Add multi-model capability to CLI driver

kraken/kraken.py

@@ -114,7 +114,7 @@ def binarizer(threshold, zoom, escale, border, perc, range, low, high, input, ou
     message('\u2713', fg='green')
 
 
-def segmenter(legacy, model, text_direction, scale, maxcolseps, black_colseps,
+def segmenter(legacy, models, text_direction, scale, maxcolseps, black_colseps,
               remove_hlines, pad, mask, device, input, output) -> None:
     import json
 
@@ -151,7 +151,7 @@ def segmenter(legacy, model, text_direction, scale, maxcolseps, black_colseps,
                                   pad=pad,
                                   mask=mask)
         else:
-            res = blla.segment(im, text_direction, mask=mask, model=model, device=device,
+            res = blla.segment(im, text_direction, mask=mask, model=models, device=device,
                                raise_on_error=ctx.meta['raise_failed'], autocast=ctx.meta["autocast"])
     except Exception:
         if ctx.meta['raise_failed']:
@@ -474,7 +474,7 @@ def binarize(ctx, threshold, zoom, escale, border, perc, range, low, high):
 
 @cli.command('segment')
 @click.pass_context
-@click.option('-i', '--model', default=None, show_default=True,
+@click.option('-i', '--model', default=None, show_default=True, multiple=True,
               help='Baseline detection model to use')
 @click.option('-x/-bl', '--boxes/--baseline', default=True, show_default=True,
               help='Switch between legacy box segmenter and neural baseline segmenter')
@@ -501,42 +501,48 @@ def segment(ctx, model, boxes, text_direction, scale, maxcolseps,
     """
     from kraken.containers import ProcessingStep
 
+    print(model)
+
     if model and boxes:
         logger.warning(f'Baseline model ({model}) given but legacy segmenter selected. Forcing to -bl.')
         boxes = False
 
     if boxes is False:
         if not model:
-            model = SEGMENTATION_DEFAULT_MODEL
+            model = [SEGMENTATION_DEFAULT_MODEL]
         ctx.meta['steps'].append(ProcessingStep(id=str(uuid.uuid4()),
                                                 category='processing',
                                                 description='Baseline and region segmentation',
-                                                settings={'model': os.path.basename(model),
+                                                settings={'model': [os.path.basename(m) for m in model],
                                                           'text_direction': text_direction}))
 
-        # first try to find the segmentation model by its given name,
-        # then look in the kraken config folder
-        location = None
-        search = [model, os.path.join(click.get_app_dir(APP_NAME), model)]
-        for loc in search:
-            if os.path.isfile(loc):
-                location = loc
-                break
-        if not location:
-            raise click.BadParameter(f'No model for {model} found')
+        # first try to find the segmentation models by their given names, then
+        # look in the kraken config folder
+        locations = []
+        for m in model:
+            location = None
+            search = [m, os.path.join(click.get_app_dir(APP_NAME), m)]
+            for loc in search:
+                if os.path.isfile(loc):
+                    location = loc
+                    locations.append(loc)
+                    break
+            if not location:
+                raise click.BadParameter(f'No model for {m} found')
 
-        from kraken.lib.vgsl import TorchVGSLModel
-        message(f'Loading ANN {model}\t', nl=False)
-        try:
-            model = TorchVGSLModel.load_model(location)
-            model.to(ctx.meta['device'])
-        except Exception:
-            if ctx.meta['raise_failed']:
-                raise
-            message('\u2717', fg='red')
-            ctx.exit(1)
 
-        message('\u2713', fg='green')
+        from kraken.lib.vgsl import TorchVGSLModel
+        models = []
+        for loc in locations:
+            message(f'Loading ANN {loc}\t', nl=False)
+            try:
+                models.append(TorchVGSLModel.load_model(loc).to(ctx.meta['device']))
+            except Exception:
+                if ctx.meta['raise_failed']:
+                    raise
+                message('\u2717', fg='red')
+                ctx.exit(1)
+            message('\u2713', fg='green')
     else:
         ctx.meta['steps'].append(ProcessingStep(id=str(uuid.uuid4()),
                                                 category='processing',
@@ -548,7 +554,7 @@ def segment(ctx, model, boxes, text_direction, scale, maxcolseps,
                                                           'remove_hlines': remove_hlines,
                                                           'pad': pad}))
 
-    return partial(segmenter, boxes, model, text_direction, scale, maxcolseps,
+    return partial(segmenter, boxes, models, text_direction, scale, maxcolseps,
                    black_colseps, remove_hlines, pad, mask, ctx.meta['device'])