Update app.py

app.py

@@ -1,3 +1,58 @@
 import gradio as gr
+from skimage import io, segmentation, morphology, measure, exposure
+from sribd_cellseg_models import MultiStreamCellSegModel,ModelConfig
+import numpy as np
+import tifffile as tif
+import requests
+import torch
+from PIL import Image
+from overlay import visualize_instances_map
+import cv2
 
-gr.Interface.load("models/Lewislou/cell-seg-sribd").launch()
+
+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)
+
+def predict(filename, model=None, device=None, reduce_labels=True):
+	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)
+	#dummy_input = np.zeros((512,512,3)).astype(np.uint8)
+	my_model = MultiStreamCellSegModel.from_pretrained("Lewislou/cellseg_sribd")
+	checkpoints = torch.load('model.pt')
+	my_model.__init__(ModelConfig())
+	my_model.load_checkpoints(checkpoints)
+	with torch.no_grad():
+	    output = my_model(pre_img_data)
+	overlay = visualize_instances_map(pre_img_data,star_label)
+	#cv2.imwrite('prediction.png', cv2.cvtColor(overlay, cv2.COLOR_RGB2BGR))
+	return overlay
+gr.Interface(
+    predict,
+    inputs=[gr.components.Image(label="Upload Input Image", type="filepath"),
+            gr.components.Textbox(label='Model Name', value='sribd_med', max_lines=1)],
+    outputs=[gr.Image(label="Processed Image"),
+             gr.Image(label="Label Image"),
+             ],
+    title="Cell Segmentation Results",
+    examples=get_examples(default_model)
+).launch()