refactored emnist lines dataset

1 files changed, 134 insertions, 38 deletions

diff --git a/text_recognizer/datasets/emnist_lines.py b/text_recognizer/datasets/emnist_lines.py
index ae23feb..9ebad22 100644
--- a/text_recognizer/datasets/emnist_lines.py
+++ b/text_recognizer/datasets/emnist_lines.py
@@ -1,16 +1,21 @@
 """Dataset of generated text from EMNIST characters."""
 from collections import defaultdict
 from pathlib import Path
-from typing import Dict, Sequence
+from typing import Callable, Dict, Tuple, Sequence
 
 import h5py
 from loguru import logger
 import numpy as np
+from PIL import Image
 import torch
 from torchvision import transforms
+from torchvision.transforms.functional import InterpolationMode
 
-from text_recognizer.datasets.base_dataset import BaseDataset
-from text_recognizer.datasets.base_data_module import BaseDataModule
+from text_recognizer.datasets.base_dataset import BaseDataset, convert_strings_to_labels
+from text_recognizer.datasets.base_data_module import (
+    BaseDataModule,
+    load_and_print_info,
+)
 from text_recognizer.datasets.emnist import EMNIST
 from text_recognizer.datasets.sentence_generator import SentenceGenerator
 
@@ -54,18 +59,23 @@ class EMNISTLines(BaseDataModule):
 
         self.emnist = EMNIST()
         self.mapping = self.emnist.mapping
-        max_width = int(self.emnist.dims[2] * (self.max_length + 1) * (1 - self.min_overlap)) + IMAGE_X_PADDING
-        
-        if max_width <= IMAGE_WIDTH:
-            raise ValueError("max_width greater than IMAGE_WIDTH")
+        max_width = (
+            int(self.emnist.dims[2] * (self.max_length + 1) * (1 - self.min_overlap))
+            + IMAGE_X_PADDING
+        )
+
+        if max_width >= IMAGE_WIDTH:
+            raise ValueError(
+                    f"max_width {max_width} greater than IMAGE_WIDTH {IMAGE_WIDTH}"
+                    )
 
         self.dims = (
             self.emnist.dims[0],
-            self.emnist.dims[1],
-            self.emnist.dims[2] * self.max_length,
+            IMAGE_HEIGHT,
+            IMAGE_WIDTH
         )
 
-        if self.max_length <= MAX_OUTPUT_LENGTH:
+        if self.max_length >= MAX_OUTPUT_LENGTH:
             raise ValueError("max_length greater than MAX_OUTPUT_LENGTH")
 
         self.output_dims = (MAX_OUTPUT_LENGTH, 1)
@@ -77,8 +87,11 @@ class EMNISTLines(BaseDataModule):
     def data_filename(self) -> Path:
         """Return name of dataset."""
         return (
-            DATA_DIRNAME
-            / f"ml_{self.max_length}_o{self.min_overlap:f}_{self.max_overlap:f}_ntr{self.num_train}_ntv{self.num_val}_nte{self.num_test}_{self.with_start_end_tokens}.h5"
+            DATA_DIRNAME / (f"ml_{self.max_length}_"
+            f"o{self.min_overlap:f}_{self.max_overlap:f}_"
+            f"ntr{self.num_train}_"
+            f"ntv{self.num_val}_"
+            f"nte{self.num_test}.h5")
         )
 
     def prepare_data(self) -> None:
@@ -92,21 +105,28 @@ class EMNISTLines(BaseDataModule):
     def setup(self, stage: str = None) -> None:
         logger.info("EMNISTLinesDataset loading data from HDF5...")
         if stage == "fit" or stage is None:
+            print(self.data_filename)
             with h5py.File(self.data_filename, "r") as f:
                 x_train = f["x_train"][:]
                 y_train = torch.LongTensor(f["y_train"][:])
                 x_val = f["x_val"][:]
                 y_val = torch.LongTensor(f["y_val"][:])
 
-            self.data_train = BaseDataset(x_train, y_train, transform=_get_transform(augment=self.augment))
-            self.data_val  = BaseDataset(x_val, y_val, transform=_get_transform(augment=self.augment))
+            self.data_train = BaseDataset(
+                x_train, y_train, transform=_get_transform(augment=self.augment)
+            )
+            self.data_val = BaseDataset(
+                x_val, y_val, transform=_get_transform(augment=self.augment)
+            )
 
         if stage == "test" or stage is None:
             with h5py.File(self.data_filename, "r") as f:
                 x_test = f["x_test"][:]
                 y_test = torch.LongTensor(f["y_test"][:])
 
-            self.data_train = BaseDataset(x_test, y_test, transform=_get_transform(augment=False))
+            self.data_test = BaseDataset(
+                x_test, y_test, transform=_get_transform(augment=False)
+            )
 
     def __repr__(self) -> str:
         """Return str about dataset."""
@@ -132,53 +152,129 @@ class EMNISTLines(BaseDataModule):
 
     def _generate_data(self, split: str) -> None:
         logger.info(f"EMNISTLines generating data for {split}...")
-        sentence_generator = SentenceGenerator(self.max_length - 2)  # Subtract by 2 because start/end token
+        sentence_generator = SentenceGenerator(
+            self.max_length - 2
+        )  # Subtract by 2 because start/end token
 
         emnist = self.emnist
         emnist.prepare_data()
         emnist.setup()
 
         if split == "train":
-            samples_by_char = _get_samples_by_char(emnist.x_train, emnist.y_train, emnist.mapping)
+            samples_by_char = _get_samples_by_char(
+                emnist.x_train, emnist.y_train, emnist.mapping
+            )
             num = self.num_train
         elif split == "val":
-            samples_by_char = _get_samples_by_char(emnist.x_train, emnist.y_train, emnist.mapping)
+            samples_by_char = _get_samples_by_char(
+                emnist.x_train, emnist.y_train, emnist.mapping
+            )
             num = self.num_val
-        elif split == "test":
-            samples_by_char = _get_samples_by_char(emnist.x_test, emnist.y_test, emnist.mapping)
+        else:
+            samples_by_char = _get_samples_by_char(
+                emnist.x_test, emnist.y_test, emnist.mapping
+            )
             num = self.num_test
 
         DATA_DIRNAME.mkdir(parents=True, exist_ok=True)
-        with h5py.File(self.data_filename, "w") as f:
+        with h5py.File(self.data_filename, "a") as f:
             x, y = _create_dataset_of_images(
-                    num, samples_by_char, sentence_generator, self.min_overlap, self.max_overlap, self.dims
-                    )
-            y = _convert_strings_to_labels(
-                    y,
-                    emnist.inverse_mapping,
-                    length=MAX_OUTPUT_LENGTH
-                    )
+                num,
+                samples_by_char,
+                sentence_generator,
+                self.min_overlap,
+                self.max_overlap,
+                self.dims,
+            )
+            y = convert_strings_to_labels(
+                y, emnist.inverse_mapping, length=MAX_OUTPUT_LENGTH
+            )
             f.create_dataset(f"x_{split}", data=x, dtype="u1", compression="lzf")
             f.create_dataset(f"y_{split}", data=y, dtype="u1", compression="lzf")
 
-def _get_samples_by_char(samples: np.ndarray, labels: np.ndarray, mapping: Dict) -> defaultdict:
+
+def _get_samples_by_char(
+    samples: np.ndarray, labels: np.ndarray, mapping: Dict
+) -> defaultdict:
     samples_by_char = defaultdict(list)
     for sample, label in zip(samples, labels):
         samples_by_char[mapping[label]].append(sample)
     return samples_by_char
 
 
-def _construct_image_from_string():
-    pass
-
-
 def _select_letter_samples_for_string(string: str, samples_by_char: defaultdict):
-    pass
-
-
-def _create_dataset_of_images(num_samples: int, samples_by_char: defaultdict, sentence_generator: SentenceGenerator, min_overlap: float, max_overlap: float, dims: Tuple) -> Tuple[torch.Tensor, torch.Tensor]:
+    null_image = torch.zeros((28, 28), dtype=torch.uint8)
+    sample_image_by_char = {}
+    for char in string:
+        if char in sample_image_by_char:
+            continue
+        samples = samples_by_char[char]
+        sample = samples[np.random.choice(len(samples))] if samples else null_image
+        sample_image_by_char[char] = sample.reshape(28, 28)
+    return [sample_image_by_char[char] for char in string]
+
+
+def _construct_image_from_string(
+    string: str,
+    samples_by_char: defaultdict,
+    min_overlap: float,
+    max_overlap: float,
+    width: int,
+) -> torch.Tensor:
+    overlap = np.random.uniform(min_overlap, max_overlap)
+    sampled_images = _select_letter_samples_for_string(string, samples_by_char)
+    N = len(sampled_images)
+    H, W = sampled_images[0].shape
+    next_overlap_width = W - int(overlap * W)
+    concatenated_image = torch.zeros((H, width), dtype=torch.uint8)
+    x = IMAGE_X_PADDING
+    for image in sampled_images:
+        concatenated_image[:, x : (x + W)] += image
+        x += next_overlap_width
+    return torch.minimum(torch.Tensor([255]), concatenated_image)
+
+
+def _create_dataset_of_images(
+    num_samples: int,
+    samples_by_char: defaultdict,
+    sentence_generator: SentenceGenerator,
+    min_overlap: float,
+    max_overlap: float,
+    dims: Tuple,
+) -> Tuple[torch.Tensor, torch.Tensor]:
     images = torch.zeros((num_samples, IMAGE_HEIGHT, dims[2]))
     labels = []
     for n in range(num_samples):
         label = sentence_generator.generate()
-        crop = _construct_image_from_string()
+        crop = _construct_image_from_string(
+            label, samples_by_char, min_overlap, max_overlap, dims[-1]
+        )
+        height = crop.shape[0]
+        y = (IMAGE_HEIGHT - height) // 2
+        images[n, y : (y + height), :] = crop
+        labels.append(label)
+    return images, labels
+
+
+def _get_transform(augment: bool = False) -> Callable:
+    if not augment:
+        return transforms.Compose([transforms.ToTensor()])
+    return transforms.Compose(
+        [
+            transforms.ToTensor(),
+            transforms.ColorJitter(brightness=(0.5, 1.0)),
+            transforms.RandomAffine(
+                degrees=3,
+                translate=(0.0, 0.05),
+                scale=(0.4, 1.1),
+                shear=(-40, 50),
+                interpolation=InterpolationMode.BILINEAR,
+                fill=0,
+            ),
+        ]
+    )
+
+
+def generate_emnist_lines() -> None:
+    """Generates a synthetic handwritten dataset and displays info,"""
+    load_and_print_info(EMNISTLines)