inference.py will be rewritten to really infere images without csv files

README.md

@@ -24,8 +24,8 @@ Use the script **./data/train_test_split.py** to split the original .csv file in
 training and one for testing (both also stored in ./data/dataset).
 
 ### ToDos:
-* FIX: name of generated png images. HINT {XXX:6.0f}.replace(' ', '0')
-* transfere images from ./data/train to ./data/dataset
+* FIX: name of generated png images. HINT: {XXX:6.0f}.replace(' ', '0')
+* on a long scale: only save raw file bounding boxes in frequency and time (t0, t1, f0, f1) and the hyperparameters of the corresponding spectrogram. USE THESE PARAMETERS IN DATASET_FN.
 
 ## model.py
 
@@ -51,6 +51,13 @@ im2 = ImageOps.grayscale(im1)
 
 * check other pretrained models from torchvision.models.detection, e.g. fasterrcnn_resnet50_fpn_v2
 
+## dataset.py
+Contains custom datasets and dataloader. These are based on the images that are stored in 
+./data/dataset. 
+
+### ToDos:
+* load/compute spectrogram directly and perform signal detection. E.g. spectrogram calculation as part of __getitem__
+
 ## config.py
 Containes Hyperparameters used by the scripts.
 

inference.py

@@ -17,7 +17,7 @@ import matplotlib.gridspec as gridspec
 from matplotlib.patches import Rectangle
 
 
-def plot_inference(img_tensor, img_name, output, target, detection_threshold):
+def plot_inference(img_tensor, img_name, output, detection_threshold):
 
     fig = plt.figure(figsize=IMG_SIZE, num=img_name)
     gs = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1)  #
@@ -35,13 +35,6 @@ def plot_inference(img_tensor, img_name, output, target, detection_threshold):
                       (y1 - y0),
                       fill=False, color="tab:green", linestyle='--', linewidth=2, zorder=10)
         )
-    for (x0, y0, x1, y1), l in zip(target['boxes'], target['labels']):
-        ax.add_patch(
-            Rectangle((x0, y0),
-                      (x1 - x0),
-                      (y1 - y0),
-                      fill=False, color="white", linewidth=2, zorder=9)
-        )
 
     ax.set_axis_off()
     plt.savefig(Path(INFERENCE_OUTDIR)/(os.path.splitext(img_name)[0] +'_inferred.png'), dpi=IMG_DPI)