import os import requests import logging from PIL import Image, ImageEnhance import cv2 import numpy as np from preprocess.humanparsing.run_parsing import Parsing from src.image_format_convertor import ImageFormatConvertor REMOVE_BG_KEY = os.getenv('REMOVE_BG_KEY') parsing_model = Parsing(0) class BackgroundProcessor: DeprecationWarning("Created only for testing. Not in use") @classmethod def add_background(cls, human_img: Image, background_img: Image): human_img = human_img.convert("RGB") width = human_img.width height = human_img.height # Create mask image parsed_img, _ = parsing_model(human_img) mask_img = parsed_img.convert("L") mask_img = mask_img.resize((width, height)) background_img = background_img.convert("RGB") background_img = background_img.resize((width, height)) # Convert to numpy arrays human_np = np.array(human_img) mask_np = np.array(mask_img) background_np = np.array(background_img) # Ensure mask is 3-channel (RGB) for compatibility mask_np = np.stack((mask_np,) * 3, axis=-1) # Apply the mask to human_img human_with_background = np.where(mask_np > 0, human_np, background_np) # Convert back to PIL Image result_img = Image.fromarray(human_with_background.astype('uint8')) # Return or save the result return result_img DeprecationWarning("Created only for testing. Not in use") @classmethod def add_background_v3(cls, foreground_pil: Image, background_pil: Image): foreground_pil= foreground_pil.convert("RGB") width = foreground_pil.width height = foreground_pil.height # Create mask image parsed_img, _ = parsing_model(foreground_pil) mask_pil = parsed_img.convert("L") # Apply a threshold to convert to binary image # mask_pil = mask_pil.point(lambda p: 1 if p > 127 else 0, mode='1') mask_pil = mask_pil.resize((width, height)) # Resize background image background_pil = background_pil.convert("RGB") background_pil = background_pil.resize((width, height)) # Load the images using PIL #foreground_pil = Image.open(human_img_path).convert("RGB") # The segmented person image #background_pil = Image.open(background_img_path).convert("RGB") # The new background image #mask_pil = Image.open(mask_img_path).convert('L') # The mask image from the human parser model # Resize the background to match the size of the foreground #background_pil = background_pil.resize(foreground_pil.size) # Resize mask #mask_pil = mask_pil.resize(foreground_pil.size) # Convert PIL images to OpenCV format foreground_cv2 = ImageFormatConvertor.pil_to_cv2(foreground_pil) background_cv2 = ImageFormatConvertor.pil_to_cv2(background_pil) #mask_cv2 = pil_to_cv2(mask_pil) mask_cv2 = np.array(mask_pil) # Directly convert to NumPy array without color conversion # Ensure the mask is a single channel image if len(mask_cv2.shape) == 3: mask_cv2 = cv2.cvtColor(mask_cv2, cv2.COLOR_BGR2GRAY) # Threshold the mask to convert it to pure black and white _, mask_cv2 = cv2.threshold(mask_cv2, 0, 255, cv2.THRESH_BINARY) # Ensure the mask is a single channel image #if len(mask_cv2.shape) == 3: # mask_cv2 = cv2.cvtColor(mask_cv2, cv2.COLOR_BGR2GRAY) # Create an inverted mask mask_inv_cv2 = cv2.bitwise_not(mask_cv2) # Convert mask to 3 channels mask_3ch_cv2 = cv2.cvtColor(mask_cv2, cv2.COLOR_GRAY2BGR) mask_inv_3ch_cv2 = cv2.cvtColor(mask_inv_cv2, cv2.COLOR_GRAY2BGR) # Extract the person from the foreground image using the mask person_cv2 = cv2.bitwise_and(foreground_cv2, mask_3ch_cv2) # Extract the background where the person is not present background_extracted_cv2 = cv2.bitwise_and(background_cv2, mask_inv_3ch_cv2) # Combine the person and the new background combined_cv2 = cv2.add(person_cv2, background_extracted_cv2) # Refine edges using Gaussian Blur (feathering technique) blurred_combined_cv2 = cv2.GaussianBlur(combined_cv2, (5, 5), 0) # Convert the result back to PIL format combined_pil = ImageFormatConvertor.cv2_to_pil(blurred_combined_cv2) """ # Post-processing: Adjust brightness, contrast, etc. (optional) enhancer = ImageEnhance.Contrast(combined_pil) post_processed_pil = enhancer.enhance(1.2) # Adjust contrast enhancer = ImageEnhance.Brightness(post_processed_pil) post_processed_pil = enhancer.enhance(1.2) # Adjust brightness """ # Save the final image # post_processed_pil.save('path_to_save_final_image_1.png') # Display the images (optional) #foreground_pil.show(title="Foreground") #background_pil.show(title="Background") #mask_pil.show(title="Mask") #combined_pil.show(title="Combined") # post_processed_pil.show(title="Post Processed") return combined_pil DeprecationWarning("Created only for testing. Not in use") @classmethod def replace_background(cls, foreground_img_path: str, background_img_path: str): # Load the input image (with alpha channel) and the background image #input_image = cv2.imread(foreground_img_path, cv2.IMREAD_UNCHANGED) # background_image = cv2.imread(background_img_path) foreground_img_pil = Image.open(foreground_img_path) width = foreground_img_pil.width height = foreground_img_pil.height background_image_pil = Image.open(background_img_path) background_image_pil = background_image_pil.resize((width, height)) input_image = ImageFormatConvertor.pil_to_cv2(foreground_img_pil) background_image = ImageFormatConvertor.pil_to_cv2(background_image_pil) # Ensure the input image has an alpha channel if input_image.shape[2] != 4: raise ValueError("Input image must have an alpha channel") # Extract the alpha channel alpha_channel = input_image[:, :, 3] # Resize the background image to match the input image dimensions background_image = cv2.resize(background_image, (input_image.shape[1], input_image.shape[0])) # Convert alpha channel to 3 channels alpha_channel_3ch = cv2.cvtColor(alpha_channel, cv2.COLOR_GRAY2BGR) alpha_channel_3ch = alpha_channel_3ch / 255.0 # Normalize to 0-1 # Extract the BGR channels of the input image input_bgr = input_image[:, :, :3] background_bgr = background_image[:,:,:3] # Blend the images using the alpha channel foreground = cv2.multiply(alpha_channel_3ch, input_bgr.astype(float)) background = cv2.multiply(1.0 - alpha_channel_3ch, background_bgr.astype(float)) combined_image = cv2.add(foreground, background).astype(np.uint8) # Save and display the result cv2.imwrite('path_to_save_combined_image.png', combined_image) cv2.imshow('Combined Image', combined_image) cv2.waitKey(0) cv2.destroyAllWindows() @classmethod def replace_background_with_removebg(cls, foreground_img_pil: Image, background_image_pil: Image): foreground_img_pil= foreground_img_pil.convert("RGB") width = foreground_img_pil.width height = foreground_img_pil.height # Resize background image background_image_pil = background_image_pil.convert("RGB") background_image_pil = background_image_pil.resize((width, height)) #foreground_img_pil = Image.open(foreground_img_path) #width = foreground_img_pil.width #height = foreground_img_pil.height #background_image_pil = Image.open(background_img_path) #background_image_pil = background_image_pil.resize((width, height)) foreground_binary = ImageFormatConvertor.pil_image_to_binary_data(foreground_img_pil) background_binary = ImageFormatConvertor.pil_image_to_binary_data(background_image_pil) combined_img_pil = cls.remove_bg(foreground_binary, background_binary) return combined_img_pil @classmethod def remove_bg(cls, foreground_binary: str, background_binary: str): # ref: https://www.remove.bg/api#api-reference url = "https://api.remove.bg/v1.0/removebg" # using form-data as passing binary data is not supported in application/json files = { "image_file": ('foreground.png', foreground_binary, 'image/png'), "bg_image_file": ('background.png', background_binary, 'image/png') } # get output image in same resolution as input payload = { "size": "full", "shadow_type": "3D" } headers = { "accept": "image/*", 'X-Api-Key': REMOVE_BG_KEY } remove_bg_request = requests.post(url, files=files, data=payload, headers=headers, timeout=20) if remove_bg_request.status_code == 200: image_content = remove_bg_request.content pil_image = ImageFormatConvertor.binary_data_to_pil_image(image_content) return pil_image logging.error(f"failed to use remove bg. Status: {remove_bg_request.status_code}. Resp: {remove_bg_request.content}") return None