Phaser.js - 仅在块顶部发生碰撞

Question

我从JSON.file加载到我的Phaser.game一个tilemap。不幸的是，我的播放器精灵只会在加载的地图块/磁贴上碰撞。

我附上了更好地描述问题的代码和图片。

红线== nocollision，蓝线==玩家碰撞

I1103 12:01:41.822055 108615 solver.cpp:337] Iteration 0, Testing net (#0)
I1103 12:01:41.849742 108615 solver.cpp:404]     Test net output #0: accuracy = 0
I1103 12:01:41.849761 108615 solver.cpp:404]     Test net output #1: loss = 6.02617 (* 1 = 6.02617 loss)
I1103 12:01:41.869380 108615 solver.cpp:228] Iteration 0, loss = 6.05644
I1103 12:01:41.869398 108615 solver.cpp:244]     Train net output #0: loss = 6.05644 (* 1 = 6.05644 loss)
I1103 12:01:41.869413 108615 sgd_solver.cpp:106] Iteration 0, lr = 0.1
I1103 12:01:47.624855 108615 solver.cpp:228] Iteration 500, loss = 87.3365
I1103 12:01:47.624876 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:01:47.624882 108615 sgd_solver.cpp:106] Iteration 500, lr = 0.1
I1103 12:01:53.290213 108615 solver.cpp:337] Iteration 1000, Testing net (#0)
I1103 12:01:53.299310 108615 solver.cpp:404]     Test net output #0: accuracy = 0
I1103 12:01:53.299327 108615 solver.cpp:404]     Test net output #1: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:01:53.314584 108615 solver.cpp:228] Iteration 1000, loss = 87.3365
I1103 12:01:53.314615 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:01:53.314621 108615 sgd_solver.cpp:106] Iteration 1000, lr = 0.01
I1103 12:01:58.991268 108615 solver.cpp:228] Iteration 1500, loss = 87.3365
I1103 12:01:58.991315 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:01:58.991322 108615 sgd_solver.cpp:106] Iteration 1500, lr = 0.01
I1103 12:02:04.664419 108615 solver.cpp:337] Iteration 2000, Testing net (#0)
I1103 12:02:04.673518 108615 solver.cpp:404]     Test net output #0: accuracy = 0
I1103 12:02:04.673537 108615 solver.cpp:404]     Test net output #1: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:02:04.690434 108615 solver.cpp:228] Iteration 2000, loss = 87.3365
I1103 12:02:04.690469 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:02:04.690481 108615 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I1103 12:02:10.373788 108615 solver.cpp:228] Iteration 2500, loss = 87.3365
I1103 12:02:10.373852 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:02:10.373859 108615 sgd_solver.cpp:106] Iteration 2500, lr = 0.001
I1103 12:02:16.047372 108615 solver.cpp:337] Iteration 3000, Testing net (#0)
I1103 12:02:16.056390 108615 solver.cpp:404]     Test net output #0: accuracy = 0
I1103 12:02:16.056407 108615 solver.cpp:404]     Test net output #1: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:02:16.070235 108615 solver.cpp:228] Iteration 3000, loss = 87.3365
I1103 12:02:16.070261 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
I1103 12:02:16.070267 108615 sgd_solver.cpp:106] Iteration 3000, lr = 0.0001
I1103 12:02:21.755348 108615 solver.cpp:228] Iteration 3500, loss = 87.3365
I1103 12:02:21.755369 108615 solver.cpp:244]     Train net output #0: loss = 87.3365 (* 1 = 87.3365 loss)
 I1103 12:02:21.755375 108615 sgd_solver.cpp:106] Iteration 3500, lr = 0.0001
 ----------------------------------

net: "/A/B/train.prototxt"
test_iter: 10
test_interval: 1000
base_lr: 0.1
lr_policy: "step"
gamma: 0.1
stepsize: 1000
display: 10
max_iter: 4000
momentum: 0.9
weight_decay: 0.0005
snapshot: 1000
snapshot_prefix: "/A/B/model_"
solver_mode: GPU
--------------------------------------------------

layer {
name: "data"
type: "HDF5Data"
top: "X"
top: "y"
hdf5_data_param{
source:"/Path/to/trainh5list.txt"
batch_size: 1
}
include{phase: TRAIN}
}
layer {
name: "data"
type: "HDF5Data"
top: "X"
top: "y"
hdf5_data_param{
source:"/Path/to/testh5list.txt"
batch_size: 1
}
include{phase: TEST}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "X"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
kernel_size: 11
stride: 4
weight_filler {
  type: "gaussian"
  std: 0.01
}
bias_filler {
  type: "constant"
  value: 0
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "norm1"
type: "LRN"
bottom: "pool1"
top: "norm1"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
.
.
.
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "drop7"
type: "Dropout"
bottom: "fc7"
top: "fc7"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc7"
top: "fc8"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
  type: "gaussian"
  std: 0.01
}
bias_filler {
  type: "constant"
  value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "y"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "y"
top: "loss"
}