support liszt

.gradio/certificate.pem

@@ -0,0 +1,31 @@
+-----BEGIN CERTIFICATE-----
+MIIFazCCA1OgAwIBAgIRAIIQz7DSQONZRGPgu2OCiwAwDQYJKoZIhvcNAQELBQAw
+TzELMAkGA1UEBhMCVVMxKTAnBgNVBAoTIEludGVybmV0IFNlY3VyaXR5IFJlc2Vh
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+emyPxgcYxn/eR44/KJ4EBs+lVDR3veyJm+kXQ99b21/+jh5Xos1AnX5iItreGCc=
+-----END CERTIFICATE-----

app.py

@@ -6,22 +6,24 @@ from scipy.ndimage import convolve
 
 def readRAW(path):
 
-    # output = np.fromfile(path, dtype=np.int16).reshape(31,40,64)
-    output = np.fromfile(path, dtype=np.int16).reshape(30,40,64)
+    output = np.fromfile(path, dtype=np.int16).reshape(31,40,64)
+    # output = np.fromfile(path, dtype=np.int16).reshape(30,40,64)
 
     return output
 
 
 def load_bin(file):
-    # raw_hist = readRAW(file.name)[1:,...]
-    raw_hist = readRAW(file.name)
+    raw_hist = readRAW(file.name)[1:,...].astype(np.float32)
+    # raw_hist = readRAW(file.name)
     # 默认显示一张 sum 图像
 
-    # multishot =  (raw_hist[...,62]*1024 + raw_hist[...,63])
-    # normalize_data =  1 / multishot * 60e3
-    # nor_hist = (raw_hist) * normalize_data[...,np.newaxis]
+    multishot =  (raw_hist[...,62]*1024 + raw_hist[...,63])
+    # multishot[multishot==0] = 20e3
+    normalize_data =  1 / multishot * 20e3
 
-    nor_hist = (raw_hist) 
+    nor_hist = (raw_hist) * normalize_data[...,np.newaxis]
+
+    # nor_hist = (raw_hist) 
 
     img = np.sum(nor_hist[:, :, :-2], axis=2)
     norm_img = (img - img.min()) / (img.max() + 1e-8)
@@ -97,4 +99,4 @@ with gr.Blocks() as demo:
 
     image_display.select(plot_pixel_histogram, inputs=[ raw_hist, nor_hist], outputs=histogram)
 
-demo.launch()
+demo.launch(share=True)