full

app.py

@@ -8,6 +8,7 @@ from dnnlib import tflib
 import imageio
 import os
 import subprocess
+import random
 
 def check_gpu():
     return tf.test.is_gpu_available(cuda_only=False, min_cuda_compute_capability=None)
@@ -15,7 +16,23 @@ def check_gpu():
 def generate_image_from_projected_latents(latent_vector):
     images = Gs.components.synthesis.run(latent_vector, **Gs_kwargs)
     return images
+## define video generation methods
+def ED_to_ES(latent_code):
+    all_imgs = []
+    amounts_up = [i/25 for i in range(0,25)]
+    amounts_down = [1-i/25 for i in range(1,26)]
 
+    for amount_to_move in amounts_up:
+        modified_latent_code = latent_code + latent_controls["time"]*amount_to_move
+        images = generate_image_from_projected_latents(modified_latent_code)
+        all_imgs.append(np.array(images[0]))
+
+    for amount_to_move in amounts_down:
+        modified_latent_code = latent_code + latent_controls["time"]*amount_to_move
+        images = generate_image_from_projected_latents(modified_latent_code)
+        all_imgs.append(np.array(images[0]))
+    
+    return np.array(all_imgs) 
 def frame_to_frame(latent_code):
     modified_latent_code = np.copy(latent_code)
     full_video = [generate_image_from_projected_latents(modified_latent_code)]
@@ -49,26 +66,46 @@ def load_initial_setup():
     return Gs, Gs_kwargs, latent_controls, sess
 
 if __name__=="__main__":
-    rnd = np.random.RandomState()
+
+    st.header('GANcMRI DEMO')
+    sphericity_index = st.slider("Sphericity Index", -2., 3., 0.0)
+    lv_area = st.slider("LV Area", -2., 3., 0.0)
+    # Check if 'random_number' is already in the session state, if not, set a random number for seed
+    if 'random_number' not in st.session_state:
+        st.session_state.random_number = random.randint(0, 1000000)
+    cols = st.columns(2)
+    
+    with cols[0]:
+        st.caption('ED-to-ES')
+    with cols[1]:
+        st.caption('Frame-to-Frame')
+
+    rnd = np.random.RandomState(st.session_state.random_number)
     Gs, Gs_kwargs, latent_controls, sess = load_initial_setup()
     with sess.as_default():
         z = rnd.randn(1, *Gs.input_shape[1:])
         random_img_latent_code = Gs.components.mapping.run(z,None)
         #make it be ED frame
         random_img_latent_code -= 0.7*latent_controls['time']
-        vid = frame_to_frame(random_img_latent_code)
-
-        temp_video_path="output.mp4"
-        writer=imageio.get_writer(temp_video_path, fps=20)
-        for i in range(vid.shape[0]):
-            frame = vid[i]
-            writer.append_data(frame)
 
-        writer.close()
-        out_path = "fixed_out.mp4"
-        command = ["ffmpeg", "-i", temp_video_path, "-vcodec", "libx264", out_path]
-        subprocess.run(command)
+        # Apply physiological adjustment
+        adjusted_latent_code = np.copy(random_img_latent_code)
+        adjusted_latent_code += sphericity_index * latent_controls['sphericity_index']
+        adjusted_latent_code += lv_area * latent_controls['lv_area']
 
-        st.video(out_path)
-        os.remove(temp_video_path)
-        os.remove(out_path)
+        ed_to_es_vid = ED_to_ES(adjusted_latent_code)
+        f_to_f_vid = frame_to_frame(adjusted_latent_code)
+        for idx,vid in enumerate([ed_to_es_vid, f_to_f_vid]):
+            temp_video_path=f"output{idx}.mp4"
+            writer=imageio.get_writer(temp_video_path, fps=20)
+            for i in range(vid.shape[0]):
+                frame = vid[i]
+                writer.append_data(frame)
+            writer.close()
+            out_path = f"fixed_out{idx}.mp4"
+            command = ["ffmpeg", "-i", temp_video_path, "-vcodec", "libx264", out_path]
+            subprocess.run(command)
+            with cols[idx]:
+                st.video(out_path)
+            os.remove(temp_video_path)
+            os.remove(out_path)