from fastapi import FastAPI, File, UploadFile, Form from fastapi.responses import StreamingResponse from pydantic import BaseModel from typing import Optional import logging import os import boto3 import json import shlex import subprocess import tempfile import time import base64 import gradio as gr import numpy as np import rembg import spaces import torch from PIL import Image from functools import partial import io from io import BytesIO from botocore.exceptions import NoCredentialsError, PartialCredentialsError import datetime app = FastAPI() subprocess.run(shlex.split('pip install wheel/torchmcubes-0.1.0-cp310-cp310-linux_x86_64.whl')) from tsr.system import TSR from tsr.utils import remove_background, resize_foreground, to_gradio_3d_orientation HEADER = """FRAME AI""" if torch.cuda.is_available(): device = "cuda:0" else: device = "cpu" torch.cuda.synchronize() model = TSR.from_pretrained( "stabilityai/TripoSR", config_name="config.yaml", weight_name="model.ckpt", ) model.renderer.set_chunk_size(131072) model.to(device) rembg_session = rembg.new_session() ACCESS = os.getenv("ACCESS") SECRET = os.getenv("SECRET") bedrock = boto3.client(service_name='bedrock', aws_access_key_id = ACCESS, aws_secret_access_key = SECRET, region_name='us-east-1') bedrock_runtime = boto3.client(service_name='bedrock-runtime', aws_access_key_id = ACCESS, aws_secret_access_key = SECRET, region_name='us-east-1') def gen_pos_prompt(text): instruction = f'''Your task is to create a positive prompt for image generation. Objective: Generate images that prioritize structural integrity and accurate shapes. The focus should be on the correct form and basic contours of objects, with minimal concern for colors. Guidelines: Complex Objects (e.g., animals, vehicles): For these, the image should resemble a toy object, emphasizing the correct shape and structure while minimizing details and color complexity. Example Input: A sports bike Example Positive Prompt: Simple sports bike with accurate shape and structure, minimal details, digital painting, concept art style, basic contours, soft lighting, clean lines, neutral or muted colors, toy-like appearance, low contrast. Example Input: A lion Example Positive Prompt: Toy-like depiction of a lion with a focus on structural accuracy, minimal details, digital painting, concept art style, basic contours, soft lighting, clean lines, neutral or muted colors, simplified features, low contrast. Simple Objects (e.g., a tennis ball): For these, the prompt should specify a realistic depiction, focusing on the accurate shape and structure. Example Input: A tennis ball Example Positive Prompt: Realistic depiction of a tennis ball with accurate shape and texture, digital painting, clean lines, minimal additional details, soft lighting, neutral or muted colors, focus on structural integrity. Prompt Structure: Subject: Clearly describe the object and its essential shape and structure. Medium: Specify the art style (e.g., digital painting, concept art). Style: Include relevant style terms (e.g., simplified, toy-like for complex objects; realistic for simple objects). Resolution: Mention resolution if necessary (e.g., basic resolution). Lighting: Indicate the type of lighting (e.g., soft lighting). Color: Use neutral or muted colors with minimal emphasis on color details. Additional Details: Keep additional details minimal or specify if not desired. Input: {text} Positive Prompt: ''' body = json.dumps({'inputText': instruction, 'textGenerationConfig': {'temperature': 0.1, 'topP': 0.01, 'maxTokenCount':512}}) response = bedrock_runtime.invoke_model(body=body, modelId='amazon.titan-text-express-v1') pos_prompt = json.loads(response.get('body').read())['results'][0]['outputText'] return pos_prompt def encode_image_to_base64(image): with io.BytesIO() as buffered: image.save(buffered, format="PNG") return base64.b64encode(buffered.getvalue()).decode('utf-8') def generate_image_from_text(encoded_image, seed, pos_prompt=None): neg_prompt = '''Detailed, complex textures, intricate patterns, realistic lighting, high contrast, reflections, fuzzy surface, realistic proportions, photographic quality, vibrant colors, detailed background, shadows, disfigured, deformed, ugly, multiple, duplicate.''' encoded_str = encode_image_to_base64(encoded_image) if pos_prompt: parameters = { 'taskType': 'IMAGE_VARIATION', 'imageVariationParams': { 'images': [encoded_str], 'text': gen_pos_prompt(pos_prompt), 'negativeText': neg_prompt, 'similarityStrength': 0.7 }, 'imageGenerationConfig': { "cfgScale": 8, "seed": int(seed), "width": 512, "height": 512, "numberOfImages": 1 } } else: parameters = { 'taskType': 'IMAGE_VARIATION', 'imageVariationParams': { 'images': [encoded_str], 'negativeText': neg_prompt, 'similarityStrength': 0.7 }, 'imageGenerationConfig': { "cfgScale": 8, "seed": int(seed), "width": 512, "height": 512, "numberOfImages": 1 } } request_body = json.dumps(parameters) response = bedrock_runtime.invoke_model(body=request_body, modelId='amazon.titan-image-generator-v1') response_body = json.loads(response.get('body').read()) base64_image_data = base64.b64decode(response_body['images'][0]) return Image.open(io.BytesIO(base64_image_data)) def check_input_image(input_image): if input_image is None: raise gr.Error("No image uploaded!") def preprocess(input_image, do_remove_background, foreground_ratio): torch.cuda.synchronize() def fill_background(image): image = np.array(image).astype(np.float32) / 255.0 image = image[:, :, :3] * image[:, :, 3:4] + (1 - image[:, :, 3:4]) * 0.5 image = Image.fromarray((image * 255.0).astype(np.uint8)) return image if do_remove_background: image = input_image.convert("RGB") image = remove_background(image, rembg_session) image = resize_foreground(image, foreground_ratio) image = fill_background(image) print("do_remove_back") torch.cuda.empty_cache() else: image = input_image if image.mode == "RGBA": image = fill_background(image) torch.cuda.synchronize() return image # @spaces.GPU def generate(image, mc_resolution, formats=["obj", "glb"]): # torch.cuda.empty_cache() scene_codes = model(image, device=device) mesh = model.extract_mesh(scene_codes, resolution=mc_resolution)[0] mesh = to_gradio_3d_orientation(mesh) mesh_path_glb = tempfile.NamedTemporaryFile(suffix=f".glb", delete=False) mesh.export(mesh_path_glb.name) mesh_path_obj = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False) mesh.apply_scale([-1, 1, 1]) # Otherwise the visualized .obj will be flipped mesh.export(mesh_path_obj.name) return mesh_path_obj.name, mesh_path_glb.name def upload_file_to_s3(file_path, bucket_name, object_name=None): s3_client = boto3.client('s3',aws_access_key_id = ACCESS, aws_secret_access_key = SECRET, region_name='us-east-1') if object_name is None: object_name = file_path try: s3_client.upload_file(file_path, bucket_name, object_name) except FileNotFoundError: print(f"The file {file_path} was not found.") return False except NoCredentialsError: print("Credentials not available.") return False except PartialCredentialsError: print("Incomplete credentials provided.") return False except Exception as e: print(f"An error occurred: {e}") return False print(f"File {file_path} uploaded successfully to {bucket_name}/{object_name}.") return True @app.post("/process_image/") async def process_image( file: UploadFile = File(...), seed: int = Form(...), enhance_image: bool = Form(...), do_remove_background: bool = Form(...), foreground_ratio: float = Form(...), mc_resolution: int = Form(...), auth: str = Form(...), text_prompt: Optional[str] = Form(None) ): if auth == os.getenv("AUTHORIZE"): image_bytes = await file.read() input_image = Image.open(BytesIO(image_bytes)) if enhance_image: image_pil = generate_image_from_text(encoded_image=input_image, seed=seed, pos_prompt=text_prompt) else: image_pil = input_image preprocessed = preprocess(image_pil, do_remove_background, foreground_ratio) mesh_name_obj, mesh_name_glb = generate(preprocessed, mc_resolution) timestamp = datetime.datetime.now().strftime('%Y%m%d%H%M%S%f') object_name = f'object_{timestamp}_1.obj' object_name_2 = f'object_{timestamp}_2.glb' if upload_file_to_s3(mesh_name_obj, 'framebucket3d',object_name) and upload_file_to_s3(mesh_name_glb, 'framebucket3d',object_name_2): return { "obj_path": f"https://framebucket3d.s3.amazonaws.com/{object_name}", "glb_path": f"https://framebucket3d.s3.amazonaws.com/{object_name_2}" } else: return {"Internal Server Error": False} else: return {"Authentication":"Failed"} if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=7860)