:
+            counterList.append(new_element)#rgb values get in the list. "count" counts the number of appearance
+    return counterList
+
+#idee: nach größe des vorkommens sortieren. ersten 3 elemente der Liste ausgeben lassen.
+#def get_the_most(counterList):
+   # for i in counterList: