refactor: Add 'result' output to AQ_SendImageToAPI node
Based on your feedback, this commit further modifies the `AQ_SendImageToAPI` node in `nodes/api/AQ_SendImageToAPI.py`:
- **Added `result` output string:**
- `RETURN_TYPES` updated to `("STRING", "STRING",)`.
- `RETURN_NAMES` updated to `("instruction", "result",)`.
- The `send_images_to_api` method now captures the outcome of the API call (or image processing).
- If successful, the `result` string will contain the HTTP status and response from the server.
- If an error occurs (e.g., network issue, server error), the `result` string will contain the error message.
- If no images are provided, a message indicating this is returned in the `result` string.
- This `result` output provides a way to access the final status of the API interaction within the ComfyUI workflow, complementing the real-time UI updates from `update_node_status`.
This commit is contained in:
google-labs-jules[bot]
+5
-2
@@ -3,13 +3,15 @@ from .nodes.transformers.AQ_Qwen import AQ_Qwen
|
||||
from .nodes.transformers.AQ_Qwen import AQ_QwenLoader
|
||||
from .nodes.pulid.AQ_multiface_ApplyPulidFlux import AQ_multiface_ApplyPulidFlux
|
||||
from .nodes.api.AQ_Gemini import AQ_Gemini
|
||||
from .nodes.api.AQ_SendImageToAPI import AQ_SendImageToAPI
|
||||
|
||||
NODE_CLASS_MAPPINGS = {
|
||||
"AQ_MasksAndImagesAsList": AQ_MasksAndImagesAsList,
|
||||
"AQ_multiface_ApplyPulidFlux": AQ_multiface_ApplyPulidFlux,
|
||||
"AQ_Qwen": AQ_Qwen,
|
||||
"AQ_QwenLoader": AQ_QwenLoader,
|
||||
"AQ_Gemini": AQ_Gemini
|
||||
"AQ_Gemini": AQ_Gemini,
|
||||
"AQ_SendImageToAPI": AQ_SendImageToAPI
|
||||
}
|
||||
|
||||
NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
@@ -17,7 +19,8 @@ NODE_DISPLAY_NAME_MAPPINGS = {
|
||||
"AQ_multiface_ApplyPulidFlux": "AQ_multiface_ApplyPulidFlux",
|
||||
"AQ_Qwen": "AQ_Qwen",
|
||||
"AQ_QwenLoader": "AQ_QwenLoader",
|
||||
"AQ_Gemini": "AQ_Gemini"
|
||||
"AQ_Gemini": "AQ_Gemini",
|
||||
"AQ_SendImageToAPI": "Aquasite Send Image to API"
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,209 @@
|
||||
import os
|
||||
import torch
|
||||
from PIL import Image
|
||||
import numpy as np
|
||||
import requests
|
||||
import io
|
||||
import base64
|
||||
import gzip
|
||||
import logging
|
||||
import pillow_avif # Ensure this is installed if you're using AVIF
|
||||
import time
|
||||
|
||||
from server import PromptServer
|
||||
|
||||
class AQ_SendImageToAPI:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {
|
||||
"required": {
|
||||
"images": ("IMAGE",),
|
||||
"api_endpoint": ("STRING", {"default": "https://yourapiendpoint.com/upload"}),
|
||||
"api_key": ("STRING", {"default": "YOUR_API_KEY"}),
|
||||
"batch_size": ("INT", {"default": 10, "min": 1, "max": 100}),
|
||||
"compression_level": ("INT", {"default": 6, "min": 0, "max": 9}),
|
||||
"image_format": (["PNG", "JPEG", "WEBP", "AVIF"], {"default": "PNG"}),
|
||||
"jpeg_quality": ("INT", {"default": 85, "min": 1, "max": 100}),
|
||||
},
|
||||
"optional": {
|
||||
"output_previews": ("BOOLEAN", {"default": True}),
|
||||
}
|
||||
}
|
||||
|
||||
RETURN_TYPES = ("STRING", "STRING",)
|
||||
RETURN_NAMES = ("instruction", "result",)
|
||||
FUNCTION = "send_images_to_api"
|
||||
OUTPUT_NODE = True
|
||||
CATEGORY = "Aquasite/API"
|
||||
|
||||
def send_images_to_api(self, images, api_endpoint, api_key, batch_size, compression_level, image_format, jpeg_quality, output_previews=True):
|
||||
instruction_string = """
|
||||
**AQ_SendImageToAPI Node Instructions**
|
||||
|
||||
Purpose:
|
||||
This node sends images to a specified API endpoint. It processes images in batches,
|
||||
compresses them, and transmits them to the server.
|
||||
|
||||
Inputs:
|
||||
- images: The images to be sent.
|
||||
- api_endpoint (URL): The server API endpoint where images will be sent.
|
||||
- api_key (String): The authorization key for API access.
|
||||
- batch_size (Int): Number of images to send in each batch (1-100).
|
||||
- compression_level (Int): Gzip compression level for image data (0-9).
|
||||
- image_format (String): Format for the image ('PNG', 'JPEG', 'WEBP', 'AVIF').
|
||||
- jpeg_quality (Int): Quality for JPEG/WEBP/AVIF formats (1-100).
|
||||
- output_previews (Boolean): Whether to print a summary message to the console.
|
||||
|
||||
(Note: The following parameters are described for completeness but are not all currently active inputs in this version of the node: `image_name_prefix`, `jobId`, `userId`, `send_gzipped` as a direct boolean toggle for gzip.)
|
||||
|
||||
- image_name_prefix (String): Optional prefix for image filenames (conceptual).
|
||||
- quality (Int): Corresponds to `jpeg_quality` for WEBP/JPG/AVIF.
|
||||
- jobId (String): Optional Job ID to associate with these images on the server (conceptual).
|
||||
- userId (String): Optional User ID to associate with these images on the server (conceptual).
|
||||
- send_gzipped (Boolean): Data is currently always gzipped; `compression_level` controls the degree.
|
||||
|
||||
Node Behavior:
|
||||
- This is an OUTPUT_NODE. It does not pass data directly to other nodes in the ComfyUI workflow via its main return path. Its primary function is to send data externally.
|
||||
- The node provides status updates during processing (e.g., progress of image uploads) via the `update_node_status` method, which communicates with the ComfyUI frontend.
|
||||
|
||||
Output:
|
||||
- instruction (String): This instructional text.
|
||||
- result (String): A string detailing the outcome of the last API call attempt.
|
||||
"""
|
||||
if not images:
|
||||
return (instruction_string, "No images to process.",)
|
||||
|
||||
results = [] # This stores the JSON responses from the API for each batch
|
||||
overall_result_string = "No API call attempted yet." # Initialize for the final return
|
||||
total_images = len(images)
|
||||
processed_images_count = 0
|
||||
successful_uploads = 0
|
||||
failed_uploads = 0
|
||||
|
||||
# Get the client ID for progress updates
|
||||
prompt_server = PromptServer.instance
|
||||
client_id = prompt_server.client_id if prompt_server else None
|
||||
|
||||
for i in range(0, total_images, batch_size):
|
||||
batch_images = images[i:i + batch_size]
|
||||
batch_payload = []
|
||||
|
||||
for image_tensor in batch_images:
|
||||
# Convert tensor to PIL Image
|
||||
image_np = image_tensor.cpu().numpy() # Change to float32 or uint8 if needed
|
||||
if image_np.min() < 0 or image_np.max() > 1: # if not in [0,1] range
|
||||
image_np = (image_np - image_np.min()) / (image_np.max() - image_np.min()) # normalize
|
||||
|
||||
if image_np.ndim == 3 and image_np.shape[0] in [1, 3, 4]: # HWC for grayscale, RGB, RGBA
|
||||
image_np = image_np.transpose(1, 2, 0) # CHW to HWC
|
||||
|
||||
image_np = (image_np * 255).astype(np.uint8)
|
||||
pil_image = Image.fromarray(image_np)
|
||||
|
||||
# Convert image to specified format and compress
|
||||
img_byte_arr = io.BytesIO()
|
||||
save_params = {}
|
||||
if image_format == "JPEG":
|
||||
save_params['quality'] = jpeg_quality
|
||||
pil_image.save(img_byte_arr, format='JPEG', **save_params)
|
||||
elif image_format == "WEBP":
|
||||
save_params['quality'] = jpeg_quality # webp also uses quality
|
||||
pil_image.save(img_byte_arr, format='WEBP', **save_params)
|
||||
elif image_format == "AVIF":
|
||||
save_params['quality'] = jpeg_quality # avif also uses quality
|
||||
# AVIF might require specific saving parameters, ensure pillow_avif is correctly used
|
||||
pil_image.save(img_byte_arr, format='AVIF', **save_params)
|
||||
else: # Default to PNG
|
||||
pil_image.save(img_byte_arr, format='PNG')
|
||||
|
||||
img_byte_arr = img_byte_arr.getvalue()
|
||||
|
||||
# Compress with gzip
|
||||
compressed_data = gzip.compress(img_byte_arr, compresslevel=compression_level)
|
||||
|
||||
# Encode to base64
|
||||
encoded_data = base64.b64encode(compressed_data).decode('utf-8')
|
||||
batch_payload.append({"image_data": encoded_data, "format": image_format.lower()})
|
||||
|
||||
if not batch_payload:
|
||||
continue
|
||||
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
"Content-Encoding": "gzip" # Inform server that payload is gzipped
|
||||
}
|
||||
|
||||
result_string_for_batch = "No API call attempted for this batch." # Initialize for current batch
|
||||
try:
|
||||
json_payload = {"images": batch_payload}
|
||||
response = requests.post(api_endpoint, json=json_payload, headers=headers)
|
||||
response.raise_for_status()
|
||||
|
||||
# Successfully got a response
|
||||
response_data = response.json()
|
||||
results.append(response_data) # Store structured response
|
||||
successful_uploads += len(batch_payload)
|
||||
result_string_for_batch = f"Success: Status {response.status_code} - {response_data}"
|
||||
|
||||
except requests.exceptions.RequestException as e:
|
||||
logging.error(f"Error sending batch to API: {e}")
|
||||
error_info = {"error": str(e), "status_code": e.response.status_code if e.response else 'N/A'}
|
||||
results.append(error_info) # Store error info
|
||||
failed_uploads += len(batch_payload)
|
||||
result_string_for_batch = f"Error: {str(e)}" # Update result string with error
|
||||
|
||||
overall_result_string = result_string_for_batch # Update overall result with the latest batch status
|
||||
|
||||
processed_images_count += len(batch_payload)
|
||||
if client_id:
|
||||
# The update_node_status is for UI progress, not for the data output string.
|
||||
# It uses counts, not the result_string_for_batch.
|
||||
self.update_node_status(client_id, total_images, processed_images_count, successful_uploads, failed_uploads)
|
||||
|
||||
final_summary_message = f"Overall Processed: {processed_images_count}/{total_images} images. Successful Uploads: {successful_uploads}, Failed Uploads: {failed_uploads}."
|
||||
if output_previews:
|
||||
print(final_summary_message)
|
||||
|
||||
# Final status update to UI, if client_id is available
|
||||
if client_id:
|
||||
self.update_node_status(client_id, total_images, processed_images_count, successful_uploads, failed_uploads)
|
||||
|
||||
return (instruction_string, overall_result_string,)
|
||||
|
||||
def update_node_status(self, client_id, total_images, processed_count, successful_uploads, failed_uploads):
|
||||
"""Sends progress updates to the ComfyUI client."""
|
||||
progress = processed_count / total_images
|
||||
# This is a simplified progress update. For more detailed UI updates,
|
||||
# you might need to interact with ComfyUI's JavaScript side.
|
||||
# The `progress` value could be sent via websockets if `client_id` is available.
|
||||
# For now, we'll log it, which might appear in the console where ComfyUI is run.
|
||||
|
||||
# Note: Direct UI updates from Python are limited.
|
||||
# This function is more of a placeholder for where such logic would go.
|
||||
# Actual progress bar updates often require custom front-end code.
|
||||
|
||||
# Example of sending a progress event (requires PromptServer and client_id)
|
||||
if PromptServer.instance and client_id:
|
||||
PromptServer.instance.send_sync("progress", {"value": processed_count, "max": total_images, "sid": client_id}, client_id)
|
||||
# You could also send custom messages for successful/failed uploads if your UI handles them
|
||||
# For example:
|
||||
# PromptServer.instance.send_sync("custom_event_name", {"successful": successful_uploads, "failed": failed_uploads, "sid": client_id}, client_id)
|
||||
|
||||
logging.info(f"Progress (client {client_id}): {processed_count}/{total_images} images. Successful: {successful_uploads}, Failed: {failed_uploads}.")
|
||||
|
||||
if __name__ == "__main__":
|
||||
# This section is for testing outside of ComfyUI if needed.
|
||||
# Example:
|
||||
# sender = AQ_SendImageToAPI()
|
||||
# Create some dummy image tensors (e.g., 1xHxWx3)
|
||||
# dummy_image = torch.rand(1, 64, 64, 3)
|
||||
# result = sender.send_images_to_api(images=[dummy_image, dummy_image],
|
||||
# api_endpoint="YOUR_TEST_ENDPOINT",
|
||||
# api_key="YOUR_TEST_KEY",
|
||||
# batch_size=1,
|
||||
# compression_level=6,
|
||||
# image_format="PNG",
|
||||
# jpeg_quality=85)
|
||||
# print(result)
|
||||
pass
|
||||
@@ -5,6 +5,7 @@ onnxruntime-gpu
|
||||
facexlib
|
||||
segment-anything
|
||||
dill
|
||||
pillow-avif
|
||||
qwen-vl-utils
|
||||
qwen-vl-utils[decord]
|
||||
|
||||
|
||||
Reference in New Issue
Block a user