import os import base64 import io import uuid from ultralytics import YOLO import cv2 import torch import numpy as np from PIL import Image from torchvision import transforms import imageio.v2 as imageio from trainer import Trainer from utils.tools import get_config import torch.nn.functional as F from iopaint.single_processing import batch_inpaint_cv2 from pathlib import Path # set current working directory cache instead of default os.environ["TORCH_HOME"] = "./pretrained-model" os.environ["HUGGINGFACE_HUB_CACHE"] = "./pretrained-model" def resize_image(input_image_path, width=640, height=640): """Resizes an image from image data and returns the resized image.""" try: # Read the image using cv2.imread img = cv2.imread(input_image_path, cv2.IMREAD_COLOR) # Resize while maintaining the aspect ratio shape = img.shape[:2] # current shape [height, width] new_shape = (width, height) # the shape to resize to # Scale ratio (new / old) r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) ratio = r, r # width, height ratios new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) # Resize the image im = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) # Pad the image color = (114, 114, 114) # color used for padding dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding # divide padding into 2 sides dw /= 2 dh /= 2 # compute padding on all corners top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border return im except Exception as e: print(f"Error resizing image: {e}") return None # Or handle differently as needed def load_weights(path, device): model_weights = torch.load(path) return { k: v.to(device) for k, v in model_weights.items() } # Function to convert image to base64 def convert_image_to_base64(image): # Convert image to bytes _, buffer = cv2.imencode('.png', image) # Convert bytes to base64 image_base64 = base64.b64encode(buffer).decode('utf-8') return image_base64 def convert_to_base64(image): # Read the image file as binary data image_data = image.read() # Encode the binary data as base64 base64_encoded = base64.b64encode(image_data).decode('utf-8') return base64_encoded def convert_to_base64_file(image): # Convert the image to binary data image_data = cv2.imencode('.png', image)[1].tobytes() # Encode the binary data as base64 base64_encoded = base64.b64encode(image_data).decode('utf-8') return base64_encoded def process_images(input_image, append_image, default_class="chair"): # Static paths config_path = Path('configs/config.yaml') model_path = Path('pretrained-model/torch_model.p') # Resize input image and get base64 data of resized image img = resize_image(input_image) if img is None: return {'error': 'Failed to decode resized image'}, 419 H, W, _ = img.shape x_point = 0 y_point = 0 width = 1 height = 1 # Load a model model = YOLO('pretrained-model/yolov8m-seg.pt') # pretrained YOLOv8m-seg model # Run batched inference on a list of images results = model(img, imgsz=(W,H), conf=0.5) # chair class 56 with confidence >= 0.5 names = model.names class_found = False for result in results: for i, label in enumerate(result.boxes.cls): # Check if the label matches the chair label if names[int(label)] == default_class: class_found = True # Convert the tensor to a numpy array chair_mask_np = result.masks.data[i].numpy() kernel = np.ones((5, 5), np.uint8) # Create a 5x5 kernel for dilation chair_mask_np = cv2.dilate(chair_mask_np, kernel, iterations=2) # Apply dilation # Find contours to get bounding box contours, _ = cv2.findContours((chair_mask_np == 1).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) # Iterate over contours to find the bounding box of each object for contour in contours: x, y, w, h = cv2.boundingRect(contour) x_point = x y_point = y width = w height = h # Get the corresponding mask mask = result.masks.data[i].numpy() * 255 dilated_mask = cv2.dilate(mask, kernel, iterations=2) # Apply dilation # Resize the mask to match the dimensions of the original image resized_mask = cv2.resize(dilated_mask, (img.shape[1], img.shape[0])) # call repainting and merge function output_base64 = repaitingAndMerge(append_image,str(model_path), str(config_path),width, height, x_point, y_point, img, resized_mask) # Return the output base64 image in the API response return output_base64 # return class not found in prediction if not class_found: return {'message': f'{default_class} object not found in the image'}, 200 def repaitingAndMerge(append_image_path, model_path, config_path, width, height, xposition, yposition, input_base, mask_base): config = get_config(config_path) device = torch.device("cpu") trainer = Trainer(config) trainer.load_state_dict(load_weights(model_path, device), strict=False) trainer.eval() # lama inpainting start print("lama inpainting start") inpaint_result_np = batch_inpaint_cv2('lama', 'cpu', input_base, mask_base) print("lama inpainting end") # Create PIL Image from NumPy array final_image = Image.fromarray(inpaint_result_np) print("merge start") # Load the append image using cv2.imread append_image = cv2.imread(append_image_path, cv2.IMREAD_UNCHANGED) cv2.imwrite('appneded-image.png',append_image) # Resize the append image while preserving transparency resized_image = cv2.resize(append_image, (width, height), interpolation=cv2.INTER_AREA) # Convert the resized image to RGBA format (assuming it's in BGRA format) resized_image = cv2.cvtColor(resized_image, cv2.COLOR_BGRA2RGBA) # Create a PIL Image from the resized image with transparent background append_image_pil = Image.fromarray(resized_image) # Paste the append image onto the final image final_image.paste(append_image_pil, (xposition, yposition), append_image_pil) # Save the resulting image print("merge end") # Convert the final image to base64 with io.BytesIO() as output_buffer: final_image.save(output_buffer, format='PNG') output_numpy = np.array(final_image) return output_numpy