import math import PIL from PIL import Image import cv2 import numpy as np from diffusers.utils import load_image def draw_kps(image_pil, kps, color_list=[(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255)]): """ Draw keypoints on an image. Args: image_pil (PIL.Image): Image on which to draw the keypoints. kps (list): List of keypoints to draw. color_list (list): List of colors to use for drawing the keypoints. Returns: PIL.Image: Image with keypoints drawn on it. """ stickwidth = 4 limbSeq = np.array([[0, 2], [1, 2], [3, 2], [4, 2]]) kps = np.array(kps) # w, h = image_pil.size # out_img = np.zeros([h, w, 3]) if type(image_pil) == PIL.Image.Image: out_img = np.array(image_pil) else: out_img = image_pil for i in range(len(limbSeq)): index = limbSeq[i] color = color_list[index[0]] x = kps[index][:, 0] y = kps[index][:, 1] length = ((x[0] - x[1]) ** 2 + (y[0] - y[1]) ** 2) ** 0.5 angle = math.degrees(math.atan2(y[0] - y[1], x[0] - x[1])) polygon = cv2.ellipse2Poly( (int(np.mean(x)), int(np.mean(y))), (int(length / 2), stickwidth), int(angle), 0, 360, 1 ) out_img = cv2.fillConvexPoly(out_img.copy(), polygon, color) out_img = (out_img * 0.6).astype(np.uint8) for idx_kp, kp in enumerate(kps): color = color_list[idx_kp] x, y = kp out_img = cv2.circle(out_img.copy(), (int(x), int(y)), 10, color, -1) out_img_pil = PIL.Image.fromarray(out_img.astype(np.uint8)) return out_img_pil def load_and_resize_image(image_path, max_width, max_height, maintain_aspect_ratio=True): """ Load and resize an image to the specified dimensions. Args: image_path (str): Path to the image file. max_width (int): Maximum width of the resized image. max_height (int): Maximum height of the resized image. maintain_aspect_ratio (bool): Whether to maintain the aspect ratio of the image. Returns: PIL.Image: Resized image. """ # Open the image image = load_image(image_path) # Get the current width and height of the image current_width, current_height = image.size if maintain_aspect_ratio: # Calculate the aspect ratio of the image aspect_ratio = current_width / current_height # Calculate the new dimensions based on the max width and height if current_width / max_width > current_height / max_height: new_width = max_width new_height = int(new_width / aspect_ratio) else: new_height = max_height new_width = int(new_height * aspect_ratio) else: # Use the max width and height as the new dimensions new_width = max_width new_height = max_height # Ensure the new dimensions are divisible by 8 new_width = (new_width // 8) * 8 new_height = (new_height // 8) * 8 # Resize the image resized_image = image.resize((new_width, new_height)) return resized_image def align_images(image1, image2): """ Resize two images to the same dimensions by cropping the larger image(s) to match the smaller one. Args: image1 (PIL.Image): First image to be aligned. image2 (PIL.Image): Second image to be aligned. Returns: tuple: A tuple containing two images with the same dimensions. """ # Determine the new size by taking the smaller width and height from both images new_width = min(image1.size[0], image2.size[0]) new_height = min(image1.size[1], image2.size[1]) # Crop both images if necessary if image1.size != (new_width, new_height): image1 = image1.crop((0, 0, new_width, new_height)) if image2.size != (new_width, new_height): image2 = image2.crop((0, 0, new_width, new_height)) return image1, image2 def align_images_2(image1, image2): """ Resize and crop the second image to match the dimensions of the first image by scaling to aspect fill and then center cropping the extra parts. Args: image1 (PIL.Image): First image which will act as the reference for alignment. image2 (PIL.Image): Second image to be aligned to the first image's dimensions. Returns: tuple: A tuple containing the first image and the aligned second image. """ # Get dimensions of the first image target_width, target_height = image1.size # Calculate the aspect ratio of the second image aspect_ratio = image2.width / image2.height # Calculate dimensions to aspect fill if target_width / target_height > aspect_ratio: # The first image is wider relative to its height than the second image fill_height = target_height fill_width = int(fill_height * aspect_ratio) else: # The first image is taller relative to its width than the second image fill_width = target_width fill_height = int(fill_width / aspect_ratio) # Resize the second image to fill dimensions filled_image = image2.resize((fill_width, fill_height), Image.Resampling.LANCZOS) # Calculate top-left corner of crop box to center crop left = (fill_width - target_width) / 2 top = (fill_height - target_height) / 2 right = left + target_width bottom = top + target_height # Crop the filled image to match the size of the first image cropped_image = filled_image.crop((int(left), int(top), int(right), int(bottom))) return image1, cropped_image