api to eval single

app.py

@@ -1,5 +1,5 @@
 from main import run_app, run_train, run_inference, run_generate_frame, run_interpolate_frames
-from evaluate import run_evaluate
+from evaluate import run_evaluate, get_score
 
 import spaces
 from PIL import Image
@@ -25,12 +25,14 @@ with gr.Blocks() as demo:
             interp_frame1 = gr.Image(type="pil", label="Interpolation Start Frame")
             interp_frame2 = gr.Image(type="pil", label="Interpolation End Frame")
             times_to_interp = gr.Number(label="Times to Interpolate", value=1)
+            name = gr.Text(label="Name", value="")
             train_btn = gr.Button(value="Train")
             inference_btn = gr.Button(value="Inference")
             generate_frame_btn = gr.Button(value="Generate Frame")
             submit_btn = gr.Button(value="Generate")
             interp_btn = gr.Button(value="Interpolate Frames")
-            eval_btn = gr.Button(value="Evaluate")
+            eval_btn = gr.Button(value="Evaluate All")
+            eval_btn2 = gr.Button(value="Evaluate")
         with gr.Column():
             animation = gr.Video(label="Result")
             frames = gr.Gallery(type="pil", label="Frames", format="png")
@@ -62,6 +64,10 @@ with gr.Blocks() as demo:
     eval_btn.click(
         run_evaluate, inputs=[], outputs=[eval_scores]
     )
+
+    eval_btn2.click(
+        get_score, inputs=[name, char_imgs, mocap, tr_steps, inf_steps, fps, remove_bg], outputs=[eval_scores]
+    )
     
 
 demo.launch(share=True)

evaluate.py

@@ -111,7 +111,7 @@ def compute_fid(img1, img2):
     return fid
 
 
-def get_score(item, image_paths, video_path, metrics, train_steps=100, inference_steps=10, fps=12, bg_remove=False):
+def get_score(item, image_paths, video_path, train_steps=100, inference_steps=10, fps=12, bg_remove=False):
     images = []
     for path in image_paths:
         img = Image.open(path)
@@ -196,6 +196,7 @@ def get_score(item, image_paths, video_path, metrics, train_steps=100, inference
     print("FID:", sum(fid2)/len(fid2))
     #print("FVD:", fvd2)
     
+    metrics = {}
     metrics[item] = {'ft': {}, 'base': {}, 'n_frames': len(gt_frames), 'complexity': len(images)}
     metrics[item]['ft']['ssim'] = {'avg': sum(ssim)/len(ssim), 'vals': ssim}
     metrics[item]['ft']['psnr'] = {'avg': sum(psnr)/len(psnr), 'vals': psnr}
@@ -209,9 +210,9 @@ def get_score(item, image_paths, video_path, metrics, train_steps=100, inference
     #metrics[item]['base']['fvd'] = fvd2
     
     #print(metrics)
+    return metrics[item]
 
-    with open('/data/metrics.json', "w", encoding="utf-8") as json_file:
-        json.dump(metrics, json_file, ensure_ascii=False, indent=4)
+    
     
     
     
@@ -252,8 +253,11 @@ def run_evaluate():
             print(images, videos)
 
             if len(videos) == 1:
-                get_score(item, images, 'test/'+item+'/'+videos[0], metrics)
+                metrics[item] = get_score(item, images, 'test/'+item+'/'+videos[0])
                 #get_score(item, ['test/'+item+'/1.jpg', 'test/'+item+'/2.jpg', 'test/'+item+'/3.jpg'], 'test/'+item+'/v.mp4')
+
+                with open('/data/metrics.json', "w", encoding="utf-8") as json_file:
+                    json.dump(metrics, json_file, ensure_ascii=False, indent=4)
             else:
                 print('Error: mp4 not found')
         except Exception as e: