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Add node to send image into chosen API endpoint using post and optionally API KEY

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pk
2025-05-22 09:30:28 +02:00
parent 9f61280ea7
commit a0c663ba1f
4 changed files with 52 additions and 58 deletions
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.gitignore
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@@ -0,0 +1 @@
__pycache__
__init__.py
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@@ -20,7 +20,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
"AQ_Qwen": "AQ_Qwen",
"AQ_QwenLoader": "AQ_QwenLoader",
"AQ_Gemini": "AQ_Gemini",
"AQ_SendImageToAPI": "Aquasite Send Image to API"
"AQ_SendImageToAPI": "AQ_SendImageToAPI"
}
nodes/api/AQ_SendImageToAPI.py
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@@ -21,12 +21,16 @@ class AQ_SendImageToAPI:
"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}),
"quality": ("INT", {"default": 85, "min": 1, "max": 100}),
},
"optional": {
"output_previews": ("BOOLEAN", {"default": True}),
"image_name_prefix": ("STRING", {"default": "image"}),
"jobId": ("STRING", {"default": ""}),
"userId": ("STRING", {"default": ""}),
"compression_level": ("INT", {"default": 6, "min": 0, "max": 9}),
"send_gzipped": ("BOOLEAN", {"default": True}),
"show_progress_logs": ("BOOLEAN", {"default": True}),
}
}
@@ -36,7 +40,9 @@ class AQ_SendImageToAPI:
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):
def send_images_to_api(self, images, api_endpoint, api_key, batch_size, image_format, quality,
image_name_prefix="image", jobId="", userId="", compression_level=6,
send_gzipped=True, show_progress_logs=True):
instruction_string = """
**AQ_SendImageToAPI Node Instructions**
@@ -49,15 +55,19 @@ class AQ_SendImageToAPI:
- 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.
- quality (Int): Quality for JPEG/WEBP/AVIF formats (1-100).
- image_name_prefix (String): Optional prefix for image filenames (conceptual).
- 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).
- compression_level (Int): Gzip compression level for image data (0-9).
- send_gzipped (Boolean): Data is currently always gzipped; `compression_level` controls the degree.
- show_progress_logs (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.
- quality (Int): Corresponds to `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.
@@ -70,7 +80,7 @@ class AQ_SendImageToAPI:
- instruction (String): This instructional text.
- result (String): A string detailing the outcome of the last API call attempt.
"""
if not images:
if images is None or len(images) == 0:
return (instruction_string, "No images to process.",)
results = [] # This stores the JSON responses from the API for each batch
@@ -89,54 +99,53 @@ class AQ_SendImageToAPI:
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 tensor to PIL Image using the working approach
array = 255.0 * image_tensor.cpu().numpy().squeeze()
pil_image = Image.fromarray(np.clip(array, 0, 255).astype(np.uint8))
# 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)
pil_image.save(img_byte_arr, format="JPEG", quality=quality)
elif image_format == "WEBP":
save_params['quality'] = jpeg_quality # webp also uses quality
pil_image.save(img_byte_arr, format='WEBP', **save_params)
pil_image.save(img_byte_arr, format="WEBP", quality=quality)
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')
pil_image.save(img_byte_arr, format="AVIF", quality=quality)
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)
image_data = img_byte_arr.getvalue()
# Encode to base64
encoded_data = base64.b64encode(compressed_data).decode('utf-8')
batch_payload.append({"image_data": encoded_data, "format": image_format.lower()})
# Compress with gzip and encode if requested
if send_gzipped:
compressed_data = gzip.compress(image_data, compresslevel=compression_level)
encoded_data = base64.b64encode(compressed_data).decode('utf-8')
else:
encoded_data = base64.b64encode(image_data).decode('utf-8')
batch_payload.append({
"image_name": f"{image_name_prefix}_{processed_images_count}.{image_format.lower()}",
"image_data": encoded_data,
"is_gzipped": send_gzipped
})
if not batch_payload:
continue
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"Content-Encoding": "gzip" # Inform server that payload is gzipped
"Content-Type": "application/json"
}
result_string_for_batch = "No API call attempted for this batch." # Initialize for current batch
try:
json_payload = {"images": batch_payload}
json_payload = {
"images": batch_payload,
"jobId": jobId if jobId else None,
"userId": userId if userId else None
}
response = requests.post(api_endpoint, json=json_payload, headers=headers)
response.raise_for_status()
@@ -162,7 +171,7 @@ class AQ_SendImageToAPI:
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:
if show_progress_logs:
print(final_summary_message)
# Final status update to UI, if client_id is available
@@ -190,20 +199,4 @@ class AQ_SendImageToAPI:
# 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
logging.info(f"Progress (client {client_id}): {processed_count}/{total_images} images. Successful: {successful_uploads}, Failed: {failed_uploads}.")
pyproject.toml
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@@ -1,7 +1,7 @@
[project]
name = "AQnodes"
description = "Nodes for ComfyUI, mostly for llm calling and automatizing pulid multiface."
version = "1.0.0"
version = "1.0.1"
license = {file = "LICENSE"}
dependencies = ["google-generativeai>=0.3.2", "google-genai", "requests>=2.31.0", "dill", "segment-anything", "facexlib", "qwen-vl-utils ", "onnxruntime-gpu ", "insightface", "accelerate", "huggingface_hub"]