├── .github └── workflows │ └── publish.yml ├── ComfyApple.gif ├── LICENSE ├── README.md ├── VideoExample.json ├── __init__.py ├── allinoneworkflow.json ├── convert-video-color-cuda.py ├── convert-video-color.py ├── convert-video.py ├── cuda-requirements.txt ├── nodes.py ├── pyproject.toml └── requirements.txt /.github/workflows/publish.yml: -------------------------------------------------------------------------------- 1 | name: Publish to Comfy registry 2 | on: 3 | workflow_dispatch: 4 | push: 5 | branches: 6 | - main 7 | - master 8 | paths: 9 | - "pyproject.toml" 10 | 11 | jobs: 12 | publish-node: 13 | name: Publish Custom Node to registry 14 | runs-on: ubuntu-latest 15 | # if this is a forked repository. Skipping the workflow. 16 | if: github.event.repository.fork == false 17 | steps: 18 | - name: Check out code 19 | uses: actions/checkout@v4 20 | - name: Publish Custom Node 21 | uses: Comfy-Org/publish-node-action@main 22 | with: 23 | ## Add your own personal access token to your Github Repository secrets and reference it here. 24 | personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }} 25 | -------------------------------------------------------------------------------- /ComfyApple.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/BetaDoggo/ComfyUI-VideoPlayer/59e3340a77b6f17e5ef11ca10950a6483a8cfe8a/ComfyApple.gif -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2024 BetaDoggo 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # ComfyUI Video Player 2 | 1 step closer to replacing all software with comfy 3 | ![preview](https://github.com/BetaDoggo/ComfyUI-VideoPlayer/blob/main/ComfyApple.gif) 4 | 5 | Install this node as well as [ComfyUI-Custom-Scripts](https://github.com/pythongosssss/ComfyUI-Custom-Scripts) 6 | # Usage (automatic) 7 | 1. Load allinoneworkflow.json 8 | 2. Enter the full video path 9 | 3. Check "Extra options" -> "Auto Queue" 10 | 4. Click "Queue Prompt" 11 | # Usage (manual) 12 | 1. Convert your file using the included convert-video.py or convert-video-color.py/convert-video-color-cuda.py (requires cuda-requirements.txt) 13 | 2. Load the example workflow 14 | 3. Set the framerate of the video 15 | 4. Set the path to the location of the frames generated by convert-video.py **including the final /** 16 | 5. Set the batch count to the number of frames in the video 17 | 6. press Queue Prompt 18 | -------------------------------------------------------------------------------- /VideoExample.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 108, 3 | "last_link_id": 120, 4 | "nodes": [ 5 | { 6 | "id": 106, 7 | "type": "ShowText|pysssss", 8 | "pos": [ 9 | 507, 10 | 120 11 | ], 12 | "size": [ 13 | 1404.1055721118216, 14 | 1027.455992305583 15 | ], 16 | "flags": {}, 17 | "order": 2, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "text", 22 | "type": "STRING", 23 | "link": 120, 24 | "widget": { 25 | "name": "text" 26 | } 27 | } 28 | ], 29 | "outputs": [ 30 | { 31 | "name": "STRING", 32 | "type": "STRING", 33 | "links": null, 34 | "shape": 6 35 | } 36 | ], 37 | "properties": { 38 | "Node name for S&R": "ShowText|pysssss" 39 | }, 40 | "widgets_values": [ 41 | "", 42 | 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43 | ] 44 | }, 45 | { 46 | "id": 108, 47 | "type": "LoadFrame", 48 | "pos": [ 49 | 179, 50 | 127 51 | ], 52 | "size": [ 53 | 315, 54 | 106 55 | ], 56 | "flags": {}, 57 | "order": 1, 58 | "mode": 0, 59 | "inputs": [ 60 | { 61 | "name": "frame", 62 | "type": "INT", 63 | "link": 119, 64 | "widget": { 65 | "name": "frame" 66 | } 67 | } 68 | ], 69 | "outputs": [ 70 | { 71 | "name": "STRING", 72 | "type": "STRING", 73 | "links": [ 74 | 120 75 | ], 76 | "shape": 3, 77 | "slot_index": 0 78 | } 79 | ], 80 | "properties": { 81 | "Node name for S&R": "LoadFrame" 82 | }, 83 | "widgets_values": [ 84 | 6573, 85 | 30, 86 | "E:\\apple\\frames\\" 87 | ] 88 | }, 89 | { 90 | "id": 102, 91 | "type": "PrimitiveNode", 92 | "pos": [ 93 | 179, 94 | 278 95 | ], 96 | "size": { 97 | "0": 210, 98 | "1": 82 99 | }, 100 | "flags": {}, 101 | "order": 0, 102 | "mode": 0, 103 | "outputs": [ 104 | { 105 | "name": "INT", 106 | "type": "INT", 107 | "links": [ 108 | 119 109 | ], 110 | "slot_index": 0, 111 | "widget": { 112 | "name": "frame" 113 | } 114 | } 115 | ], 116 | "properties": { 117 | "Run widget replace on values": false 118 | }, 119 | "widgets_values": [ 120 | 6573, 121 | "increment" 122 | ] 123 | } 124 | ], 125 | "links": [ 126 | [ 127 | 119, 128 | 102, 129 | 0, 130 | 108, 131 | 0, 132 | "INT" 133 | ], 134 | [ 135 | 120, 136 | 108, 137 | 0, 138 | 106, 139 | 0, 140 | "STRING" 141 | ] 142 | ], 143 | "groups": [], 144 | "config": {}, 145 | "extra": { 146 | "ds": { 147 | "scale": 0.7513148009015781, 148 | "offset": [ 149 | 225.0933498881783, 150 | 41.518321694416585 151 | ] 152 | } 153 | }, 154 | "version": 0.4 155 | } -------------------------------------------------------------------------------- /__init__.py: -------------------------------------------------------------------------------- 1 | from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS 2 | 3 | __all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS'] -------------------------------------------------------------------------------- /allinoneworkflow.json: -------------------------------------------------------------------------------- 1 | { 2 | "last_node_id": 16, 3 | "last_link_id": 11, 4 | "nodes": [ 5 | { 6 | "id": 7, 7 | "type": "ShowText|pysssss", 8 | "pos": [ 9 | 1214, 10 | 338 11 | ], 12 | "size": { 13 | "0": 1404.90625, 14 | "1": 1109.595703125 15 | }, 16 | "flags": {}, 17 | "order": 2, 18 | "mode": 0, 19 | "inputs": [ 20 | { 21 | "name": "text", 22 | "type": "STRING", 23 | "link": 10, 24 | "widget": { 25 | "name": "text" 26 | } 27 | } 28 | ], 29 | "outputs": [ 30 | { 31 | "name": "STRING", 32 | "type": "STRING", 33 | "links": null, 34 | "shape": 6 35 | } 36 | ], 37 | "properties": { 38 | "Node name for S&R": "ShowText|pysssss" 39 | }, 40 | "widgets_values": [ 41 | "", 42 | "Failed to load frame. Either the video is over, the video path is wrong or there's another error. \nMake sure that you entered a direct path and that there are no \"s in the path." 43 | ] 44 | }, 45 | { 46 | "id": 15, 47 | "type": "AllInOnePlayer", 48 | "pos": [ 49 | 882, 50 | 341 51 | ], 52 | "size": [ 53 | 315, 54 | 130 55 | ], 56 | "flags": {}, 57 | "order": 1, 58 | "mode": 0, 59 | "inputs": [ 60 | { 61 | "name": "frame", 62 | "type": "INT", 63 | "link": 11, 64 | "widget": { 65 | "name": "frame" 66 | } 67 | } 68 | ], 69 | "outputs": [ 70 | { 71 | "name": "STRING", 72 | "type": "STRING", 73 | "links": [ 74 | 10 75 | ], 76 | "shape": 3, 77 | "slot_index": 0 78 | } 79 | ], 80 | "properties": { 81 | "Node name for S&R": "AllInOnePlayer" 82 | }, 83 | "widgets_values": [ 84 | 1, 85 | "", 86 | 100, 87 | 30 88 | ] 89 | }, 90 | { 91 | "id": 16, 92 | "type": "PrimitiveNode", 93 | "pos": [ 94 | 883, 95 | 515 96 | ], 97 | "size": [ 98 | 267.27526413341457, 99 | 82 100 | ], 101 | "flags": {}, 102 | "order": 0, 103 | "mode": 0, 104 | "outputs": [ 105 | { 106 | "name": "INT", 107 | "type": "INT", 108 | "links": [ 109 | 11 110 | ], 111 | "slot_index": 0, 112 | "widget": { 113 | "name": "frame" 114 | } 115 | } 116 | ], 117 | "title": "Frame Counter\n", 118 | "properties": { 119 | "Run widget replace on values": false 120 | }, 121 | "widgets_values": [ 122 | 1, 123 | "increment" 124 | ] 125 | } 126 | ], 127 | "links": [ 128 | [ 129 | 10, 130 | 15, 131 | 0, 132 | 7, 133 | 0, 134 | "STRING" 135 | ], 136 | [ 137 | 11, 138 | 16, 139 | 0, 140 | 15, 141 | 0, 142 | "INT" 143 | ] 144 | ], 145 | "groups": [], 146 | "config": {}, 147 | "extra": { 148 | "ds": { 149 | "scale": 0.7513148009015777, 150 | "offset": [ 151 | -552.0969407948444, 152 | -147.57064944942996 153 | ] 154 | } 155 | }, 156 | "version": 0.4 157 | } -------------------------------------------------------------------------------- /convert-video-color-cuda.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import os 4 | import cupy as cp 5 | from numba import cuda 6 | 7 | # Pre-compute color distances 8 | COLORS = { 9 | '🟥': (255, 0, 0), # Red 10 | '🟧': (255, 165, 0), # Orange 11 | '🟨': (255, 255, 0), # Yellow 12 | '🟩': (0, 255, 0), # Green 13 | '🟦': (0, 0, 255), # Blue 14 | '🟪': (128, 0, 128), # Purple 15 | '🟫': (165, 42, 42), # Brown 16 | '⬛': (0, 0, 0), # Black 17 | '⬜': (255, 255, 255) # White 18 | } 19 | 20 | COLOR_ARRAY = np.array(list(COLORS.values()), dtype=np.uint8) 21 | EMOJI_LIST = list(COLORS.keys()) 22 | 23 | @cuda.jit 24 | def get_closest_emoji_kernel(rgb_image, color_array, result): 25 | x, y = cuda.grid(2) 26 | if x < rgb_image.shape[0] and y < rgb_image.shape[1]: 27 | min_distance = 2147483647 # Max value for int32 28 | min_index = 0 29 | for i in range(color_array.shape[0]): 30 | distance = 0 31 | for c in range(3): 32 | diff = int(rgb_image[x, y, c]) - int(color_array[i, c]) 33 | distance += diff * diff 34 | if distance < min_distance: 35 | min_distance = distance 36 | min_index = i 37 | result[x, y] = min_index 38 | 39 | def image_to_emoji(image, width): 40 | height = int(image.shape[0] * width / image.shape[1]) 41 | resized = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) 42 | 43 | d_image = cuda.to_device(resized) 44 | d_color_array = cuda.to_device(COLOR_ARRAY) 45 | d_result = cuda.device_array((height, width), dtype=np.int32) 46 | 47 | threads_per_block = (16, 16) 48 | blocks_per_grid_x = int(np.ceil(height / threads_per_block[0])) 49 | blocks_per_grid_y = int(np.ceil(width / threads_per_block[1])) 50 | blocks_per_grid = (blocks_per_grid_x, blocks_per_grid_y) 51 | 52 | get_closest_emoji_kernel[blocks_per_grid, threads_per_block](d_image, d_color_array, d_result) 53 | 54 | result = d_result.copy_to_host() 55 | emoji_array = np.array(EMOJI_LIST)[result] 56 | 57 | return '\n'.join(''.join(row) for row in emoji_array) 58 | 59 | def video_to_emoji(video_path, output_folder, width): 60 | os.makedirs(output_folder, exist_ok=True) 61 | video = cv2.VideoCapture(video_path) 62 | 63 | frame_count = 0 64 | while True: 65 | success, frame = video.read() 66 | if not success: 67 | break 68 | frame_count += 1 69 | 70 | emoji_frame = image_to_emoji(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), width) 71 | 72 | with open(os.path.join(output_folder, f"{frame_count}.txt"), "w", encoding="utf-8") as f: 73 | f.write(emoji_frame) 74 | 75 | print(f"Processed frame {frame_count}") 76 | 77 | video.release() 78 | print("Video processing completed") 79 | 80 | # Usage 81 | video_path = input("Enter video path: ") 82 | try: 83 | width = int(input("Enter desired width (100 is usually best): ")) 84 | except: 85 | print("Invalid width, defaulting to 100") 86 | width = 100 87 | output_folder = "./frames" 88 | video_to_emoji(video_path, output_folder, width) -------------------------------------------------------------------------------- /convert-video-color.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | import numpy as np 3 | import os 4 | from concurrent.futures import ThreadPoolExecutor 5 | 6 | # Pre-compute color distances 7 | COLORS = { 8 | '🟥': (255, 0, 0), # Red 9 | '🟧': (255, 165, 0), # Orange 10 | '🟨': (255, 255, 0), # Yellow 11 | '🟩': (0, 255, 0), # Green 12 | '🟦': (0, 0, 255), # Blue 13 | '🟪': (128, 0, 128), # Purple 14 | '🟫': (165, 42, 42), # Brown 15 | '⬛': (0, 0, 0), # Black 16 | '⬜': (255, 255, 255) # White 17 | } 18 | 19 | COLOR_ARRAY = np.array(list(COLORS.values())) 20 | EMOJI_LIST = list(COLORS.keys()) 21 | 22 | def get_closest_emoji(rgb): 23 | distances = np.sum((COLOR_ARRAY - rgb) ** 2, axis=1) 24 | return EMOJI_LIST[np.argmin(distances)] 25 | 26 | def image_to_emoji(image, width): 27 | height = int(image.shape[0] * width / image.shape[1]) 28 | resized = cv2.resize(image, (width, height), interpolation=cv2.INTER_AREA) 29 | 30 | vectorized_get_closest = np.vectorize(get_closest_emoji, signature='(n)->()') 31 | emoji_array = vectorized_get_closest(resized.reshape(-1, 3)).reshape(height, width) 32 | 33 | return '\n'.join(''.join(row) for row in emoji_array) 34 | 35 | def process_frame(args): 36 | frame_number, frame, width, output_folder = args 37 | emoji_frame = image_to_emoji(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), width) 38 | with open(os.path.join(output_folder, f"{frame_number}.txt"), "w", encoding="utf-8") as f: 39 | f.write(emoji_frame) 40 | return frame_number 41 | 42 | def video_to_emoji(video_path, output_folder, width): 43 | os.makedirs(output_folder, exist_ok=True) 44 | video = cv2.VideoCapture(video_path) 45 | 46 | frame_count = 0 47 | frames = [] 48 | while True: 49 | success, frame = video.read() 50 | if not success: 51 | break 52 | frame_count += 1 53 | frames.append((frame_count, frame, width, output_folder)) 54 | 55 | video.release() 56 | 57 | with ThreadPoolExecutor() as executor: 58 | for processed_frame in executor.map(process_frame, frames): 59 | print(f"Processed frame {processed_frame}") 60 | 61 | print("Video processing completed") 62 | 63 | # Usage 64 | video_path = input("Enter video path: ") 65 | try: 66 | width = int(input("Enter desired width (100 is usually best): ")) 67 | except: 68 | print("Invalid width, defaulting to 100") 69 | width = 100 70 | output_folder = "./frames" 71 | video_to_emoji(video_path, output_folder, width) -------------------------------------------------------------------------------- /convert-video.py: -------------------------------------------------------------------------------- 1 | import cv2 2 | from PIL import Image 3 | import numpy as np 4 | import os 5 | 6 | def image_to_emoji(image, width): 7 | img = Image.fromarray(image).convert('L') 8 | #resize based on width 9 | aspect_ratio = img.height / img.width 10 | height = int(width * aspect_ratio) 11 | img = img.resize((width, height)) 12 | pixels = np.array(img) 13 | #set the black/white threshold to the mean value of the pixels 14 | threshold = np.mean(pixels) 15 | ascii_image = "" 16 | #this is quite a bit faster than the previous double for loop method 17 | emoji_array = np.where(pixels > threshold, "⬜", "⬛") 18 | ascii_image = '\n'.join([''.join(row) for row in emoji_array]) 19 | return ascii_image 20 | 21 | def video_to_ascii(video_path, output_folder, width): 22 | os.makedirs(output_folder, exist_ok=True) 23 | video = cv2.VideoCapture(video_path) 24 | frame_count = 0 25 | while True: 26 | success, frame = video.read() 27 | if not success: #if frame fails to load/doesn't exist 28 | break 29 | 30 | emoji_frame = image_to_emoji(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB), width) 31 | frame_count += 1 32 | with open(os.path.join(output_folder, f"{frame_count}.txt"), "w", encoding="utf-8") as f: 33 | f.write(emoji_frame) 34 | print(frame_count) 35 | 36 | video.release() 37 | print("done") 38 | 39 | # Usage 40 | video_path = input("Enter video path: ") 41 | try: 42 | width = int(input("Enter desired width (100 is usually best): ")) 43 | except: 44 | print("Invalid width, defaulting to 100") 45 | width = 100 46 | output_folder = "./frames" 47 | video_to_ascii(video_path, output_folder, width) -------------------------------------------------------------------------------- /cuda-requirements.txt: -------------------------------------------------------------------------------- 1 | cupy-cuda12x 2 | numba 3 | numpy 4 | opencv-python -------------------------------------------------------------------------------- /nodes.py: -------------------------------------------------------------------------------- 1 | import os 2 | import cv2 3 | import time 4 | import torch 5 | import shutil 6 | import numpy as np 7 | from PIL import Image 8 | from comfy.utils import ProgressBar 9 | 10 | class LoadFrame: 11 | @classmethod 12 | def INPUT_TYPES(s): 13 | return { 14 | "required": { 15 | "frame": ("INT", {"default": 1, "min": 1, "max": 100000, "step": 1, "forceInput": True}), 16 | "frameRate": ("INT", {"default": 0, "min": 0, "max": 144, "step": 1}), 17 | "path": ("STRING", {"forceInput": False}), 18 | }, 19 | } 20 | 21 | RETURN_TYPES = ("STRING", "FLOAT",) 22 | FUNCTION = "Loadframe" 23 | CATEGORY = "VideoPlayer" 24 | def Loadframe(self, frame, frameRate, path): 25 | try: 26 | path = path.replace('"', '') #make "copy as path" faster 27 | with open((path + str(frame) + ".txt"), 'r', encoding='utf-8') as file: 28 | content = file.read() 29 | timestamp = 0.00 30 | if frameRate != 0: 31 | time.sleep(1/int(frameRate)) 32 | timestamp = frame/frameRate 33 | return (content, timestamp,) 34 | except Exception as e: 35 | raise RuntimeError(f"Error reading file: {str(e)}") 36 | 37 | class LoadJPGFrame: 38 | @classmethod 39 | def INPUT_TYPES(s): 40 | return { 41 | "required": { 42 | "frame": ("INT", {"default": 1, "min": 1, "max": 100000, "step": 1, "forceInput": True}), 43 | "frameRate": ("INT", {"default": 0, "min": 0, "max": 144, "step": 1}), 44 | "path": ("STRING", {"default": "", "forceInput": False}), 45 | }, 46 | } 47 | 48 | RETURN_TYPES = ("IMAGE", "FLOAT",) 49 | FUNCTION = "load_jpg_frame" 50 | CATEGORY = "VideoPlayer" 51 | 52 | def load_jpg_frame(self, frame, frameRate, path): 53 | try: 54 | path = path.replace('"', '') #make "copy as path" faster 55 | image_path = os.path.join(path, f"{frame:05d}.jpg") 56 | with Image.open(image_path) as img: 57 | img = img.convert("RGB") 58 | image = np.array(img).astype(np.float32) / 255.0 59 | #Convert to PyTorch tensor and add batch dimension 60 | image = torch.from_numpy(image)[None,] 61 | timestamp = 0.00 62 | if frameRate != 0: 63 | time.sleep(1/int(frameRate)) 64 | timestamp = frame/frameRate 65 | return (image, timestamp,) 66 | except Exception as e: 67 | raise RuntimeError(f"Error reading image file: {str(e)}") 68 | 69 | class LoadVideoFrame: 70 | @classmethod 71 | def INPUT_TYPES(s): 72 | return { 73 | "required": { 74 | "video_path": ("STRING", {"default": ""}), 75 | "frame": ("INT", {"default": 1, "min": 1, "max": 100000, "step": 1, "forceInput": True}), 76 | "frameRate": ("INT", {"default": 0, "min": 0, "max": 144, "step": 1}), 77 | }, 78 | } 79 | 80 | RETURN_TYPES = ("IMAGE", "FLOAT",) 81 | FUNCTION = "LoadVideoFrame" 82 | CATEGORY = "VideoPlayer" 83 | 84 | def LoadVideoFrame(self, video_path, frame, frameRate): 85 | try: 86 | video_path = video_path.replace('"', '') #make "copy as path" faster 87 | cap = cv2.VideoCapture(video_path) 88 | cap.set(cv2.CAP_PROP_POS_FRAMES, frame - 1) # Subtract 1 because frame count starts at 0 89 | ret, img = cap.read() 90 | img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 91 | pil_image = Image.fromarray(img_rgb) 92 | image = np.array(pil_image).astype(np.float32) / 255.0 93 | #Convert to PyTorch tensor and add batch dimension 94 | image_tensor = torch.from_numpy(image)[None,] 95 | timestamp = 0.00 96 | if frameRate != 0: 97 | time.sleep(1/int(frameRate)) 98 | timestamp = frame/frameRate 99 | cap.release() 100 | return (image_tensor, timestamp,) 101 | except Exception as e: 102 | raise RuntimeError(f"Error reading video frame: {str(e)}") 103 | 104 | class ImageToEmoji: 105 | @classmethod 106 | def INPUT_TYPES(s): 107 | return { 108 | "required": { 109 | "image": ("IMAGE",), 110 | "width": ("INT", {"default": 100, "min": 10, "max": 200, "step": 1}), 111 | }, 112 | } 113 | 114 | RETURN_TYPES = ("STRING",) 115 | FUNCTION = "ImageToEmoji" 116 | CATEGORY = "VideoPlayer" 117 | 118 | def ImageToEmoji(self, image, width): 119 | #Convert from tensor to array 120 | image_np = 255. * image.cpu().numpy().squeeze() 121 | image_np = np.clip(image_np, 0, 255).astype(np.uint8) 122 | img = Image.fromarray(image_np).convert('L') 123 | #resize 124 | aspect_ratio = img.height / img.width 125 | height = int(width * aspect_ratio) 126 | img = img.resize((width, height)) 127 | pixels = np.array(img) 128 | #Set the black/white threshold to the mean value of the pixels 129 | threshold = np.mean(pixels) 130 | emoji_array = np.where(pixels > threshold, "⬜", "⬛") 131 | ascii_image = '\n'.join([''.join(row) for row in emoji_array]) 132 | return(ascii_image,) 133 | 134 | class AllInOnePlayer: 135 | @classmethod 136 | def INPUT_TYPES(s): 137 | return { 138 | "required": { 139 | "frame": ("INT", {"default": 1, "min": 1, "max": 100000, "step": 1, "forceInput": True}), 140 | "video_path": ("STRING", {"forceInput": False}), 141 | "width": ("INT", {"default": 100, "min": 10, "max": 200, "step": 1}), 142 | "framerate": ("INT", {"default": 30, "min": 0, "max": 500, "step": 1}), 143 | }, 144 | } 145 | 146 | RETURN_TYPES = ("STRING",) 147 | FUNCTION = "PlayVideo" 148 | CATEGORY = "VideoPlayer" 149 | 150 | def __init__(self): 151 | self.node_dir = os.path.dirname(os.path.abspath(__file__)) 152 | 153 | def ImageToEmoji(self, image, width): 154 | if len(image.shape) == 3: 155 | image = np.mean(image, axis=2).astype(np.uint8) 156 | else: 157 | image = image.astype(np.uint8) 158 | img = Image.fromarray(image) 159 | pixels = np.array(img) 160 | threshold = np.mean(pixels) 161 | emoji_array = np.where(pixels > threshold, "⬜", "⬛") 162 | ascii_image = '\n'.join([''.join(row) for row in emoji_array]) 163 | return ascii_image 164 | 165 | def get_video_prefix(self, video_path): 166 | return os.path.splitext(os.path.basename(video_path))[0] 167 | 168 | def resize_frame(self, frame, target_width): 169 | height, width = frame.shape[:2] 170 | aspect_ratio = height / width 171 | new_height = int(target_width * aspect_ratio) 172 | return cv2.resize(frame, (target_width, new_height), interpolation=cv2.INTER_AREA) 173 | 174 | def ExtractFrames(self, video_path, target_width): 175 | temp_frames_dir = os.path.join(self.node_dir, "temp_frames") 176 | shutil.rmtree(temp_frames_dir, ignore_errors=True) 177 | os.makedirs(temp_frames_dir) 178 | video_prefix = self.get_video_prefix(video_path) 179 | cap = cv2.VideoCapture(video_path) 180 | total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) 181 | #Initialize progress bar 182 | progress = ProgressBar(10) 183 | frame_count = 0 184 | next_progress_update = total_frames // 10 # Calculate frames per 10% 185 | 186 | while True: 187 | ret, frame = cap.read() 188 | if not ret: 189 | break 190 | resized_frame = self.resize_frame(frame, target_width) 191 | emoji_frame = self.ImageToEmoji(resized_frame, target_width) 192 | 193 | frame_filename = os.path.join(temp_frames_dir, f"{video_prefix}_frame_{frame_count:04d}.txt") 194 | with open(frame_filename, 'w', encoding='utf-8') as f: 195 | f.write(emoji_frame) 196 | 197 | frame_count += 1 198 | #Update progress bar every 10% of frames processed 199 | if frame_count >= next_progress_update: 200 | progress.update(1) 201 | next_progress_update += total_frames // 10 202 | 203 | cap.release() 204 | 205 | def PlayVideo(self, frame, video_path, width, framerate): 206 | video_path = video_path.replace('"', '') #make "copy as path" faster 207 | progress = ProgressBar(10) 208 | progress.update 209 | temp_frames_dir = os.path.join(self.node_dir, "temp_frames") 210 | video_prefix = self.get_video_prefix(video_path) 211 | 212 | if not os.path.exists(temp_frames_dir) or not any(f.startswith(video_prefix) for f in os.listdir(temp_frames_dir)): 213 | self.ExtractFrames(video_path, width) 214 | 215 | frame_path = os.path.join(temp_frames_dir, f"{video_prefix}_frame_{frame:04d}.txt") 216 | if os.path.exists(frame_path): 217 | with open(frame_path, 'r', encoding='utf-8') as f: 218 | emoji_frame = f.read() 219 | 220 | if framerate != 0: 221 | time.sleep(1/framerate) 222 | return (emoji_frame,) 223 | else: 224 | return ("Failed to load frame. Either the video is over, the video path is wrong, or there's another error.",) 225 | 226 | NODE_CLASS_MAPPINGS = { 227 | "LoadFrame": LoadFrame, 228 | "LoadJPGFrame": LoadJPGFrame, 229 | "LoadVideoFrame": LoadVideoFrame, 230 | "ImageToEmoji": ImageToEmoji, 231 | "AllInOnePlayer": AllInOnePlayer, 232 | } 233 | 234 | NODE_DISPLAY_NAME_MAPPINGS = { 235 | "LoadFrame": "LoadFrame", 236 | "LoadJPGFrame": "LoadJPGFrame", 237 | "LoadVideoFrame": "Load Video Frame", 238 | "ImageToEmoji": "Image To Emoji", 239 | "AllInOnePlayer": "AllInOnePlayer", 240 | } -------------------------------------------------------------------------------- /pyproject.toml: -------------------------------------------------------------------------------- 1 | [project] 2 | name = "comfyui-videoplayer" 3 | description = "A silly POC Video Player for ComfyUI" 4 | version = "1.0.0" 5 | license = {file = "LICENSE"} 6 | 7 | [project.urls] 8 | Repository = "https://github.com/BetaDoggo/ComfyUI-VideoPlayer" 9 | # Used by Comfy Registry https://comfyregistry.org 10 | 11 | [tool.comfy] 12 | PublisherId = "betadoggo" 13 | DisplayName = "ComfyUI-VideoPlayer" 14 | Icon = "" 15 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python --------------------------------------------------------------------------------