def __getitem__(self, index): item = self.records[index] img = rio.load_site(item.dataset, item.experiment, item.plate, item.well, item.site, base_path=self.root) label = item.sirna if self.mode == 'train' else item.id_code out = self.transform(img, item.experiment) if self.with_plates: p = torch.zeros(4, dtype=torch.float32) p[item.plate - 1] = 1.0 out = out, p return out, label
def load_image(row): images = [] for site in [1, 2]: image = rio.load_site('train', row['experiment'], row['plate'], row['well'], site, base_path=row['root']) images.append(image) images = np.stack(images, 0) return images
# In[12]: md['site'].value_counts() # <strong>Image</strong> # In[13]: # pixel_stats.csv # row 1-6 # train set, experiment HEPG2-01, plate 1, well B02, site 1 t = rio.load_site('train', 'HEPG2-01', 1, 'B02', 1, base_path=LOCAL_IMAGES_BASE_PATH) t.shape # In[14]: img = t[:, :, 0] # channel 1 img.shape # In[15]:
# In[3]: # Ref: # rxrx/io.py, line: 14, 15 LOCAL_IMAGES_BASE_PATH = 'D:\\_peng\\recursion-cellular-image-classification' # windows DEFAULT_METADATA_BASE_PATH = LOCAL_IMAGES_BASE_PATH # In[4]: # train set, experiment RPE-05, plate 3, well D19, site 2 t = rio.load_site('train', 'RPE-05', 3, 'D19', 2, base_path=LOCAL_IMAGES_BASE_PATH) t.shape # In[5]: fig, axes = plt.subplots(2, 3, figsize=(24, 16)) for i, ax in enumerate(axes.flatten()): ax.axis('off') ax.set_title('channel {}'.format(i + 1)) _ = ax.imshow(t[:, :, i], cmap='gray') # In[6]:
import numpy as np # linear algebra import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) import os print(os.listdir("../input")) import sys import matplotlib.pyplot as plt ### matplotlib inline ### git clone https://github.com/recursionpharma/rxrx1-utils print('rxrx1-utils cloned!') ### ls sys.path.append('rxrx1-utils') import rxrx.io as rio t = rio.load_site('train', 'RPE-05', 3, 'D19', 2) t.shape fig, axes = plt.subplots(2, 3, figsize=(24, 16)) for i, ax in enumerate(axes.flatten()): ax.axis('off') ax.set_title('channel {}'.format(i + 1)) _ = ax.imshow(t[:, :, i], cmap='gray') x = rio.convert_tensor_to_rgb(t) x.shape plt.figure(figsize=(8, 8)) plt.axis('off') _ = plt.imshow(x) y = rio.load_site_as_rgb('train', 'HUVEC-08', 4, 'K09', 1) plt.figure(figsize=(8, 8)) plt.axis('off')