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write_ply_planenet.py
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write_ply_planenet.py
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#from planenet code is adapted for planercnn code
import cv2
import numpy as np
WIDTH = 640
HEIGHT = 480
ALL_TITLES = ['PlaneNet']
ALL_METHODS = [('sample_np10_hybrid3_bl0_dl0_ds0_crfrnn5_sm0', '', 0, 2)]
def predict3D(folder, index, image, depth, segmentation, planes, info):
writePLYFile(folder, index, image, depth, segmentation, planes, info)
#writePLYFile(options.test_dir, image_index + options.startIndex, segmentationImageBlended, pred_dict['depth'][image_index], segmentation, pred_dict['plane'][image_index], pred_dict['info'][image_index])
print("done")
def getCameraFromInfo(info):
camera = {}
camera['fx'] = info[0]
camera['fy'] = info[5]
camera['cx'] = info[2]
camera['cy'] = info[6]
camera['width'] = info[16]
camera['height'] = info[17]
camera['depth_shift'] = info[18]
return camera
def writePLYFile(folder, index, image, depth, segmentation, planes, info):
imageFilename = str(index) + '_model_texture.png'
cv2.imwrite(folder + '/' + imageFilename, image)
#print("target-",folder + '/' + imageFilename, image)
width = image.shape[1]
height = image.shape[0]
numPlanes = planes.shape[0]
camera = getCameraFromInfo(info)
#camera = getNYURGBDCamera()
#camera = getSUNCGCamera()
urange = (np.arange(width, dtype=np.float32) / width * camera['width'] - camera['cx']) / camera['fx']
urange = urange.reshape(1, -1).repeat(height, 0)
vrange = (np.arange(height, dtype=np.float32) / height * camera['height'] - camera['cy']) / camera['fy']
vrange = vrange.reshape(-1, 1).repeat(width, 1)
X = depth * urange
Y = depth
Z = -depth * vrange
XYZ = np.stack([X, Y, Z], axis=2)
#focalLength = 517.97
faces = []
#minDepthDiff = 0.15
#maxDepthDiff = 0.3
#occlusionBoundary = boundaries[:, :, 1]
betweenRegionThreshold = 0.1
nonPlanarRegionThreshold = 0.02
planesD = np.linalg.norm(planes, axis=1, keepdims=True)
planeNormals = -planes / np.maximum(planesD, 1e-4)
croppingRatio = -0.05
dotThreshold = np.cos(np.deg2rad(30))
for y in range(height - 1):
for x in range(width - 1):
if y < height * croppingRatio or y > height * (1 - croppingRatio) or x < width * croppingRatio or x > width * (1 - croppingRatio):
continue
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
continue
point = XYZ[y][x]
#neighborPixels = []
validNeighborPixels = []
for neighborPixel in [(x, y + 1), (x + 1, y), (x + 1, y + 1)]:
neighborSegmentIndex = segmentation[neighborPixel[1]][neighborPixel[0]]
if neighborSegmentIndex == segmentIndex:
if segmentIndex < numPlanes:
validNeighborPixels.append(neighborPixel)
else:
neighborPoint = XYZ[neighborPixel[1]][neighborPixel[0]]
if np.linalg.norm(neighborPoint - point) < nonPlanarRegionThreshold:
validNeighborPixels.append(neighborPixel)
pass
pass
else:
neighborPoint = XYZ[neighborPixel[1]][neighborPixel[0]]
if segmentIndex < numPlanes and neighborSegmentIndex < numPlanes:
print("line1",planeNormals[segmentIndex].shape ,neighborPoint.shape,planesD[segmentIndex].shape )
print((np.dot(planeNormals[segmentIndex],neighborPoint)))
print(neighborPoint + planesD[segmentIndex])
print(betweenRegionThreshold or abs(np.dot(planeNormals[neighborSegmentIndex], point) + planesD[neighborSegmentIndex]))
print(betweenRegionThreshold and np.abs(np.dot(planeNormals[segmentIndex])))
print(planeNormals[neighborSegmentIndex] , dotThreshold)
# if (abs(np.dot(planeNormals[segmentIndex], neighborPoint) + planesD[segmentIndex]) < betweenRegionThreshold or abs(np.dot(planeNormals[neighborSegmentIndex], point) + planesD[neighborSegmentIndex]) < betweenRegionThreshold) and np.abs(np.dot(planeNormals[segmentIndex], planeNormals[neighborSegmentIndex])) < dotThreshold:
# validNeighborPixels.append(neighborPixel)
# pass
if np.linalg.norm(neighborPoint - point) < betweenRegionThreshold:
validNeighborPixels.append(neighborPixel)
pass
pass
else:
#print("reach1")
if np.linalg.norm(neighborPoint - point) < betweenRegionThreshold:
validNeighborPixels.append(neighborPixel)
pass
pass
pass
continue
if len(validNeighborPixels) == 3:
faces.append((x, y, x + 1, y + 1, x + 1, y))
faces.append((x, y, x, y + 1, x + 1, y + 1))
elif len(validNeighborPixels) == 2 and segmentIndex < numPlanes:
faces.append((x, y, validNeighborPixels[0][0], validNeighborPixels[0][1], validNeighborPixels[1][0], validNeighborPixels[1][1]))
pass
continue
continue
with open(folder + '/' + str(index) + '_model.ply', 'w') as f:
header = """ply
format ascii 1.0
comment VCGLIB generated
comment TextureFile """
header += imageFilename
header += """
element vertex """
header += str(width * height)
header += """
property float x
property float y
property float z
element face """
header += str(len(faces))
header += """
property list uchar int vertex_indices
property list uchar float texcoord
end_header
"""
f.write(header)
for y in range(height):
for x in range(width):
segmentIndex = segmentation[y][x]
if segmentIndex == -1:
f.write("0.0 0.0 0.0\n")
continue
point = XYZ[y][x]
X = point[0]
Y = point[1]
Z = point[2]
#Y = depth[y][x]
#X = Y / focalLength * (x - width / 2) / width * 640
#Z = -Y / focalLength * (y - height / 2) / height * 480
f.write(str(X) + ' ' + str(Z) + ' ' + str(-Y) + '\n')
continue
continue
for face in faces:
f.write('3 ')
for c in range(3):
f.write(str(face[c * 2 + 1] * width + face[c * 2]) + ' ')
continue
f.write('6 ')
for c in range(3):
f.write(str(float(face[c * 2]) / width) + ' ' + str(1 - float(face[c * 2 + 1]) / height) + ' ')
continue
f.write('\n')
continue
f.close()
pass
return
def evaluatePlanes(options):
for image_index in range(options.visualizeImages):
if options.applicationType == 'grids':
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_image.png', pred_dict['image'][image_index])
segmentation = predictions[0]['segmentation'][image_index]
#segmentation = np.argmax(np.concatenate([segmentation, pred_dict['np_mask'][image_index]], axis=2), -1)
segmentationImage = drawSegmentationImage(segmentation, blackIndex=options.numOutputPlanes)
#cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_segmentation_pred_' + str(0) + '.png', segmentationImage)
segmentationImageBlended = (segmentationImage * 0.7 + pred_dict['image'][image_index] * 0.3).astype(np.uint8)
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_segmentation_pred_blended_' + str(0) + '.png', segmentationImageBlended)
continue
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_image.png', pred_dict['image'][image_index])
info = pred_dict['info'][image_index]
for method_index, pred_dict in enumerate(predictions):
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_depth_pred_' + str(method_index) + '.png', drawDepthImage(pred_dict['depth'][image_index]))
if 'pixelwise' in options.methods[method_index][1]:
continue
allSegmentations = pred_dict['segmentation'][image_index]
segmentation = np.argmax(allSegmentations, axis=-1)
#segmentation = np.argmax(np.concatenate([segmentation, pred_dict['np_mask'][image_index]], axis=2), -1)
segmentationImage = drawSegmentationImage(segmentation, blackIndex=options.numOutputPlanes)
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_segmentation_pred_' + str(method_index) + '.png', segmentationImage)
segmentationImageBlended = (segmentationImage * 0.7 + pred_dict['image'][image_index] * 0.3).astype(np.uint8)
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_segmentation_pred_blended_' + str(method_index) + '.png', segmentationImageBlended)
segmentationImageBlended = np.minimum(segmentationImage * 0.3 + pred_dict['image'][image_index] * 0.7, 255).astype(np.uint8)
if options.imageIndex >= 0:
for planeIndex in range(options.numOutputPlanes):
cv2.imwrite(options.test_dir + '/mask_' + str(planeIndex) + '.png', drawMaskImage(segmentation == planeIndex))
continue
if options.applicationType == 'logo_video':
copyLogoVideo(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index], textureType='logo')
elif options.applicationType == 'wall_video':
if options.wallIndices == '':
print('please specify wall indices')
exit(1)
pass
wallIndices = [int(value) for value in options.wallIndices.split(',')]
copyLogoVideo(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index], textureType='wall', wallInds=wallIndices)
elif options.applicationType == 'ruler':
if options.startPixel == '' or options.endPixel == '':
print('please specify start pixel and end pixel')
exit(1)
pass
startPixel = tuple([int(value) for value in options.startPixel.split(',')])
endPixel = tuple([int(value) for value in options.endPixel.split(',')])
addRulerComplete(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index], startPixel=startPixel, endPixel=endPixel, fixedEndPoint=True, numFrames=1000)
elif options.applicationType == 'logo_texture':
resultImage = copyLogo(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index])
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_result.png', resultImage)
elif options.applicationType == 'wall_texture':
if options.wallIndices == '':
print('please specify wall indices')
exit(1)
pass
wallIndices = [int(value) for value in options.wallIndices.split(',')]
resultImage = copyWallTexture(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index], wallPlanes=wallIndices)
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_result.png', resultImage)
elif options.applicationType == 'TV':
if options.wallIndices == '':
print('please specify wall indices')
exit(1)
pass
wallIndices = [int(value) for value in options.wallIndices.split(',')]
copyLogoVideo(options.textureImageFilename, options.test_dir, image_index + options.startIndex, pred_dict['image'][image_index], pred_dict['depth'][image_index], pred_dict['plane'][image_index], segmentation, pred_dict['info'][image_index], textureType='TV', wallInds=wallIndices)
elif options.applicationType == 'pool':
print('dump')
newPlanes = []
newSegmentation = np.full(segmentation.shape, -1)
newPlaneIndex = 0
planes = pred_dict['plane'][image_index]
for planeIndex in range(options.numOutputPlanes):
mask = segmentation == planeIndex
if mask.sum() > 0:
newPlanes.append(planes[planeIndex])
newSegmentation[mask] = newPlaneIndex
newPlaneIndex += 1
pass
continue
np.save('pool/dump/' + str(image_index + options.startIndex) + '_planes.npy', np.stack(newPlanes, axis=0))
#print(global_gt['non_plane_mask'].shape)
np.save('pool/dump/' + str(image_index + options.startIndex) + '_segmentation.npy', newSegmentation)
cv2.imwrite('pool/dump/' + str(image_index + options.startIndex) + '_image.png', pred_dict['image'][image_index])
depth = pred_dict['depth'][image_index]
np.save('pool/dump/' + str(image_index + options.startIndex) + '_depth.npy', depth)
info = pred_dict['info'][image_index]
#normal = calcNormal(depth, info)
#np.save('test/' + str(image_index + options.startIndex) + '_normal.npy', normal)
np.save('pool/dump/' + str(image_index + options.startIndex) + '_info.npy', info)
exit(1)
else:
print('please specify application type')
np_mask = (segmentation == options.numOutputPlanes).astype(np.float32)
np_depth = pred_dict['np_depth'][image_index].squeeze()
np_depth = cv2.resize(np_depth, (np_mask.shape[1], np_mask.shape[0]))
cv2.imwrite(options.test_dir + '/' + str(image_index + options.startIndex) + '_np_depth_pred_' + str(method_index) + '.png', drawDepthImage(np_depth * np_mask))
# folder, \ - directory - done
# index, \ - idx number of image - done
# image, \ - segmentationImageBlended
# depth, \ - pred_dict['depth'][image_index] - done
# segmentation, \ - segmentation
# planes, \ - pred_dict['plane'][image_index]
# info - pred_dict['info'][image_index] - done
writePLYFile(options.test_dir, image_index + options.startIndex, segmentationImageBlended, pred_dict['depth'][image_index], segmentation, pred_dict['plane'][image_index], pred_dict['info'][image_index])
pass
exit(1)
pass
continue
continue
writeHTML(options)
return
if __name__=='__main__':
info = np.array([1.82e+03, 0.00e+00, 1.63e+03, 0.00e+00,\
0.00e+00, 1.82e+03, 1.22e+03, 0.00e+00, 0.00e+00, 0.00e+00, \
1.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 1.00e+00, 3.26e+03, 2.45e+03,\
1.00e+03,5.00e+00])
info[16] = 460
info[17] = 640
#image = cv2.imread("genrate_3dmodel/single_rgb_sample/12/12_segmentation_0_final.png") #x,x,3
#depth = cv2.imread("genrate_3dmodel/single_rgb_sample/12/12_depth_0_final_ori.png",0) #x,x
#segmentation = cv2.imread("genrate_3dmodel/single_rgb_sample/12/12_segmentation_0_final.png",0) #change it
# print("segmentation shape",segmentation.shape)
# cv2.imshow("seg1",segmentation)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
#planes = np.load("genrate_3dmodel/single_rgb_sample/12/12_plane_masks_0.npy") #change if its not working
image = cv2.imread("test/inference/0_segmentation_0_final.png") #x,x,3
depth = cv2.imread("test/inference/0_depth_0_final_ori.png",0) #x,x
segmentation = cv2.imread("test/inference/0_segmentation_0_final.png",0) #change it
# print("segmentation shape",segmentation.shape)
cv2.imshow("seg2",segmentation)
cv2.waitKey(0)
cv2.destroyAllWindows()
planes = np.load("test/inference/0_plane_masks_0.npy") #change if its not working
folder = "test2"
index = 0
predict3D(folder, index, image, depth, segmentation, planes, info)
#todo
# try to add focal length
# try to do with rgb based one