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| 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 | |
| if isinstance(image_path, np.ndarray): | |
| image_path = Image.fromarray(image_path) | |
| 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 | |