added XSeg model

samplelib/SampleGeneratorImage.py

@@ -0,0 +1,66 @@
+import traceback
+
+import cv2
+import numpy as np
+
+from core.joblib import SubprocessGenerator, ThisThreadGenerator
+from samplelib import (SampleGeneratorBase, SampleLoader, SampleProcessor,
+                       SampleType)
+
+
+class SampleGeneratorImage(SampleGeneratorBase):
+    def __init__ (self, samples_path, debug, batch_size, sample_process_options=SampleProcessor.Options(), output_sample_types=[], raise_on_no_data=True, **kwargs):
+        super().__init__(debug, batch_size)
+        self.initialized = False
+        self.sample_process_options = sample_process_options
+        self.output_sample_types = output_sample_types
+
+        samples = SampleLoader.load (SampleType.IMAGE, samples_path)
+        
+        if len(samples) == 0:
+            if raise_on_no_data:
+                raise ValueError('No training data provided.')
+            return
+        
+        self.generators = [ThisThreadGenerator ( self.batch_func, samples )] if self.debug else \
+                          [SubprocessGenerator ( self.batch_func, samples )]
+
+        self.generator_counter = -1
+        self.initialized = True
+        
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        self.generator_counter += 1
+        generator = self.generators[self.generator_counter % len(self.generators) ]
+        return next(generator)
+
+    def batch_func(self, samples):
+        samples_len = len(samples)
+        
+
+        idxs = [ *range(samples_len) ]
+        shuffle_idxs = []
+
+        while True:
+
+            batches = None
+            for n_batch in range(self.batch_size):
+
+                if len(shuffle_idxs) == 0:
+                    shuffle_idxs = idxs.copy()
+                    np.random.shuffle (shuffle_idxs)
+
+                idx = shuffle_idxs.pop()
+                sample = samples[idx]
+                
+                x, = SampleProcessor.process ([sample], self.sample_process_options, self.output_sample_types, self.debug)
+
+                if batches is None:
+                    batches = [ [] for _ in range(len(x)) ]
+
+                for i in range(len(x)):
+                    batches[i].append ( x[i] )
+
+            yield [ np.array(batch) for batch in batches]