Merge branch 'feat_testing' into dev_refactor

stimuli/papers/RHS2007.py

@@ -39,6 +39,28 @@
 PPD = 32
 
 
+def gen_all(ppd=PPD, skip=False):
+    stims = {}  # save the stimulus-dicts in a larger dict, with name as key
+    for stim_name in __all__:
+        print(f"Generating RHS2007.{stim_name}")
+
+        # Get a reference to the actual function
+        func = globals()[stim_name]
+        try:
+            stim = func()
+
+            # Accumulate
+            stims[stim_name] = stim
+        except NotImplementedError as e:
+            if not skip:
+                raise e
+            # Skip stimuli that aren't implemented
+            print("-- not implemented")
+            pass
+
+    return stims
+
+
 def WE_thick(ppd=PPD):
     height, width = 12.0, 16.0
     n_cycles = 4.0
@@ -660,12 +682,6 @@ def bullseye_thick(ppd=PPD):
 if __name__ == "__main__":
     from stimuli.utils import plot_stimuli
 
-    stims = {}
-    for stimname in __all__:
-        print("Generating " + stimname)
-        try:
-            stims[stimname] = globals()[stimname]()
-        except NotImplementedError:
-            print("-- not implemented")
+    stims = gen_all(skip=True)
 
     plot_stimuli(stims, mask=False)

stimuli/papers/__init__.py

@@ -1 +1 @@
-from . import RHS2007, domijan2015, murray2020
+__all__ = ["RHS2007", "domijan2015", "murray2020"]

stimuli/papers/domijan2015.py

@@ -19,6 +19,28 @@
 PPD = 10
 
 
+def gen_all(ppd=PPD, skip=False):
+    stims = {}  # save the stimulus-dicts in a larger dict, with name as key
+    for stim_name in __all__:
+        print(f"Generating domijan2015.{stim_name}")
+
+        # Get a reference to the actual function
+        func = globals()[stim_name]
+        try:
+            stim = func()
+
+            # Accumulate
+            stims[stim_name] = stim
+        except NotImplementedError as e:
+            if not skip:
+                raise e
+            # Skip stimuli that aren't implemented
+            print("-- not implemented")
+            pass
+
+    return stims
+
+
 def dungeon(ppd=PPD):
     return illusions.dungeon.dungeon_illusion(
         ppd=ppd,
@@ -222,12 +244,6 @@ def checkerboard_extended(ppd=PPD):
 if __name__ == "__main__":
     from stimuli.utils import plot_stimuli
 
-    stims = {}
-    for stimname in __all__:
-        print("Generating " + stimname)
-        try:
-            stims[stimname] = globals()[stimname]()
-        except NotImplementedError:
-            print("-- not implemented")
+    stims = gen_all(skip=True)
 
     plot_stimuli(stims, mask=False)

stimuli/papers/murray2020.py

@@ -59,6 +59,28 @@
 mat_content = scipy.io.loadmat(mat_fname)
 
 
+def gen_all(skip=False):
+    stims = {}  # save the stimulus-dicts in a larger dict, with name as key
+    for stim_name in __all__:
+        print(f"Generating murray2020.{stim_name}")
+
+        # Get a reference to the actual function
+        func = globals()[stim_name]
+        try:
+            stim = func()
+
+            # Accumulate
+            stims[stim_name] = stim
+        except NotImplementedError as e:
+            if not skip:
+                raise e
+            # Skip stimuli that aren't implemented
+            print("-- not implemented")
+            pass
+
+    return stims
+
+
 def get_mask(target1, target2, shape):
     mask = np.zeros(shape)
 
@@ -322,12 +344,7 @@ def white():
 if __name__ == "__main__":
     from stimuli.utils import plot_stimuli
 
-    stims = {}
-    for stimname in __all__:
-        print("Generating " + stimname)
-        try:
-            stims[stimname] = globals()[stimname]()
-        except NotImplementedError:
-            print("-- not implemented")
+    # Generate all stimuli exported in __all__
+    stims = gen_all(skip=True)
 
     plot_stimuli(stims, mask=False)

stimuli/utils/export.py

@@ -0,0 +1,18 @@
+import json
+from hashlib import md5
+
+import numpy as np
+
+
+def arrs_to_checksum(stim, keys=["img", "mask"]):
+    # Hash (md5) values, and save only the hex
+    for key in keys:
+        stim[key] = md5(np.ascontiguousarray(stim[key])).hexdigest()
+
+    return stim
+
+
+def to_json(stim, filename):
+    # stimulus-dict(s) as (pretty) JSON
+    with open(filename, "w", encoding="utf-8") as f:
+        json.dump(stim, f, ensure_ascii=False, indent=4)

tests/papers/RHS2007.json

@@ -0,0 +1,90 @@
+{
+    "WE_thick": {
+        "img": "4e6d0130b6f37eeaff7f568c31c68325",
+        "mask": "b523788d266b5f7ded635717acad5a54"
+    },
+    "WE_thin_wide": {
+        "img": "d0540819930d454dd272f9ee91739114",
+        "mask": "82738b790feeb3d762822cf13136abd5"
+    },
+    "WE_dual": {
+        "img": "0eb7042718b74b8973d8e945f0454867",
+        "mask": "88a5faacb4e5db2d7f5761066dc92f38"
+    },
+    "WE_anderson": {
+        "img": "34284f8560d01e2231fe30b9362e8577",
+        "mask": "ec5671221e98b96b6b35622424266eaf"
+    },
+    "WE_howe": {
+        "img": "f68a1210ae539a0dd4976bc8eaa701a8",
+        "mask": "c05d4411f96114b3b7ffe73254ff2875"
+    },
+    "WE_radial_thick_small": {
+        "img": "b92d533177eed4a009625d6d2cb0da59",
+        "mask": "78da2d5ec63b48e53f5bdbd90d2b82b9"
+    },
+    "WE_radial_thick": {
+        "img": "d0067dfb57016354f7b107ae410d217c",
+        "mask": "482a1a903b54df01f3e16cb284b8bfeb"
+    },
+    "WE_radial_thin_small": {
+        "img": "94e6b546f8ad5c6b95a502a8b409c00d",
+        "mask": "38a4da8d63237ac33b1a7f5e1cd021ec"
+    },
+    "WE_circular1": {
+        "img": "2ecc5cb5ad9cfef50de681ee3b923f46",
+        "mask": "32f876b2d56ce7a9f712f7784dc60289"
+    },
+    "WE_circular05": {
+        "img": "5d09c011b57d3afffcaf82e7aea84b34",
+        "mask": "dde7cc1ee48b2d47b37fbe9422e0f677"
+    },
+    "WE_circular025": {
+        "img": "ec3feee88c49aed0a5554c3838be8519",
+        "mask": "e4e9c650c9a247180211dbcde0e38fb2"
+    },
+    "grating_induction": {
+        "img": "da62004fdc198660b44529f8b6358362",
+        "mask": "40f02639e692658b9f357b09c11e9df1"
+    },
+    "sbc_large": {
+        "img": "8c1d036742bdf01159177f42625fe144",
+        "mask": "6f1f89e7d2a6d1b88c29c051ef3a2dd0"
+    },
+    "sbc_small": {
+        "img": "ae061d71fede147fcc8e7680e14e82c9",
+        "mask": "f4f124cbfae47fd81b7d398c09836495"
+    },
+    "todorovic_equal": {
+        "img": "caecfb4065b54222e8d810e00da685af",
+        "mask": "2821bec9b23503e1598696d0ab418f3b"
+    },
+    "todorovic_in_large": {
+        "img": "ba9c3382426f8e0e9864d4b999c7979c",
+        "mask": "265174ba6cc11c8a90fe9c822b18dfeb"
+    },
+    "todorovic_in_small": {
+        "img": "0ab88dbeb1419b4b786134cc432d73d7",
+        "mask": "a711607fc494557ce2b52e984dc103b4"
+    },
+    "checkerboard_016": {
+        "img": "cffb926181df5cea95608b95bbc163f1",
+        "mask": "1eb12e597ac5c454830dc2873730ccc1"
+    },
+    "checkerboard_0938": {
+        "img": "ffe0d07c218fa56eecce2a9386908ff5",
+        "mask": "845b86fd98823fc4778f76a360da6d52"
+    },
+    "checkerboard209": {
+        "img": "3058e7c3d03899760fede8aba1c940ac",
+        "mask": "1172e323e0af65b431a4641be9836b7b"
+    },
+    "bullseye_thin": {
+        "img": "59c97cb1feffb4c9c502b143c5699fb3",
+        "mask": "08cfee34814f232afda08e138da10661"
+    },
+    "bullseye_thick": {
+        "img": "59c97cb1feffb4c9c502b143c5699fb3",
+        "mask": "08cfee34814f232afda08e138da10661"
+    }
+}

tests/papers/domijan2015.json

@@ -0,0 +1,50 @@
+{
+    "dungeon": {
+        "img": "23859250c9f5a895de48c20e7095f4e9",
+        "mask": "1954462ad5eeb864935b349630f918f9"
+    },
+    "cube": {
+        "img": "59ac0095ba9c17a2cdd98ffff6613447",
+        "mask": "3a6543daa69e82047d0b84b76a446312"
+    },
+    "grating": {
+        "img": "44286e04ca4f44fe98930722c0d45b1a",
+        "mask": "cb796a47e9ca6c8426a2585c9b604989"
+    },
+    "rings": {
+        "img": "38a7bc014428864fffcee590d0953465",
+        "mask": "3c5c395de3b4cae5b6956314624ea012"
+    },
+    "bullseye": {
+        "img": "e30fb79cf7897dfed4ce04983b36ba28",
+        "mask": "721937af54e313931f38a22b7a6ce885"
+    },
+    "simultaneous_brightness_contrast": {
+        "img": "a0e0353cd1fb9a829241ddecc51fbc64",
+        "mask": "c46a87aa52c48192ce96c90fbe10c062"
+    },
+    "white": {
+        "img": "377a2903002d526da2ba1d052eb193d2",
+        "mask": "306423f9e89e6d3bd21a946881ef684a"
+    },
+    "benary": {
+        "img": "4c3f2278ac91effebf1db9f580a24db6",
+        "mask": "c790aa14edddb57f8054a4f330c7ddc6"
+    },
+    "todorovic": {
+        "img": "1b9d5b09475aace9b97f599c5731177b",
+        "mask": "2eeeeb428daf6be031c64ef841e7c897"
+    },
+    "checkerboard_contrast_contrast": {
+        "img": "246c282926e1a0b390eb4bca6bd00b16",
+        "mask": "487c5cbe9dfd08719ead98ea9a7e901c"
+    },
+    "checkerboard": {
+        "img": "6891d301caf87043b99c199f13fdf607",
+        "mask": "70844521cd62275df0cab9a7c1184d9d"
+    },
+    "checkerboard_extended": {
+        "img": "570e9597fa49a33a6e96687a007ad3e0",
+        "mask": "f6ee08aeab807602449271ee7367cc44"
+    }
+}

tests/papers/gen_ground_truth.py

@@ -0,0 +1,22 @@
+from os.path import abspath, dirname
+
+from stimuli.papers import *
+from stimuli.papers import __all__ as papers
+from stimuli.utils import export
+
+d = dirname(abspath(__file__))
+
+# Generate the ground_truth dict for each paper; save as .JSON
+for paper in papers:
+    # Get a reference to the actual module, from the name-string
+    paper_module = globals()[paper]
+
+    # Generate all the stimulus-dicts (skip over NotImplemented)
+    stims = paper_module.gen_all(skip=True)
+
+    # Convert "img", "mask" to checksums
+    for stim in stims.values():
+        export.arrs_to_checksum(stim, ["img", "mask"])
+
+    # Save all as .JSON
+    export.to_json(stims, f"{d}/{paper}.json")

tests/papers/murray2020.json

@@ -0,0 +1,50 @@
+{
+    "argyle": {
+        "img": "cb4ed25c9e5d878c4daf41a57656e4ba",
+        "mask": "b9c1760d980dc13c5bb2fda8ebe537c2"
+    },
+    "argyle_control": {
+        "img": "e3e1e33d7eb3f1e4ea2c0100e551b3e6",
+        "mask": "91dbc28e28a6209bcd187a11e0321cae"
+    },
+    "argyle_long": {
+        "img": "ce09cf6536bc04037536c767c973ef9c",
+        "mask": "83ffc7391ddcbebfe6cec7b0944c6dc0"
+    },
+    "snake": {
+        "img": "06337656c123eeb36dd75963639adaa2",
+        "mask": "02636e9e6289b9ae0bb36e518fc20e3f"
+    },
+    "snake_control": {
+        "img": "3473bf614bdf0eb68b4c7c8ce1b29697",
+        "mask": "02636e9e6289b9ae0bb36e518fc20e3f"
+    },
+    "koffka_adelson": {
+        "img": "16f09cb37a170053a73f5183f289b6df",
+        "mask": "ecf8713ae03549fa9ee16b1462ff5bf1"
+    },
+    "koffka_broken": {
+        "img": "f710a7d1ceb75d1f6cac9d91e1c2a821",
+        "mask": "40c249e4d8835580b7f57d04e0df65c4"
+    },
+    "koffka_connected": {
+        "img": "9ffb69c20afbaf731877b4626d61c404",
+        "mask": "4e4fa0cbe12df9fa7bdab6b0fc073270"
+    },
+    "checkassim": {
+        "img": "ef5b9b410280b3f2e2ff45ee165e32d7",
+        "mask": "4bc92371e105629f30513c1050f99cf4"
+    },
+    "simcon": {
+        "img": "87da2163dcd4ce3ef214c48fa243c787",
+        "mask": "99fbc73e5c8aefd70501838130b806e9"
+    },
+    "simcon_articulated": {
+        "img": "61685f75a7352ff756d566a6ffcc8faf",
+        "mask": "99fbc73e5c8aefd70501838130b806e9"
+    },
+    "white": {
+        "img": "0fb691c397d4bdcbb0eb5f89134be5aa",
+        "mask": "b0f4ec00fd2d12d15f005af9b53b29f8"
+    }
+}