import os
import time
import datetime
import mmcv
import cv2 as cv
import json
import numpy as np
import pycocotools.mask as maskutil
import pycocotools.coco as COCO
from itertools import groupby
from skimage import measure,draw,data
from PIL import Image
def close_contour(contour):
if not np.array_equal(contour[0], contour[-1]):
contour = np.vstack((contour, contour[0]))
return contour
def binary_mask_to_polygon(binary_mask, tolerance=0):
"""Converts a binary mask to COCO polygon representation
Args:
binary_mask: a 2D binary numpy array where '1's represent the object
tolerance: Maximum distance from original points of polygon to approximated
polygonal chain. If tolerance is 0, the original coordinate array is returned.
"""
polygons = []
# pad mask to close contours of shapes which start and end at an edge
padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0)
contours = measure.find_contours(padded_binary_mask, 0.5)
contours = np.subtract(contours, 1)
for contour in contours:
contour = close_contour(contour)
contour = measure.approximate_polygon(contour, tolerance)
if len(contour) < 3:
continue
contour = np.flip(contour, axis=1)
segmentation = contour.ravel().tolist()
# after padding and subtracting 1 we may get -0.5 points in our segmentation
segmentation = [0 if i < 0 else i for i in segmentation]
polygons.append(segmentation)
return polygons
def binary_mask_to_rle(binary_mask):
rle = {'counts': [], 'size': list(binary_mask.shape)}
counts = rle.get('counts')
for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))):
if i == 0 and value == 1:
counts.append(0)
counts.append(len(list(elements)))
return rle
def main2():
seg=np.array([312.29, 562.89, 402.25, 511.49, 400.96, 425.38, 398.39, 372.69, 388.11, 332.85, 318.71, 325.14, 295.58, 305.86, 269.88, 314.86, 258.31, 337.99, 217.19, 321.29, 182.49, 343.13, 141.37, 348.27, 132.37, 358.55, 159.36, 377.83, 116.95, 421.53, 167.07, 499.92, 232.61, 560.32, 300.72, 571.89])
compactedRLE = maskutil.frPyObjects([seg], 768, 768)
print(compactedRLE)
#compactedRLE=[
# {"size":[768, 768],
# "counts": "`eQ66ig02O1O000000000000000000000000001O00000000000000000000000000000000000000000000000000000000O2O0NbZj:"
# }]
mask = maskutil.decode(compactedRLE)
mask=np.reshape(mask,(768,768))
mask[:,:]=mask[:,:]*255
print(mask)
#mmcv.imshow(mask)
'''
mask=np.array(
[
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 1, 0, 0, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 1, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0]
]
)
print(mask)
'''
poly=binary_mask_to_polygon(mask)
print(poly)
rle=binary_mask_to_rle(mask)
print(rle)
#mmcv.imshow(area)
return 0
def class2color(classes=1,class_id=0):
sum = classes*12357
return [sum%(class_id+0),sum%(class_id+1),sum%(class_id+2)]
def mainContour():
imgfile = "/home/wit/Pictures/7dd98d1001e9390100d9e95171ec54e737d19681.jpg"
img = cv.imread(imgfile)
h, w, _ = img.shape
gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
ret, thresh = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)
# Find Contour
_, contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)
print(contours)
def main():
testimagepath = "/media/wit/WeiJX/AirbusShip/coco-labels/instances_ships_test2018.json"
compressedRLECOCOlabelpath = "/media/wit/WeiJX/workspace/out/maskrcnn.reorg.pkl.json"
imageprefix = "/media/wit/WeiJX/AirbusShip/test-images/"
startTime = time.time()
trthset = json.load(open(testimagepath, 'r'))
assert type(trthset) == dict, 'annotation file format {} not supported'.format(type(trthset))
prdcset = json.load(open(compressedRLECOCOlabelpath, 'r'))
assert type(prdcset) == dict, 'annotation file format {} not supported'.format(type(prdcset))
print('Done (t={:0.2f}s)'.format(time.time() - startTime))
ann_Y0 = trthset['annotations']
ann_Y1 = prdcset['annotations']
for image in trthset['images']:
imagepath = imageprefix+image['file_name']
img = cv.imread(imagepath)
src = np.zeros((768,768,3), np.uint8)
src[:,:,:]=img[:,:,:]
dst = np.zeros((768,768,3), np.uint8)
dst[:,:,:]=img[:,:,:]
masks = np.zeros((768, 768, 1), np.uint8)
masks.fill(0)
id0 = image['id']
counts = 0
contours = []
for target in ann_Y0:
if target['image_id']==id0:
counts += 1
j=0
X=[]
Y=[]
for seg in target['segmentation'][0]:
if j == 0:
x = float(seg)
X.append(x)
else:
y = float(seg)
Y.append(y)
j = 1-j
rr, cc = draw.polygon(Y, X)
draw.set_color(src, [rr, cc], [0, 0, 255], 0.4)
Point = np.zeros((len(Y), 2), dtype='int32')
Point [:, 0] = X[:]
Point [:, 1] = Y[:]
#print(Point)
cv.fillPoly(masks, np.array([Point],'int32'), 1)
src[:, :, 0] = img[:, :, 0] #* 0.9 + masks[:, :, 0] * 0.1 * 255.0 / counts
src[:, :, 1] = img[:, :, 1] #* 0.9 + masks[:, :, 0] * 0.1 * 255.0 / counts
src[:, :, 2] = img[:, :, 2] * 0.2 + masks[:, :, 0] * 0.8 * 255.0 / counts
mmcv.imshow(src,"Y",1)
masks.fill(0)
counts = 0
for target in ann_Y1:
if target['image_id']==id0:
counts += 1
CRLE = target['segmentation']
#print(CRLE)
mask = maskutil.decode(CRLE)
mask = np.reshape(mask, (img.shape[1], img.shape[0], 1))
masks[:, :] = masks[:, :] + mask[:, :]
dst[:, :, 0] = img[:, :, 0] * 0.2 + masks[:, :, 0] * 0.8 * 255.0/counts
dst[:, :, 1] = img[:, :, 1] #* 0.5 + masks[:, :, 0] * 0.5 * 255.0/counts
dst[:, :, 2] = src[:, :, 2] * 0.9 + masks[:, :, 0] * 0.1 * 255.0/counts
mmcv.imshow(dst,"Y'")
return 0
if __name__ == '__main__':
main()
