Ethanfel/ComfyUI-Sharp-Selector
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Merge pull request 'parralel' (#2) from parralel into main

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Reviewed-on: #2
This commit was merged in pull request #2.
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Ethanfel committed 2026-01-19 12:30:42 +01:00
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__init__.py
+5 -2
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@@ -1,13 +1,16 @@
from .sharp_node import SharpnessAnalyzer, SharpFrameSelector
from .parallel_loader import ParallelSharpnessLoader
NODE_CLASS_MAPPINGS = {
"SharpnessAnalyzer": SharpnessAnalyzer,
"SharpFrameSelector": SharpFrameSelector
"SharpFrameSelector": SharpFrameSelector,
"ParallelSharpnessLoader": ParallelSharpnessLoader
}
NODE_DISPLAY_NAME_MAPPINGS = {
"SharpnessAnalyzer": "1. Sharpness Analyzer",
"SharpFrameSelector": "2. Sharp Frame Selector"
"SharpFrameSelector": "2. Sharp Frame Selector",
"ParallelSharpnessLoader": "3. Parallel Video Loader (Sharpness)"
}
__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
assets/nodes.png
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example_workflows/comfyui-sharp-example.json
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"📌 ComfyUI Sharpness Tools Explained\n\n1. Parallel Video Loader (The \"Loader\")\n\n Best for: Processing long videos or movie files directly from disk.\n\n How it works: It opens the video file itself and uses multi-threading (parallel CPU cores) to scan thousands of frames without loading them into memory. It only \"decodes\" the final few sharpest frames.\n\n Use Case: Extracting dataset images, finding high-quality frames from a raw movie file, or scanning 10,000 frames without crashing your RAM.\n\n Key Feature: Features a \"Batch Counter\" to automatically page through long videos (e.g., scan minute 0-1, then minute 1-2).\n\n2. Standard Sharpness Duo (The \"Filter\")\n\n Best for: Processing images already inside your workflow (e.g., after an img2img pass, or a short generated GIF).\n\n How it works:\n\n Node A (Analyzer): Assigns a score to every image in the batch.\n\n Node B (Selector): Picks the best ones based on those scores.\n\n Use Case: Filtering bad generations, picking the best frame from a small batch of AnimateDiff results, or cleaning up a sequence.\n\n Limitation: It is single-threaded and requires all images to be loaded in VRAM/RAM first (slow for long videos).\n\n🚀 Which one to use?\n\n Starting from a Video File? → Use Parallel Loader.\n\n Starting from a Generation/Latent? → Use Standard Duo."
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example_workflows/comfyui-sharp.json
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"filename": "ComfyUI_temp_ytveg_00028_.png",
"subfolder": "",
"type": "temp"
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],
"isExecuting": false
},
{
"id": 21,
"type": "easy showAnything",
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"size": [
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"flags": {},
"order": 1,
"mode": 0,
"inputs": [
{
"name": "anything",
"shape": 7,
"type": "*",
"link": 24
}
],
"outputs": [
{
"name": "output",
"type": "*",
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}
],
"properties": {
"cnr_id": "comfyui-easy-use",
"ver": "5dfcbcf51d8a6efed947bc7bdd6797827fecab55",
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"type": "IMAGE",
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},
{
"name": "scores",
"type": "SHARPNESS_SCORES",
"link": 3
}
],
"outputs": [
{
"name": "selected_images",
"type": "IMAGE",
"links": [
22
]
},
{
"name": "count",
"type": "INT",
"links": null
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"type": "VHS_BatchManager",
"link": 25
},
{
"name": "vae",
"shape": 7,
"type": "VAE",
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],
"outputs": [
{
"name": "IMAGE",
"type": "IMAGE",
"links": [
1,
17
]
},
{
"name": "frame_count",
"type": "INT",
"links": null
},
{
"name": "audio",
"type": "AUDIO",
"links": null
},
{
"name": "video_info",
"type": "VHS_VIDEOINFO",
"links": null
}
],
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"ue_links": [],
"links_added_by_ue": [],
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"VHS_MetadataImage": true,
"VHS_KeepIntermediate": true
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}
js/sharp_tooltips.js
-35
View File
@@ -1,35 +0,0 @@
import { app } from "../../scripts/app.js";
app.registerExtension({
name: "SharpFrames.Tooltips",
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeData.name === "SharpFrameSelector") {
const tooltips = {
// Must match Python INPUT_TYPES keys exactly
"selection_method": "Strategy:\n• 'batched': Best for video. Splits time into slots.\n• 'best_n': Global top sharpest frames.",
"batch_size": "For 'batched' mode.\nSize of the analysis window (in frames).",
"batch_buffer": "For 'batched' mode.\nFrames to skip AFTER each batch (dead zone).",
"num_frames": "For 'best_n' mode.\nTotal frames to output.",
"min_sharpness": "Threshold Filter.\nDiscard frames with score below this.\nNote: Scores are lower on resized images.",
"images": "Input High-Res images.",
"scores": "Input Sharpness Scores from Analyzer."
};
const onNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = function () {
onNodeCreated?.apply(this, arguments);
if (this.widgets) {
for (const w of this.widgets) {
if (tooltips[w.name]) {
w.tooltip = tooltips[w.name];
// Force update for immediate feedback
w.options = w.options || {};
w.options.tooltip = tooltips[w.name];
}
}
}
};
}
},
});
parallel_loader.py
+129
View File
@@ -0,0 +1,129 @@
import cv2
import torch
import numpy as np
import concurrent.futures
import os
class ParallelSharpnessLoader:
@classmethod
def INPUT_TYPES(s):
return {
"required": {
"video_path": ("STRING", {"default": "C:\\path\\to\\video.mp4"}),
# BATCHING CONTROLS
"batch_index": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1, "label": "Batch Counter (Auto-Increment)"}),
"scan_limit": ("INT", {"default": 1440, "min": 1, "max": 10000000, "step": 1, "label": "Frames per Batch"}),
# STANDARD CONTROLS
"frame_scan_step": ("INT", {"default": 5, "min": 1, "step": 1, "label": "Analyze Every Nth Frame"}),
"return_count": ("INT", {"default": 4, "min": 1, "max": 1024, "step": 1, "label": "Best Frames to Return"}),
"min_distance": ("INT", {"default": 24, "min": 0, "max": 10000, "step": 1, "label": "Min Distance (Frames)"}),
"manual_skip_start": ("INT", {"default": 0, "min": 0, "max": 10000000, "step": 1, "label": "Global Start Offset"}),
},
}
# Added a 4th output: STRING (The status sentence)
RETURN_TYPES = ("IMAGE", "STRING", "INT", "STRING")
RETURN_NAMES = ("images", "scores_info", "batch_int", "batch_status")
FUNCTION = "load_video"
CATEGORY = "BetaHelper/Video"
def calculate_sharpness(self, frame_data):
gray = cv2.cvtColor(frame_data, cv2.COLOR_BGR2GRAY)
return cv2.Laplacian(gray, cv2.CV_64F).var()
def load_video(self, video_path, batch_index, scan_limit, frame_scan_step, return_count, min_distance, manual_skip_start):
# 1. Validation
if not os.path.exists(video_path):
video_path = video_path.strip('"')
if not os.path.exists(video_path):
raise FileNotFoundError(f"Video not found: {video_path}")
# 2. Calculate Offsets
current_skip = (batch_index * scan_limit) + manual_skip_start
range_end = current_skip + scan_limit
# Create the Status String
status_msg = f"Batch {batch_index}: Skipped {current_skip} frames. Scanning range {current_skip} -> {range_end}."
print(f"xx- Parallel Loader | {status_msg}")
cap = cv2.VideoCapture(video_path)
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if current_skip >= total_frames:
print("xx- End of video reached.")
empty_img = torch.zeros((1, 64, 64, 3))
return (empty_img, "End of Video", batch_index, "End of Video Reached")
# 3. Scanning (Pass 1)
if current_skip > 0:
cap.set(cv2.CAP_PROP_POS_FRAMES, current_skip)
frame_scores = []
current_frame = current_skip
scanned_count = 0
with concurrent.futures.ThreadPoolExecutor(max_workers=16) as executor:
futures = []
while True:
if scanned_count >= scan_limit:
break
ret, frame = cap.read()
if not ret: break
future = executor.submit(self.calculate_sharpness, frame)
futures.append((current_frame, future))
scanned_count += 1
# Manual Stepping
if frame_scan_step > 1:
for _ in range(frame_scan_step - 1):
if not cap.grab(): break
current_frame += 1
current_frame += 1
for idx, future in futures:
frame_scores.append((idx, future.result()))
cap.release()
# 4. Selection
if not frame_scores:
return (torch.zeros((1, 64, 64, 3)), "No frames in batch", batch_index, status_msg + " (No frames found)")
frame_scores.sort(key=lambda x: x[1], reverse=True)
selected = []
for idx, score in frame_scores:
if len(selected) >= return_count: break
if all(abs(s[0] - idx) >= min_distance for s in selected):
selected.append((idx, score))
selected.sort(key=lambda x: x[0])
# 5. Extraction (Pass 2)
cap = cv2.VideoCapture(video_path)
output_tensors = []
info_log = []
for idx, score in selected:
cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
ret, frame = cap.read()
if ret:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frame = frame.astype(np.float32) / 255.0
output_tensors.append(torch.from_numpy(frame))
info_log.append(f"F:{idx} (Score:{int(score)})")
cap.release()
if not output_tensors:
return (torch.zeros((1, 64, 64, 3)), "Extraction Failed", batch_index, status_msg)
# Return all 4 outputs
return (torch.stack(output_tensors), ", ".join(info_log), batch_index, status_msg)
readme
+22 -20
View File
@@ -1,29 +1,31 @@
# 🔪 ComfyUI Sharp Frame Selector
A suite of custom nodes for [ComfyUI](https://github.com/comfyanonymous/ComfyUI) designed to intelligently extract the sharpest frames from video footage.
A collection of high-performance custom nodes for **ComfyUI** designed to detect blur, calculate sharpness scores, and automatically extract the best frames from videos or image batches.
Based on the [sharp-frames](https://github.com/Reflct/sharp-frames-python) logic, this tool uses **Laplacian Variance** to score image clarity. It is optimized for high-resolution video processing using a **Sidechain Workflow** that saves massive amounts of RAM.
This pack includes two distinct approaches:
1. **Parallel Video Loader:** A multi-threaded, path-based loader for processing massive video files directly from disk (Low RAM usage).
2. **Standard Sharpness Duo:** A classic filter setup for processing images/latents *inside* your existing workflow.
## ✨ Key Features
---
---
* **Sidechain Optimization:** Analyze lightweight 512px proxy images to control the selection of heavy 4K raw frames.
* **Batched Extraction:** Splits video into time slots (e.g., 1 second) and picks the single best frame from each slot. Perfect for ensuring action scenes are not missed.
* **Threshold Filtering:** Automatically discards frames that are too blurry, even if they are the "winner" of their batch.
* **Buffer Control:** Optional dead-zones between batches to reduce frame count or ensure temporal separation.
## 🚀 Key Features
### 1. New: Parallel Video Loader (Path-Based)
* **Zero-RAM Scanning:** Scans video files directly from disk without decoding every frame to memory.
* **Multi-Threaded:** Uses all CPU cores to calculate sharpness scores at high speed.
* **Batching Support:** Includes a "Page" system to process long movies in chunks (e.g., minute-by-minute) without restarting ComfyUI.
* **Smart Selection:** Automatically skips "adjacent" frames to ensure you get a diverse selection of sharp images.
### 2. Standard Sharpness Duo (Tensor-Based)
* **Workflow Integration:** Works with any node that outputs an `IMAGE` batch (e.g., AnimateDiff, VideoHelperSuite).
* **Precision Filtering:** Sorts and filters generated frames before saving or passing to a second pass (img2img).
---
## 🚀 Installation
## 📦 Installation
### Option 1: ComfyUI Manager (Recommended)
1. Open ComfyUI Manager.
2. Search for **"Sharp Frame Selector"**.
3. Click **Install**.
### Option 2: Manual Installation
Clone this repository into your `custom_nodes` folder:
```bash
cd ComfyUI/custom_nodes/
git clone [https://github.com/YOUR_USERNAME/ComfyUI-Sharp-Selector.git](https://github.com/YOUR_USERNAME/ComfyUI-Sharp-Selector.git)
pip install -r ComfyUI-Sharp-Selector/requirements.txt
1. Clone this repository into your `custom_nodes` folder:
```bash
cd ComfyUI/custom_nodes/
git clone https://github.com/ethanfel/ComfyUI-Sharp-Selector.git