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Torsten Bögershausen authoredWhen working in a cross-platform environment, a user may want to check if text files are stored normalized in the repository and if .gitattributes are set appropriately. Make it possible to let Git show the line endings in the index and in the working tree and the effective text/eol attributes. The end of line ("eolinfo") are shown like this: "-text" binary (or with bare CR) file "none" text file without any EOL "lf" text file with LF "crlf" text file with CRLF "mixed" text file with mixed line endings. The effective text/eol attribute is one of these: "", "-text", "text", "text=auto", "text eol=lf", "text eol=crlf" git ls-files --eol gives an output like this: i/none w/none attr/text=auto t/t5100/empty i/-text w/-text attr/-text t/test-binary-2.png i/lf w/lf attr/text eol=lf t/t5100/rfc2047-info-0007 i/lf w/crlf attr/text eol=crlf doit.bat i/mixed w/mixed attr/ locale/XX.po to show what eol convention is used in the data in the index ('i'), and in the working tree ('w'), and what attribute is in effect, for each path that is shown. Add test cases in t0027. Helped-By:
Eric Sunshine <sunshine@sunshineco.com>
Signed-off-by: Torsten Bögershausen <tboegi@web.de>
Signed-off-by: Junio C Hamano <gitster@pobox.com>Torsten Bögershausen authoredWhen working in a cross-platform environment, a user may want to check if text files are stored normalized in the repository and if .gitattributes are set appropriately. Make it possible to let Git show the line endings in the index and in the working tree and the effective text/eol attributes. The end of line ("eolinfo") are shown like this: "-text" binary (or with bare CR) file "none" text file without any EOL "lf" text file with LF "crlf" text file with CRLF "mixed" text file with mixed line endings. The effective text/eol attribute is one of these: "", "-text", "text", "text=auto", "text eol=lf", "text eol=crlf" git ls-files --eol gives an output like this: i/none w/none attr/text=auto t/t5100/empty i/-text w/-text attr/-text t/test-binary-2.png i/lf w/lf attr/text eol=lf t/t5100/rfc2047-info-0007 i/lf w/crlf attr/text eol=crlf doit.bat i/mixed w/mixed attr/ locale/XX.po to show what eol convention is used in the data in the index ('i'), and in the working tree ('w'), and what attribute is in effect, for each path that is shown. Add test cases in t0027. Helped-By:Eric Sunshine <sunshine@sunshineco.com>
Signed-off-by: Torsten Bögershausen <tboegi@web.de>
Signed-off-by: Junio C Hamano <gitster@pobox.com>
detect_structures.py 24.93 KiB
# From Python
import sys
import cv2
import numpy as np
import os
from sys import platform
from dotmap import DotMap
from shapely import affinity
from shapely.geometry import Polygon
#sys.path.append('/Users/Tilman/Documents/Programme/Python/forschungspraktikum/openpose/python');
from openpose import pyopenpose as op
from lib.triangles import *
from lib.bisection import *
from lib.misc import *
from lib.kmeans import *
SHOW_WINDOW = True
SAVE_FILE = not SHOW_WINDOW
SAVE_BDCN = False
BDCN_EDGES = False
TRIANGLES = False
BODY_LINES = True
BISEC_VECTORS = False
BISEC_CONES = True
GLOBAL_LINE = True
INPAINT_AND_KMEANS = True
DRAW_FIRST_CONE_LAYER_BRIGTHER = True
BISEC_CONE_ANGLE = 50
CORRECTION_ANGLE = 23
OVERLAY_ALPHA = 0.2
BISEC_SKIP_LOWER_LEVELS = False
DISPLAY_RASTER_ELEMENTS = 500
KMEANS_AREA_MASK_THRESHOLD = 0.09 #max 1.0 (percent of pixels), cur 0.09
KMEANS_K = 10 #10
OUT_DIR = 'images/out/images_imdahl/kmeans_output_canvas/new'
#IN_DIR = "images/first_email/" # images from first email
IN_DIR = "images/images_imdahl/" # images from imdahl
IN_DIR_BDCN = "images/out/images_imdahl/bdcn_output/"
SKIP_OPENPOSE = False
OPENPOSE_DEMO_KEYPOINTS = np.array([[[4.7613028e+02, 3.3695804e+02, 9.0203685e-01],[5.3667474e+02, 3.8633786e+02, 6.6615295e-01],[5.1645105e+02, 3.8405157e+02, 5.1514143e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[5.5459924e+02, 3.8859457e+02, 6.4240879e-01],[5.6353766e+02, 4.7384988e+02, 1.8810490e-01],[5.3886292e+02, 5.2543573e+02, 9.0144195e-02],[5.4566248e+02, 5.3215259e+02, 3.6083767e-01],[5.2768524e+02, 5.3213129e+02, 3.1196830e-01],[5.4556714e+02, 6.3534674e+02, 1.8182488e-01],[5.8149310e+02, 7.2958716e+02, 1.3625422e-01],[5.6579541e+02, 5.3216382e+02, 3.6866242e-01],[5.8822272e+02, 6.2862476e+02, 1.7708556e-01],[6.0843213e+02, 7.2955762e+02, 2.2736737e-01],[4.7597812e+02, 3.2798129e+02, 5.7176876e-01],[4.8729745e+02, 3.3027243e+02, 9.1296065e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[5.2090784e+02, 3.3472034e+02, 7.7942842e-01],[5.7928674e+02, 7.5646222e+02, 2.0351715e-01],[5.9049512e+02, 7.5648248e+02, 2.0819387e-01],[6.2183606e+02, 7.3853394e+02, 1.7312977e-01],[5.8145673e+02, 7.5420642e+02, 1.2660497e-01],[5.7701074e+02, 7.5417773e+02, 1.2881383e-01],[5.8374255e+02, 7.3627380e+02, 9.4869599e-02]]
,[[6.4435681e+02, 3.6383255e+02, 8.9096022e-01],[6.6903070e+02, 3.9760306e+02, 8.7681645e-01],[6.4430103e+02, 3.9525812e+02, 7.9584122e-01],[6.3310535e+02, 4.5589160e+02, 3.7108111e-01],[5.9046979e+02, 4.2451276e+02, 4.0277350e-01],[6.9366602e+02, 4.0197583e+02, 8.9528430e-01],[6.8247137e+02, 4.6042902e+02, 5.5132395e-01],[6.0616620e+02, 4.3569894e+02, 3.4303352e-01],[6.5551196e+02, 5.1196445e+02, 2.9572365e-01],[6.3529651e+02, 5.0747903e+02, 2.8629595e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[6.7573169e+02, 5.1421967e+02, 3.0180413e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[6.4206000e+02, 3.5276721e+02, 7.2430253e-01],[6.5327673e+02, 3.5271103e+02, 9.4265050e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[6.7577380e+02, 3.5269864e+02, 8.9672232e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[7.2723553e+02, 4.0875150e+02, 8.3982950e-01],[7.6091986e+02, 4.6032086e+02, 5.0676465e-01],[7.3178253e+02, 4.5359366e+02, 3.5797939e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.8784674e+02, 4.6483188e+02, 5.6356871e-01],[7.6320721e+02, 5.6802844e+02, 3.7939239e-01],[7.2953772e+02, 5.4564911e+02, 1.5424372e-01],[7.6546356e+02, 6.1964557e+02, 1.7308682e-01],[7.3854327e+02, 6.1513757e+02, 1.5351829e-01],[7.3855487e+02, 7.3405249e+02, 5.6986582e-02],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.8789227e+02, 6.2636108e+02, 1.8666090e-01],[7.9010718e+02, 7.5197815e+02, 9.0752751e-02],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.2722571e+02, 3.9980579e+02, 4.9854943e-01],[7.4074554e+02, 4.0420221e+02, 8.2562774e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.6537799e+02, 4.0880304e+02, 6.8228495e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[2.6297342e+02, 3.4823679e+02, 9.1535652e-01],[2.1584425e+02, 3.8410617e+02, 4.2777365e-01],[2.0466562e+02, 3.8629623e+02, 6.5148002e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[2.2483388e+02, 3.7963403e+02, 2.8349286e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[2.1584836e+02, 5.5681036e+02, 7.1318626e-02],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[2.5625162e+02, 3.3922253e+02, 8.9375269e-01],[2.6528430e+02, 3.3701016e+02, 1.3707811e-01],[2.2490630e+02, 3.4151849e+02, 8.1041366e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[3.1685654e+02, 3.3244104e+02, 6.7855740e-01],[2.9669766e+02, 3.7735825e+02, 3.5962355e-01],[2.6300262e+02, 3.8186972e+02, 4.8755571e-01],[2.8984323e+02, 5.1421375e+02, 1.4892229e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[3.3024896e+02, 3.7068640e+02, 3.1298172e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[3.0774149e+02, 3.2796570e+02, 6.2570477e-01],[3.1678952e+02, 3.1911349e+02, 2.6238269e-01],[2.8093988e+02, 3.3702823e+02, 4.3097427e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[4.0661322e+02, 3.3243243e+02, 7.2449613e-01],[3.5496320e+02, 3.7965060e+02, 3.2941282e-01],[2.0466562e+02, 3.8629623e+02, 6.5148002e-01],[2.4725473e+02, 4.3794165e+02, 1.0388593e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[3.9527917e+02, 3.7732455e+02, 1.9104436e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[3.9753812e+02, 3.2572601e+02, 7.9601538e-01],[4.1098145e+02, 3.2347913e+02, 4.7544584e-01],[3.5937631e+02, 3.2570648e+02, 6.8124008e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[1.1046178e+02, 3.3481174e+02, 8.0748719e-01],[5.2089359e+01, 3.7064417e+02, 1.8357244e-01],[6.7774979e+01, 3.6842288e+02, 4.0538907e-01],[1.3283961e+02, 3.8408841e+02, 2.2997330e-01],[1.7771373e+02, 3.4149902e+02, 2.7701011e-01],[3.4127533e+01, 3.6390732e+02, 1.1019738e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[9.9306007e+01, 3.3019724e+02, 9.2014235e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.8990585e+01, 3.3474988e+02, 8.4556317e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]
,[[7.0929346e+02, 3.5261667e+02, 3.9232758e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.8120416e+02, 4.0420923e+02, 5.1513046e-02],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.0712549e+02, 3.4816431e+02, 1.5942883e-01],[7.1832990e+02, 3.4808749e+02, 3.8954309e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[7.4299701e+02, 3.5270523e+02, 2.7498546e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00]]])
## misc functions
def overlay_two_image_v2(image, overlay, ignore_color=[0,0,0], alpha=0.1):
ignore_color = np.asarray(ignore_color)
mask = (overlay==ignore_color).all(-1,keepdims=True)
out = np.where(mask,image,(image * (1-alpha) + overlay * alpha).astype(image.dtype))
return out
## misc functions end
params = dict()
#params["model_folder"] = "/Users/Tilman/Documents/Programme/Python/forschungspraktikum/openpose/models/"
params["model_folder"] = os.environ['OPENPOSE_MODELS']
#https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/doc/output.md#pose-output-format-body_25
# Starting OpenPose
if not SKIP_OPENPOSE:
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
images = [os.path.join(os.getcwd(), IN_DIR, f) for f in os.listdir(IN_DIR)] #make path absolute so os.chdir has no side effects
images_bdcn = [os.path.join(os.getcwd(), IN_DIR_BDCN, f) for f in os.listdir(IN_DIR_BDCN)]
images.sort()
images_bdcn.sort()
os.chdir(OUT_DIR) #save images in this dir
#for img_name in images:
# for img_name, img_bdcn in list(zip(images, images_bdcn)):
# for img_name, img_bdcn in list(zip(images, images_bdcn)):
for img_name, img_bdcn in (list(zip(images, images_bdcn))[15:16] if SKIP_OPENPOSE else list(zip(images, images_bdcn))): #jesus
# for img_name, img_bdcn in list(zip(images, images_bdcn))[6:7]: #fusswaschung
# Process Image
print("calculating: "+img_name)
img = cv2.imread(img_name)
output_canvas = np.array([[[255,255,255]]*len(img[0])]*len(img),np.uint8)
bdcn_image = cv2.imread(img_bdcn)
max_lw = max(len(img),len(img[0]))
esz = max_lw / DISPLAY_RASTER_ELEMENTS
if SKIP_OPENPOSE:
datum = DotMap()
datum.poseKeypoints = OPENPOSE_DEMO_KEYPOINTS
print("Skipping OPENPOSE")
else:
datum = op.Datum()
datum.cvInputData = img
opWrapper.emplaceAndPop([datum])
img = datum.cvOutputData
if INPAINT_AND_KMEANS:
target = cv2.imread(img_name)
#remove cracks and prepare for kmeans
# target = cv2.GaussianBlur(target,(5,5),0) #unsharpen all
# target = cv2.medianBlur(target,33) #remove cracks with with 33 pixel
# target = cv2.bilateralFilter(target,33,90,90) #bring back edges and remove textures
# target = cv2.bilateralFilter(target,13,90,90) #bring back edges and remove textures
# target = cv2.medianBlur(target,53) #remove big color patches
# target = cv2.bilateralFilter(target,13,90,90) #bring back edges and remove textures
target = cv2.medianBlur(target,11) #smoothen cracks
target = cv2.bilateralFilter(target,60,90,90) #remove cracks
fposes = np.array([np.array([line[:2] for line in pose if line[2] > 0]) for pose in datum.poseKeypoints]) #filtered poses without zero lines
mask = np.zeros((len(img),len(img[0]),1), np.uint8)
kmout_mask = np.zeros((len(img),len(img[0]),1), np.uint8)
for pose in fposes: #remove bodys
convexhull = Polygon(pose).convex_hull
sconvexhull = affinity.scale(convexhull, xfact=1.7, yfact=2, origin=convexhull.centroid)
cv2.drawContours(mask, [polyToArr(sconvexhull)], 0, 255, int(20*esz))
cv2.drawContours(kmout_mask, [polyToArr(sconvexhull)], 0, 255, int(20*esz))
cv2.drawContours(mask, [polyToArr(sconvexhull)], 0, 255, -1)
cv2.drawContours(kmout_mask, [polyToArr(sconvexhull)], 0, 255, -1)
cv2.rectangle(mask, (0,0), (len(img[0]),len(img)), 255, int(40*esz)) #remove frames
inpainted = cv2.inpaint(target, mask, 3, cv2.INPAINT_TELEA)
inpainted = cv2.medianBlur(inpainted,int(esz*5)+1 if int(esz*5)%2==0 else int(esz*5))
inpainted = cv2.bilateralFilter(inpainted,30,60,60)
#to kmeans
kmeans_out = imgKmeans(inpainted, KMEANS_K)
# cv2.imshow(img_name+"inpainted", inpainted)
# cv2.imshow(img_name+"mask", kmout_mask)
kmout_mask=cv2.cvtColor(kmout_mask,cv2.COLOR_GRAY2BGR) #change mask to a 3 channel image
km_mask_out=cv2.subtract(kmout_mask,kmeans_out)
km_mask_out=cv2.subtract(kmout_mask,km_mask_out)
#idea: count how many pixels the white mask has, count how many pixels each color from kmeans result appears in mask -> area > 80% is foreground
kmeans_colors, kmeans_counts = np.unique(kmeans_out.reshape(-1, kmeans_out.shape[-1]),axis=0,return_counts=True)
km_mask_colors, km_mask_counts = np.unique(km_mask_out.reshape(-1, km_mask_out.shape[-1]),axis=0,return_counts=True)
mask_colors, mask_counts = np.unique(kmout_mask.reshape(-1, kmout_mask.shape[-1]),axis=0,return_counts=True)
threshold_count = int(KMEANS_AREA_MASK_THRESHOLD*mask_counts[(np.argwhere(mask_colors>0))[0,0]]) #select counts from white color and generate threshold with it.
km_mask_colors = km_mask_colors[km_mask_counts.argsort()][::-1] # sort colors by count
km_mask_counts = km_mask_counts[km_mask_counts.argsort()][::-1] # sort counts by count
filtered_km_mask_FG_colors = km_mask_colors[np.argwhere(km_mask_counts>=threshold_count)]
filtered_km_mask_FG_colors = filtered_km_mask_FG_colors[np.sum(filtered_km_mask_FG_colors, axis=2)>0] #filter out black from mask
filtered_km_mask_BG_colors = km_mask_colors[np.argwhere(km_mask_counts<threshold_count)]
filtered_km_mask_BG_colors = filtered_km_mask_BG_colors[np.sum(filtered_km_mask_BG_colors, axis=2)>0] #filter out black from mask
print("kmeans_colors,kmeans_counts", kmeans_colors, kmeans_counts)
print(km_mask_colors, km_mask_counts)
print(mask_colors, mask_counts)
print(filtered_km_mask_FG_colors)
print(len(km_mask_colors))
cv2.putText(km_mask_out, "Foreground colors:", (10,25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255))
offset=220;
for color in filtered_km_mask_FG_colors:
#print colors on screen
print("color",color[0],color[1],color[2])
x = offset
y = 15
cv2.rectangle(km_mask_out, (x,y), (x+10,y+10), (int(color[0]),int(color[1]),int(color[2])), -1)
offset += 15
#replace colors
# kmeans_out[((kmeans_out[:,:,0] == color[0]) & (kmeans_out[:,:,1] == color[1]) & (kmeans_out[:,:,2] == color[2]))]=filtered_km_mask_FG_colors[0] #search in kmeans image for specific color and replace all by first color
kmeans_out[((kmeans_out[:,:,0] == color[0]) & (kmeans_out[:,:,1] == color[1]) & (kmeans_out[:,:,2] == color[2]))]=[255,255,255] #search in kmeans_out image for specific color and replace all by first color
for color in filtered_km_mask_BG_colors:
kmeans_out[((kmeans_out[:,:,0] == color[0]) & (kmeans_out[:,:,1] == color[1]) & (kmeans_out[:,:,2] == color[2]))]=[0,0,0] #search in kmeans_out image for specific color and replace all by first color
for color in kmeans_colors[np.invert(np.isin(kmeans_colors, km_mask_colors).all(axis=1))]: #colors in kmean_out wich do not appear in any mask
kmeans_out[((kmeans_out[:,:,0] == color[0]) & (kmeans_out[:,:,1] == color[1]) & (kmeans_out[:,:,2] == color[2]))]=[0,0,0] #search in kmeans_out image for specific color and replace all by first color
#apply erosure/dilation morphing filters
# kmeans_out =
# cv2.imshow(img_name+"kmeans_masked", km_mask_out)
#replace colors
# kmeans_out = cv2.medianBlur(kmeans_out,53)
# kmeans_out = cv2.bilateralFilter(kmeans_out,30,60,60)
# kmeans_bgfg = imgKmeans(kmeans_out, 3)
# cv2.namedWindow("kmeans_bgfg", cv2.WINDOW_NORMAL)
# cv2.imshow("kmeans_bgfg", kmeans_bgfg)
cv2.namedWindow("kmeans_out", cv2.WINDOW_NORMAL)
cv2.imshow("kmeans_out", kmeans_out)
cv2.waitKey(0)
cv2.rectangle(kmeans_out, (0,0), (235,30), (0,0,0), -1)
cv2.putText(kmeans_out, "Foreground color:", (10,25), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255,255,255))
cv2.rectangle(kmeans_out, (220,15), (230,25), (int(filtered_km_mask_FG_colors[0][0]),int(filtered_km_mask_FG_colors[0][1]),int(filtered_km_mask_FG_colors[0][2])), -1)
cv2.rectangle(kmeans_out, (220,15), (230,25), (255,255,255), 1)
output_canvas = kmeans_out
if BDCN_EDGES:
#overlay = np.array([[[255,255,255]]*len(img[0])]*len(img),np.uint8) #white image canvas
#output_canvas = overlay_two_image_v2(output_canvas, overlay, [0,0,0], 0.2)
fposes = np.array([np.array([line[:2] for line in pose if line[2] > 0]) for pose in datum.poseKeypoints]) #filtered poses without zero lines
for pose in fposes:
convexhull = Polygon(pose).convex_hull
sconvexhull = affinity.scale(convexhull, xfact=1.6, yfact=1.6, origin=convexhull.centroid)
# print(convexhull)
#cv2.line(img, (int(pose[0][0]),int(pose[0][1])), (int(pose[1][0]),int(pose[1][1])), (0,255,0), int(6*esz))
# cv2.drawContours(img, [polyToArr(sconvexhull)], 0, 255, int(10*esz))
# cv2.drawContours(img, [polyToArr(sconvexhull)], 0, 255, -1)
cv2.drawContours(bdcn_image, [polyToArr(sconvexhull)], 0, (255,255,255), int(10*esz))
cv2.drawContours(bdcn_image, [polyToArr(sconvexhull)], 0, (255,255,255), -1)
#draw rectangle
# cv2.rectangle(img, (0,0), (len(img[0]),len(img)), (255,0,0), int(40*esz))
cv2.rectangle(bdcn_image, (0,0), (len(img[0]),len(img)), (255,255,255), int(40*esz)) #eliminate rectangle from frames
blurred = cv2.medianBlur(bdcn_image,int(esz*5)+1 if int(esz*5)%2==0 else int(esz*5))
print("med1")
blurred = cv2.bilateralFilter(blurred,30,60,60)
print("bil1")
blurred = cv2.bilateralFilter(blurred,30,60,60)
print("bil2")
blurred = cv2.bilateralFilter(blurred,30,60,60)
print("bil3")
blurred = cv2.bilateralFilter(blurred,30,60,60)
print("bil4")
#output_canvas = overlay_two_image_v2(output_canvas, blurred, [0,0,0], 0.5)
#output_canvas = cv2.addWeighted(blurred, 0.2, output_canvas, 0.8, 0.0)
# cv2.imshow(img_name+"blurred", blurred)
rows,cols,channels = blurred.shape
roi = output_canvas[0:rows, 0:cols ]
img2gray = cv2.cvtColor(blurred,cv2.COLOR_BGR2GRAY)
ret, mask = cv2.threshold(img2gray, 100, 255, cv2.THRESH_BINARY_INV)
mask_inv = cv2.bitwise_not(mask)
# cv2.imshow(img_name+"mask_inv", mask_inv)
output_canvas = cv2.bitwise_and(roi,roi,mask = mask_inv)
if TRIANGLES or BODY_LINES:
triangles = [poseToTriangle(pose) for pose in datum.poseKeypoints]
for triangle in triangles:
if triangle is not None:
if TRIANGLES:
cv2.drawContours(img, [triangle], 0, 255, -1)
cv2.drawContours(output_canvas, [triangle], 0, 255, -1)
if BODY_LINES:
linePoints = triangleToBodyLine(triangle)
cv2.line(img, trp(linePoints[0]), trp(linePoints[1]), (0,255,0), int(6*esz))
cv2.line(output_canvas, trp(linePoints[0]), trp(linePoints[1]), (0,255,0), int(6*esz))
if BISEC_CONES or GLOBAL_LINE:
bisecCones = [poseToBisectCone(pose, max_lw, BISEC_CONE_ANGLE, CORRECTION_ANGLE) for pose in datum.poseKeypoints] #use max(img.height,width) as cone length
bisecCones = [v for v in bisecCones if v] #remove None values
#print([polyToArr(bisecCone) for bisecCone in bisecCones])
intersections = coneIntersections(bisecCones)
maxlevel = max(map(lambda t: len(t), intersections.keys()))
for combi in intersections:
is_not_last_level = len(combi) < maxlevel
if is_not_last_level and BISEC_SKIP_LOWER_LEVELS:
continue;
else:
overlay = np.zeros((len(img),len(img[0]),3), np.uint8)
color = min(((len(combi)-1)*100,255))
alpha = OVERLAY_ALPHA
if DRAW_FIRST_CONE_LAYER_BRIGTHER and len(combi) == 1:
cv2.drawContours(overlay, [polyToArr(intersections[combi])], 0, (0,255,0), -1)
img = overlay_two_image_v2(img, overlay, [0,0,0], (0.25))
if BISEC_CONES:
cv2.drawContours(overlay, [polyToArr(intersections[combi])], 0, (color,0,(0 if is_not_last_level else 255)), -1)
img = overlay_two_image_v2(img, overlay, [0,0,0], (alpha if is_not_last_level else 0.6))
if not is_not_last_level and GLOBAL_LINE: #draw centroid of last polygon
xy = (int(intersections[combi].centroid.x),int(intersections[combi].centroid.y))
global_angle = getGlobalLineAngle(datum.poseKeypoints, CORRECTION_ANGLE)
print("global_angle",np.rad2deg(global_angle))
dist = max_lw
d = (int(dist * np.cos(global_angle)), int(dist * np.sin(global_angle))) #with origin zero
d_l = (int(-dist * np.cos(global_angle)), int(-dist * np.sin(global_angle))) #with origin zero
# draw line with global gaze angle (special mean of all gaze angles) and through center of last intersection
cv2.line(img, xy, (xy[0]+d[0],xy[1]-d[1]), (0,255,255), int(10*esz))
cv2.line(output_canvas, xy, (xy[0]+d[0],xy[1]-d[1]), (0,255,255), int(10*esz))
cv2.line(img, xy, (xy[0]+d_l[0],xy[1]-d_l[1]), (0,255,255), int(10*esz))
cv2.line(output_canvas, xy, (xy[0]+d_l[0],xy[1]-d_l[1]), (0,255,255), int(10*esz))
cv2.circle(img, xy, int(13*esz), (255,255,0), -1)
cv2.circle(output_canvas, xy, int(13*esz), (255,255,0), -1)
if BISEC_VECTORS:
bisecVectors = [poseToBisectVector(pose, CORRECTION_ANGLE) for pose in datum.poseKeypoints]
for bisecVector in bisecVectors:
if bisecVector is not None:
cv2.arrowedLine(img, trp(bisecVector[1]), trp(bisecVector[0]), (0,0,255), int(4*esz))
cv2.arrowedLine(output_canvas, trp(bisecVector[1]), trp(bisecVector[0]), (0,0,255), int(4*esz))
if SAVE_FILE:
cv2.imwrite(os.path.basename(img_name),img)
cv2.imwrite(os.path.basename(img_bdcn)+'canvas_cracksNewFilterd_ne2.jpg',output_canvas)
if SHOW_WINDOW:
cv2.namedWindow(img_name, cv2.WINDOW_NORMAL)
cv2.imshow(img_name, img)
cv2.namedWindow("canvas", cv2.WINDOW_NORMAL)
cv2.imshow("canvas", output_canvas)
cv2.waitKey(0)
if SHOW_WINDOW:
cv2.waitKey(0)
cv2.destroyAllWindows()