From 2a80c07498c480c5f34dc36629adad98a6577834 Mon Sep 17 00:00:00 2001
From: LonicaMewinsky <93007558+LonicaMewinsky@users.noreply.github.com>
Date: Mon, 13 Nov 2023 15:43:02 -0600
Subject: [PATCH] Delete LoadAnim.py

---
 LoadAnim.py | 81 -----------------------------------------------------
 1 file changed, 81 deletions(-)
 delete mode 100644 LoadAnim.py

diff --git a/LoadAnim.py b/LoadAnim.py
deleted file mode 100644
index 1d9544b..0000000
--- a/LoadAnim.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import cv2
-import torch
-import torchvision.transforms as transforms
-import numpy as np
-import os
-
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-class BreakFrames:
-    def __init__(self):
-        pass
-
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "file_input": ("STRING", {
-                    "multiline": False,
-                    "default": "C:/Videos/video.mp4"
-                }),
-                "num_keyframes": ("INT", {
-                        "default": 0, 
-                        "min": 0,
-                        "max": 4096,
-                        "step": 1
-                }),
-            },
-        }
-    RETURN_TYPES = ("IMAGE", "IMAGE", "STRING")
-    RETURN_NAMES = ("Frames","Keyframes", "Keyframe indices")
-
-    FUNCTION = "breakframes"
-    CATEGORY = "Frames"
-
-    def breakframes(self, file_input, num_keyframes):
-        if not os.path.exists(file_input):
-            raise FileNotFoundError(f"File '{file_input} cannot be found.'")
-        # Open the video file
-        try:
-            video_capture = cv2.VideoCapture(file_input)
-            # Check if the anim was loaded
-            if not video_capture.isOpened():
-                print(f"Error: Could not open video file {file_input}.")
-                return None
-        except:
-            print(f"Error: Could not open video file {file_input}.")
-            return None
-        # Read each frame from anim
-        frame_count = 0
-        transformer = transforms.ToTensor()
-        tensors = []
-        while True:
-            ret, frame = video_capture.read()
-            if ret:
-                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-                tensors.append(transformer(frame).to(device).unsqueeze(0))
-                frame_count += 1
-            else:
-                break
-        video_capture.release()
-        if num_keyframes > 0:
-            N = np.clip(num_keyframes, 2, len(tensors)-1) # Save a spot for first frame
-            differences = [torch.norm(tensors[i+1] - tensors[i], p=2) for i in range(len(tensors)-1)]
-            _, top_indices = torch.topk(torch.tensor(differences), k=N, largest=True)
-            keyframe_indices = sorted([index.item() + 1 for index in top_indices])
-            keyframe_indices.insert(0, 0)
-            cat_keyframe_tensors = [tensors[i] for i in keyframe_indices]
-            cat_keyframe_tensors = torch.cat(cat_keyframe_tensors, dim = 0).permute(0, 2, 3, 1)
-        else:
-            cat_keyframe_tensors = None
-        cat_frame_tensors = torch.cat(tensors, dim = 0).permute(0, 2, 3, 1)
-        print(str(keyframe_indices))
-        return (cat_frame_tensors, cat_keyframe_tensors, str(keyframe_indices))
-
-NODE_CLASS_MAPPINGS = {
-    "BreakFrames": BreakFrames
-}
-
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "BreakFrames": "BreakFrames"
-}