tests/test.sh exit code (0 → resolved); the classification below is post-hoc and cannot change it.cat /home/user/task/scenes/car.tscn; echo "---ASSETS---"; ls -la /home/user/task/assets/ 2>/dev/null; echo "---IMPORT---"; ls -la /home/user/task/assets/spritesheet_vehicles.png* 2>/dev/null
{
"description": "Read car.tscn and list assets"
}[gd_scene load_steps=1 format=3 uid="uid://codexcarsetupscene"] [node name="Car" type="CharacterBody2D"] ---ASSETS--- total 108 drwxr-xr-x 2 user user 4096 Jun 21 02:10 . drwxr-xr-x 6 user user 128 Jun 21 02:10 .. -rw-r--r-- 1 user user 102392 Jun 20 23:26 spritesheet_vehicles.png -rw-r--r-- 1 user user 800 Jun 21 02:10 spritesheet_vehicles.png.import ---IMPORT--- -rw-r--r-- 1 user user 102392 Jun 20 23:26 /home/user/task/assets/spritesheet_vehicles.png -rw-r--r-- 1 user user 800 Jun 21 02:10 /home/user/task/assets/spritesheet_vehicles.png.import [stdout] [gd_scene load_steps=1 format=3 uid="uid://codexcarsetupscene"] [node name="Car" type="CharacterBody2D"] ---ASSETS--- total 108 drwxr-xr-x 2 user user 4096 Jun 21 02:10 . drwxr-xr-x 6 user user 128 Jun 21 02:10 .. -rw-r--r-- 1 user user 102392 Jun 20 23:26 spritesheet_vehicles.png -rw-r--r-- 1 user user 800 Jun 21 02:10 spritesheet_vehicles.png.import ---IMPORT--- -rw-r--r-- 1 user user 102392 Jun 20 23:26 /home/user/task/assets/spritesheet_vehicles.png -rw-r--r-- 1 user user 800 Jun 21 02:10 /home/user/task/assets/spritesheet_vehicles.png.import
{
"file_path": "/home/user/task/assets/spritesheet_vehicles.png"
}{"type": "image", "source": {"type": "base64", "data": "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…[truncated]cat /home/user/task/assets/spritesheet_vehicles.png.import; echo "---DIM---"; python3 -c "from PIL import Image; im=Image.open('/home/user/task/assets/spritesheet_vehicles.png'); print(im.size)" 2>/dev/null || file /home/user/task/assets/spritesheet_vehicles.png
{
"description": "Read import file and image dimensions"
}Exit code 127
[remap]
importer="texture"
type="CompressedTexture2D"
uid="uid://4j3r3nq5e1ub"
path="res://.godot/imported/spritesheet_vehicles.png-e16a3162cc0edb8275366b9e44a79438.ctex"
metadata={
"vram_texture": false
}
[deps]
source_file="res://assets/spritesheet_vehicles.png"
dest_files=["res://.godot/imported/spritesheet_vehicles.png-e16a3162cc0edb8275366b9e44a79438.ctex"]
[params]
compress/mode=0
compress/high_quality=false
compress/lossy_quality=0.7
compress/hdr_compression=1
compress/normal_map=0
compress/channel_pack=0
mipmaps/generate=false
mipmaps/limit=-1
roughness/mode=0
roughness/src_normal=""
process/fix_alpha_border=true
process/premult_alpha=false
process/normal_map_invert_y=false
process/hdr_as_srgb=false
process/hdr_clamp_exposure=false
process/size_limit=0
detect_3d/compress_to=1
---DIM---
/bin/bash: line 1: file: command not found
[error] tool reported failurepython3 -c "
import struct
with open('/home/user/task/assets/spritesheet_vehicles.png','rb') as f:
d=f.read(33)
w,h=struct.unpack('>II', d[16:24])
print('size',w,h)
"
{
"description": "Parse PNG dimensions"
}size 1024 512 [stdout] size 1024 512
python3 -c "
import zlib, struct
f=open('/home/user/task/assets/spritesheet_vehicles.png','rb').read()
# parse chunks
i=8
W=H=0; bitd=ct=0; idat=b''
while i<len(f):
ln=struct.unpack('>I',f[i:i+4])[0]
typ=f[i+4:i+8]
data=f[i+8:i+8+ln]
if typ==b'IHDR':
W,H,bitd,ct=struct.unpack('>IIBB',data[:10])
elif typ==b'IDAT':
idat+=data
elif typ==b'IEND':
break
i+=12+ln
print('W,H,bitdepth,colortype',W,H,bitd,ct)
raw=zlib.decompress(idat)
ch=4 if ct==6 else 3
stride=W*ch
# unfilter
def paeth(a,b,c):
p=a+b-c; pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)
return a if pa<=pb and pa<=pc else (b if pb<=pc else c)
out=bytearray()
prev=bytearray(stride)
pos=0
for y in range(H):
ft=raw[pos]; pos+=1
line=bytearray(raw[pos:pos+stride]); pos+=stride
for x in range(stride):
a=line[x-ch] if x>=ch else 0
b=prev[x]
c=prev[x-ch] if x>=ch else 0
if ft==1: line[x]=(line[x]+a)&255
elif ft==2: line[x]=(line[x]+b)&255
elif ft==3: line[x]=(line[x]+((a+b)>>1))&255
elif ft==4: line[x]=(line[x]+paeth(a,b,c))&255
out+=line
prev=line
# alpha channel
import sys
alpha=[[out[(y*W+x)*ch+3] for x in range(W)] for y in range(H)]
# save alpha as simple bbox finder: find connected non-transparent columns/rows for top-left region
# print a coarse 32x16 grid of occupancy
gx,gy=32,16
cw=W//gx; chh=H//gy
for ry in range(gy):
row=''
for rx in range(gx):
s=0
for yy in range(ry*chh,(ry+1)*chh,4):
for xx in range(rx*cw,(rx+1)*cw,4):
if alpha[yy][xx]>20: s+=1
row+= ('#' if s>3 else '.')
print(row)
import pickle
pickle.dump((W,H,alpha),open('/tmp/alpha.pkl','wb'))
"
{
"description": "Decode PNG alpha and print occupancy grid"
}W,H,bitdepth,colortype 1024 512 8 6 #####################........... #####################........... ######################.......... ######################.......... ######################.......... ######################.......... #####################........... #####################........... #####################........... #####################........... #####################........... ######################.......... ######################.......... #####################........... ##################.##........... ##################.##........... [stdout] W,H,bitdepth,colortype 1024 512 8 6 #####################........... #####################........... ######################.......... ######################.......... ######################.......... ######################.......... #####################........... #####################........... #####################........... #####################........... #####################........... ######################.......... ######################.......... #####################........... ##################.##........... ##################.##...........
python3 -c "
import pickle
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
# find fully (or near) transparent columns in top band y 0..130 to find left sprite separators
def colgap(y0,y1):
res=[]
for x in range(W):
s=sum(1 for y in range(y0,y1) if alpha[y][x]>20)
res.append(s)
return res
# Overall column occupancy
cols=[sum(1 for y in range(H) if alpha[y][x]>20) for x in range(W)]
# print empty-ish column ranges
empty=[x for x in range(W) if cols[x]==0]
# group
def groups(lst):
g=[];
for v in lst:
if g and v==g[-1][-1]+1: g[-1].append(v)
else: g.append([v])
return [(gg[0],gg[-1]) for gg in g]
print('empty col groups:', groups(empty)[:40])
rows=[sum(1 for x in range(W) if alpha[y][x]>20) for y in range(H)]
emptyr=[y for y in range(H) if rows[y]==0]
print('empty row groups:', groups(emptyr)[:40])
"
{
"description": "Find transparent separator columns/rows"
}empty col groups: [(690, 1022)] empty row groups: [] [stdout] empty col groups: [(690, 1022)] empty row groups: []
python3 -c "
import pickle
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
def groups_lowcols(y0,y1,thresh):
cols=[sum(1 for y in range(y0,y1) if alpha[y][x]>20) for x in range(W)]
low=[x for x in range(min(700,W)) if cols[x]<=thresh]
g=[]
for v in low:
if g and v==g[-1][-1]+1: g[-1].append(v)
else: g.append([v])
return [(gg[0],gg[-1]) for gg in g]
# Top band only (first car row, y 0..128)
print('top band y0-128 low cols (gaps):', groups_lowcols(2,126,1))
# find row gaps for left column x 0..72
rows=[sum(1 for x in range(0,72) if alpha[y][x]>20) for y in range(H)]
low=[y for y in range(H) if rows[y]<=1]
g=[]
for v in low:
if g and v==g[-1][-1]+1: g[-1].append(v)
else: g.append([v])
print('left col x0-72 row gaps:', [(gg[0],gg[-1]) for gg in g])
"
{
"description": "Find low-occupancy gaps in top band and left column"
}top band y0-128 low cols (gaps): [(675, 699)] left col x0-72 row gaps: [(501, 510)] [stdout] top band y0-128 low cols (gaps): [(675, 699)] left col x0-72 row gaps: [(501, 510)]
python3 -c "
import pickle
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
cols=[sum(1 for y in range(2,126) if alpha[y][x]>20) for x in range(0,260)]
# print as compact numbers every column, marking low points
for x in range(0,260,1):
pass
# instead print local minima
line=''.join(str(min(9,cols[x]//13)) for x in range(0,260))
print(line)
"
{
"description": "Print column occupancy profile top band"
}24899999999999999999999999999999999999999999999999999999999999999999842222489999999999999999999999999999999999999999999999999999999999999999984222247788889999999999999999999999999999999999999999999999999999999888887422227888999999999999999999999999999999999999 [stdout] 24899999999999999999999999999999999999999999999999999999999999999999842222489999999999999999999999999999999999999999999999999999999999999999984222247788889999999999999999999999999999999999999999999999999999999888887422227888999999999999999999999999999999999999
python3 -c "
import pickle
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
cols=[sum(1 for y in range(2,126) if alpha[y][x]>20) for x in range(0,260)]
for x in range(60,90): print(x,cols[x])
print('---rows for x in 0..70 (find top car vertical bbox)---')
rows=[sum(1 for x in range(0,72) if alpha[y][x]>20) for y in range(0,150)]
for y in range(0,150):
if y<5 or rows[y]<10 or (130<y<150): print(y,rows[y])
"
{
"description": "Find exact minima and vertical bbox"
}60 124 61 124 62 123 63 123 64 122 65 121 66 120 67 118 68 115 69 61 70 35 71 35 72 31 73 31 74 64 75 115 76 117 77 120 78 121 79 122 80 123 81 123 82 124 83 124 84 124 85 124 86 124 87 124 88 124 89 124 ---rows for x in 0..70 (find top car vertical bbox)--- 0 27 1 46 2 53 3 57 4 59 131 26 132 27 133 27 134 46 135 53 136 57 137 59 138 61 139 63 140 64 141 65 142 67 143 67 144 67 145 69 146 69 147 69 148 69 149 69 [stdout] 60 124 61 124 62 123 63 123 64 122 65 121 66 120 67 118 68 115 69 61 70 35 71 35 72 31 73 31 74 64 75 115 76 117 77 120 78 121 79 122 80 123 81 123 82 124 83 124 84 124 85 124 86 124 87 124 88 124 89 124 ---rows for x in 0..70 (find top car vertical bbox)--- 0 27 1 46 2 53 3 57 4 59 131 26 132 27 133 27 134 46 135 53 136 57 137 59 138 61 139 63 140 64 141 65 142 67 143 67 144 67 145 69 146 69 147 69 148 69 149 69
python3 -c "
import pickle
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
x0,x1,y0,y1=0,73,0,131
minx=miny=10**9; maxx=maxy=-1
for y in range(y0,y1):
for x in range(x0,x1):
if alpha[y][x]>30:
minx=min(minx,x); maxx=max(maxx,x); miny=min(miny,y); maxy=max(maxy,y)
print('tight bbox: x',minx,maxx,'y',miny,maxy,'w',maxx-minx+1,'h',maxy-miny+1)
"
{
"description": "Tight bbox of top-left car"
}tight bbox: x 0 72 y 0 130 w 73 h 131 [stdout] tight bbox: x 0 72 y 0 130 w 73 h 131
{
"file_path": "/home/user/task/scenes/car.tscn",
"content": "[gd_scene load_steps=3 format=3 uid=\"uid://codexcarsetupscene\"]\n\n[ext_resource type=\"Texture2D\" uid=\"uid://4j3r3nq5e1ub\" path=\"res://assets/spritesheet_vehicles.png\" id=\"1_car\"]\n\n[sub_resource type=\"CapsuleShape2D\" id=\"CapsuleShape2D_car\"]\nradius = 30.0\nheight = 126.0\n\n[node name=\"Car\" type=\"CharacterBody2D\"]\n\n[node name=\"Sprite2D\" type=\"Sprite2D\" parent=\".\"]\nrotation = 1.5707964\ntexture = ExtResource(\"1_car\")\nregion_enabled = true\nregion_rect = Rect2(0, 0, 73, 131)\n\n[node name=\"CollisionShape2D\" type=\"CollisionShape2D\" parent=\".\"]\nrotation = 1.5707964\nshape = SubResource(\"CapsuleShape2D_car\")\n\n[node name=\"Camera2D\" type=\"Camera2D\" parent=\".\"]\n"
}<tool_use_error>File has not been read yet. Read it first before writing to it.</tool_use_error> [error] tool reported failure
{
"file_path": "/home/user/task/scenes/car.tscn"
}1 [gd_scene load_steps=1 format=3 uid="uid://codexcarsetupscene"] 2 3 [node name="Car" type="CharacterBody2D"] 4
{
"file_path": "/home/user/task/scenes/car.tscn",
"content": "[gd_scene load_steps=3 format=3 uid=\"uid://codexcarsetupscene\"]\n\n[ext_resource type=\"Texture2D\" uid=\"uid://4j3r3nq5e1ub\" path=\"res://assets/spritesheet_vehicles.png\" id=\"1_car\"]\n\n[sub_resource type=\"CapsuleShape2D\" id=\"CapsuleShape2D_car\"]\nradius = 30.0\nheight = 126.0\n\n[node name=\"Car\" type=\"CharacterBody2D\"]\n\n[node name=\"Sprite2D\" type=\"Sprite2D\" parent=\".\"]\nrotation = 1.5707964\ntexture = ExtResource(\"1_car\")\nregion_enabled = true\nregion_rect = Rect2(0, 0, 73, 131)\n\n[node name=\"CollisionShape2D\" type=\"CollisionShape2D\" parent=\".\"]\nrotation = 1.5707964\nshape = SubResource(\"CapsuleShape2D_car\")\n\n[node name=\"Camera2D\" type=\"Camera2D\" parent=\".\"]\n"
}The file /home/user/task/scenes/car.tscn has been updated successfully. (file state is current in your context , no need to Read it back)
python3 -c "
import pickle,struct,zlib
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
# reuse: re-decode RGB too for the crop preview
f=open('/home/user/task/assets/spritesheet_vehicles.png','rb').read()
i=8; idat=b''; ct=6
while i<len(f):
ln=struct.unpack('>I',f[i:i+4])[0]; typ=f[i+4:i+8]; data=f[i+8:i+8+ln]
if typ==b'IDAT': idat+=data
if typ==b'IEND': break
i+=12+ln
raw=zlib.decompress(idat); ch=4; stride=W*ch
def paeth(a,b,c):
p=a+b-c; pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)
return a if pa<=pb and pa<=pc else (b if pb<=pc else c)
out=bytearray(); prev=bytearray(stride); pos=0
for y in range(H):
ft=raw[pos]; pos+=1; line=bytearray(raw[pos:pos+stride]); pos+=stride
for x in range(stride):
a=line[x-ch] if x>=ch else 0; b=prev[x]; c=prev[x-ch] if x>=ch else 0
if ft==1: line[x]=(line[x]+a)&255
elif ft==2: line[x]=(line[x]+b)&255
elif ft==3: line[x]=(line[x]+((a+b)>>1))&255
elif ft==4: line[x]=(line[x]+paeth(a,b,c))&255
out+=line; prev=line
# crop region 0,0,73,131 -> write png
rx,ry,rw,rh=0,0,73,131
def png(wd,ht,pix):
sig=b'\x89PNG\r\n\x1a\n'
def chunk(t,d): return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)
ihdr=struct.pack('>IIBBBBB',wd,ht,8,6,0,0,0)
rawd=bytearray()
for y in range(ht):
rawd.append(0); rawd+=pix[y*wd*4:(y+1)*wd*4]
return sig+chunk(b'IHDR',ihdr)+chunk(b'IDAT',zlib.compress(bytes(rawd),9))+chunk(b'IEND',b'')
crop=bytearray()
for y in range(ry,ry+rh):
for x in range(rx,rx+rw):
o=(y*W+x)*4; crop+=out[o:o+4]
open('/tmp/crop.png','wb').write(png(rw,rh,crop))
print('wrote /tmp/crop.png', rw, rh)
"
{
"description": "Export the cropped region for verification"
}wrote /tmp/crop.png 73 131 [stdout] wrote /tmp/crop.png 73 131
{
"file_path": "/tmp/crop.png"
}{"type": "image", "source": {"type": "base64", "data": "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", "media_type": "image/png"}}
python3 -c "
import pickle,struct,zlib
W,H,alpha=pickle.load(open('/tmp/alpha.pkl','rb'))
f=open('/home/user/task/assets/spritesheet_vehicles.png','rb').read()
i=8; idat=b''
while i<len(f):
ln=struct.unpack('>I',f[i:i+4])[0]; typ=f[i+4:i+8]; data=f[i+8:i+8+ln]
if typ==b'IDAT': idat+=data
if typ==b'IEND': break
i+=12+ln
raw=zlib.decompress(idat); ch=4; stride=W*ch
def paeth(a,b,c):
p=a+b-c; pa=abs(p-a); pb=abs(p-b); pc=abs(p-c)
return a if pa<=pb and pa<=pc else (b if pb<=pc else c)
out=bytearray(); prev=bytearray(stride); pos=0
for y in range(H):
ft=raw[pos]; pos+=1; line=bytearray(raw[pos:pos+stride]); pos+=stride
for x in range(stride):
a=line[x-ch] if x>=ch else 0; b=prev[x]; c=prev[x-ch] if x>=ch else 0
if ft==1: line[x]=(line[x]+a)&255
elif ft==2: line[x]=(line[x]+b)&255
elif ft==3: line[x]=(line[x]+((a+b)>>1))&255
elif ft==4: line[x]=(line[x]+paeth(a,b,c))&255
out+=line; prev=line
def png(wd,ht,pix):
sig=b'\x89PNG\r\n\x1a\n'
def chunk(t,d): return struct.pack('>I',len(d))+t+d+struct.pack('>I',zlib.crc32(t+d)&0xffffffff)
ihdr=struct.pack('>IIBBBBB',wd,ht,8,6,0,0,0)
rawd=bytearray()
for y in range(ht):
rawd.append(0); rawd+=pix[y*wd*4:(y+1)*wd*4]
return sig+chunk(b'IHDR',ihdr)+chunk(b'IDAT',zlib.compress(bytes(rawd),9))+chunk(b'IEND',b'')
rx,ry,rw,rh=0,0,70,131
crop=bytearray()
for y in range(ry,ry+rh):
for x in range(rx,rx+rw):
o=(y*W+x)*4; crop+=out[o:o+4]
open('/tmp/crop2.png','wb').write(png(rw,rh,crop)); print('ok',rw,rh)
"
{
"description": "Re-crop at width 70"
}ok 70 131 [stdout] ok 70 131
{
"file_path": "/tmp/crop2.png"
}{"type": "image", "source": {"type": "base64", "data": "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", "media_type": "image/png"}}
{
"replace_all": false,
"file_path": "/home/user/task/scenes/car.tscn",
"old_string": "region_rect = Rect2(0, 0, 73, 131)",
"new_string": "region_rect = Rect2(0, 0, 70, 131)"
}The file /home/user/task/scenes/car.tscn has been updated successfully. (file state is current in your context , no need to Read it back)
No file changes, the agent left the workspace untouched.
Godot Engine v4.4.stable.official.4c311cbee - https://godotengine.org
WARNING: Started the engine as `root`/superuser. This is a security risk, and subsystems like audio may not work correctly.
Set the environment variable `GODOT_SILENCE_ROOT_WARNING` to 1 to silence this warning.
at: setup2 (main/main.cpp:3181)
VALIDATION_FAILED: Wrong car selected , find the correct forward-facing vertical car body in the spritesheetReproduce this trial: git checkout 2f94510 && PYTHONPATH=src python3 scripts/build_site.py , then open trial/trial_3de822d4cc7d4fbc. Re-running the agent live requires EVAL_PLATFORM_ENABLE_OAUTH_SMOKE=1 and is non-deterministic.
Trial trial_3de822d4cc7d4fbc · verifier authoritative; classifier explanatory.