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test_perception_performance.py
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test_perception_performance.py
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import h5py, cv2, sys, pickle, math, numpy
import numpy as np
sys.path.append("/data/yang/code/aws")
from all_perceptions import Perceptions
class SegPerformance():
def __init__(self):
self.p = Perceptions(det_COCO=False,
det_TL=False,
det_TS=False,
seg=True,
depth=False,
batch_size=1, # batch_size could also be a dict
gpu_assignment=[0],
compute_methods={},
viz_methods={},
num_replicates={},
path_config="path_jormungandr")
self.accu = np.zeros((6, 2), dtype=np.int64)
def _compute_inter_union(self, gt, pred, nclasses):
# return a nclass * 2 matrix: [iclass][inter, union]
out = np.zeros((nclasses, 2), dtype=np.int64)
pred = pred[gt != 0] - 1
gt = gt[gt != 0] - 1
for i in range(nclasses):
out[i, 0] = np.sum(np.logical_and(pred == i, gt == i))
out[i, 1] = np.sum(np.logical_or(pred == i, gt == i))
return out
def reset(self):
self.accu = np.zeros((6, 2), dtype=np.int64)
def one_pair(self, image, seg):
'''
0 None
1 Buildings
2 Fences
3 Other
4 Pedestrians
5 Poles
6 RoadLines
7 Roads
8 Sidewalks
9 Vegetation
10 Vehicles
11 Walls
12 TrafficSigns
# 0 1 2 3 4 5 6
{'Ignored', 'Movable', 'Navigable', 'NoneNavigable', 'StaticLayout', 'Sky', 'Lane'}
'''
image_batch = np.expand_dims(image, 0)
out = self.p.compute(image_batch)
out = np.squeeze(out['seg'])
out = np.argmax(out, axis=2) + 1 # 0 does not exists, since it is the ignore class underhood
out = cv2.resize(out, (out.shape[1] * 4, out.shape[0] * 4), interpolation=cv2.INTER_NEAREST)
mapping = np.array([0, 4, 3, 0, 1, 4, 6, 2, 3, 4, 1, 4, 4])
seg_cvt = mapping[seg]
self.accu += self._compute_inter_union(seg_cvt, out, 6)
def summarize(self):
names = ['Movable', 'Navigable', 'NoneNavigable', 'StaticLayout', 'Sky', 'Lane']
out = {}
for i in range(len(names)):
out[names[i]] = 1.0 * self.accu[i, 0] / self.accu[i, 1]
return out
class DepthPerformance():
def __init__(self):
width = 768
fov = 90.0
self.focal = width / (2.0 * math.tan(fov * math.pi / 360.0))
self.baseline = 0.54
self.p = Perceptions(det_COCO=False,
det_TL=False,
det_TS=False,
seg=False,
depth=True,
batch_size=1, # batch_size could also be a dict
gpu_assignment=[0],
compute_methods={},
viz_methods={},
num_replicates={},
path_config="path_jormungandr")
self.reset()
def _compute_errors(self, gt, pred):
thresh = np.maximum((gt / pred), (pred / gt))
a1 = (thresh < 1.25).mean()
a2 = (thresh < 1.25 ** 2).mean()
a3 = (thresh < 1.25 ** 3).mean()
rmse = (gt - pred) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(gt) - np.log(pred)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
abs_rel = np.mean(np.abs(gt - pred) / gt)
sq_rel = np.mean(((gt - pred) ** 2) / gt)
return abs_rel, sq_rel, rmse, rmse_log, a1, a2, a3
def reset(self):
self.accu = []
self.count = 0
def one_pair(self, image, depth):
image_batch = np.expand_dims(image, 0)
out = self.p.compute(image_batch)
out = np.squeeze(out['depth'])
out = cv2.resize(out, (image.shape[1], image.shape[0]), interpolation=cv2.INTER_LINEAR)
out = self.baseline * self.focal / (out + 0.0000001)
out = out / 2000.0 # some magic number that I made up
errors = self._compute_errors(depth + 1e-5, out + 1e-5)
self.accu.append(errors)
self.count += 1
def summarize(self):
names = ['abs_rel', 'sq_rel', 'rmse', 'rmse_log', 'a1', 'a2', 'a3']
errors = np.stack(self.accu, axis=0)
errors = np.mean(errors, 0)
out = {}
for i in range(len(names)):
out[names[i]] = errors[i]
return out
modality = "depth" # "seg"
perf = {"depth": DepthPerformance, "seg": None}[modality]()
results = {}
for town in ["Town01", "Town02"]:
results[town] = {}
for weather in range(1, 14):
perf.reset()
path = "/scratch/yang/aws_data/carla_collect/" + town + "_allsensor/default_RotationPitch=0_WeatherId=" + str(
weather).zfill(2) + "/data_00000.h5"
f = h5py.File(path, "r")
for i in range(200):
# print(i)
image = f['CameraMiddle'][i]
image = cv2.imdecode(image, 1)
image = image[:, :, ::-1]
if modality == "seg":
seg = f['SegMiddle'][i]
mode_image = cv2.imdecode(seg, 0)
elif modality == "depth":
mode_image = f['DepthMiddle'][i]
mode_image = cv2.imdecode(mode_image, 1)
def to_depth(array):
# input is bgr
array = array.astype(numpy.float32)
# Apply (R + G * 256 + B * 256 * 256) / (256 * 256 * 256 - 1).
normalized_depth = numpy.dot(array[:, :, :], [65536.0, 256.0, 1.0])
normalized_depth /= 16777215.0 / 1000.0 # (256.0 * 256.0 * 256.0 - 1.0)
return normalized_depth
mode_image = to_depth(mode_image)
else:
raise
perf.one_pair(image, mode_image)
f.close()
print(town, "weather", weather, perf.summarize())
results[town][weather] = perf.summarize()
with open(modality + ".pkl", "w") as ppp:
pickle.dump(results, ppp)
print("done")