add function for creating a multiview dataset and stitching these ima…

…ges together

data/badspecimens_multi.csv

@@ -0,0 +1,47 @@
+Catalog_number
+jtlc000007882
+casent0903570
+focol2958
+casent0916813
+focol2797
+casent0101417
+casent0102532
+casent0901925
+casent0101879
+casent0101951
+casent0911777
+casent0910170
+casent0903586
+casent0903589
+casent0905327
+focol2959
+casent0902084
+jtlc000002495
+casent0132820
+casent0101934
+casent0916815
+casent0916821
+casent0911308
+casent0901683
+casent0912507
+casent0178536
+focol2208
+casent0907373
+casent0010821
+casent0010882
+casent0102292
+casent0922207
+focol1383
+focol1384
+casent0902845
+casent0907478
+castype06976
+casent0922570
+casent0913604
+casent0915417
+antweb1038244
+casent0137344
+casent0912542
+casent0906517
+inbiocri002280277
+casent0913605

formicID/AntWeb/json_to_csv.py

@@ -90,7 +90,7 @@ def _filter_json(json_file, quality, multi_only):
             scientific_name = row[1]
             image_url = {}
             if multi_only is True:
-                if ("d" in row[2] and "h" in row[2] and "p" in row[2]):
+                if "d" in row[2] and "h" in row[2] and "p" in row[2]:
                     image_url["d"] = row[2]["d"]["img"][qlty]
                     image_url["h"] = row[2]["h"]["img"][qlty]
                     image_url["p"] = row[2]["p"]["img"][qlty]
@@ -118,7 +118,12 @@ def _filter_json(json_file, quality, multi_only):
 
 
 def batch_json_to_csv(
-    csvname, dataset, quality="low", output_dir=None, overwrite=False, multi_only=False
+    csvname,
+    dataset,
+    quality="low",
+    output_dir=None,
+    overwrite=False,
+    multi_only=False,
 ):
     """Creates a csvfile, from a batch of json files, filling it with all the
     relevant information for downloading images and naming the files.

formicID/configs/config.json

@@ -1,14 +1,14 @@
 {
-    "exp_name": "Multi_test",
+    "exp_name": "T97_CaAll_QuM_ShAll_AugM_D05_LR0001_E200_I4_def_clean",
     "data_set": "top97species_Qmed_def_clean_multi",
     "batch_size": 32,
     "dropout": 0.5,
     "learning_rate": 0.001,
     "model": "InceptionResNetV2",
-    "num_epochs": 5,
+    "num_epochs": 200,
     "optimizer": "Nadam",
     "seed": 1,
     "test_split": 0.1,
     "val_split": 0.2,
-    "shottype": "head"
+    "shottype": "stitched"
 }

formicID/data_loader/data_input.py

@@ -188,13 +188,11 @@ def split_in_directory(config, bad=None):
         random.shuffle(shuffled)
         num1 = round(len(shuffled) * test_split)
         num2 = round(len(shuffled) * val_split)
-        # fmt: off
         to_test, to_val, to_train = (
             shuffled[:num1],
             shuffled[num1:num2],
             shuffled[num2:],
         )
-        # fmt: on
         for image in os.listdir(os.path.join(input_dir, species)):
             if image.endswith(".jpg"):
                 for img in to_test: