Update README.md

README.md

@@ -10,4 +10,53 @@ tags:
 - hover-net
 library_name: transformers
 pipeline_tag: image-segmentation
----
+---
+
+
+
+
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import cellseg_sribd
+from skimage import io, segmentation, morphology, measure, exposure
+import numpy as np
+import tifffile as tif
+import requests
+
+img_name = 'cell_00010.png'
+def normalize_channel(img, lower=1, upper=99):
+    non_zero_vals = img[np.nonzero(img)]
+    percentiles = np.percentile(non_zero_vals, [lower, upper])
+    if percentiles[1] - percentiles[0] > 0.001:
+        img_norm = exposure.rescale_intensity(img, in_range=(percentiles[0], percentiles[1]), out_range='uint8')
+    else:
+        img_norm = img
+    return img_norm.astype(np.uint8)
+if img_name.endswith('.tif') or img_name.endswith('.tiff'):
+    img_data = tif.imread(img_name)
+else:
+    img_data = io.imread(img_name)
+        # normalize image data
+if len(img_data.shape) == 2:
+    img_data = np.repeat(np.expand_dims(img_data, axis=-1), 3, axis=-1)
+elif len(img_data.shape) == 3 and img_data.shape[-1] > 3:
+    img_data = img_data[:,:, :3]
+else:
+    pass
+pre_img_data = np.zeros(img_data.shape, dtype=np.uint8)
+for i in range(3):
+    img_channel_i = img_data[:,:,i]
+    if len(img_channel_i[np.nonzero(img_channel_i)])>0:
+        pre_img_data[:,:,i] = normalize_channel(img_channel_i, lower=1, upper=99)
+
+
+
+model = cellseg_sribd.from_pretrained("Lewislou/cellseg_sribd")
+with torch.no_grad():
+    output = model(pre_img_data)
+
+```