--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co./docs/hub/model-cards {} --- # Kosmos-2: Grounding Multimodal Large Language Models to the World

This Hub repository contains a HuggingFace's `transformers` implementation of [the original Kosmos-2 model](https://github.com/microsoft/unilm/tree/master/kosmos-2) from Microsoft. ## How to Get Started with the Model Use the code below to get started with the model. ```python import requests from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq model = AutoModelForVision2Seq.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) processor = AutoProcessor.from_pretrained("ydshieh/kosmos-2-patch14-224", trust_remote_code=True) prompt = "An image of" url = "https://huggingface.co./ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg" image = Image.open(requests.get(url, stream=True).raw) inputs = processor(text=prompt, images=image, return_tensors="pt") generated_ids = model.generate( pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"][:, :-1], attention_mask=inputs["attention_mask"][:, :-1], img_features=None, img_attn_mask=inputs["img_attn_mask"][:, :-1], use_cache=True, max_new_tokens=64, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # Specify `cleanup_and_extract=False` in order to see the raw model generation. processed_text = processor.post_processor_generation(generated_text, cleanup_and_extract=False) print(processed_text) # ` An image of a snowman

warming himself by a fire

.` # By default, the generated text is cleanup and the entities are extracted. processed_text, entities = processor.post_processor_generation(generated_text) print(processed_text) # `An image of a snowman warming himself by a fire.` print(entities) # `[('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])]` ``` ## Draw the bounding bboxes of the entities on the image Once you have the `entities`, you can use the following helper function to draw their bounding bboxes on the image: ```python import cv2 import numpy as np import os import requests import torch import torchvision.transforms as T from PIL import Image def is_overlapping(rect1, rect2): x1, y1, x2, y2 = rect1 x3, y3, x4, y4 = rect2 return not (x2 < x3 or x1 > x4 or y2 < y3 or y1 > y4) def draw_entity_boxes_on_image(image, entities, show=False, save_path=None): """_summary_ Args: image (_type_): image or image path collect_entity_location (_type_): _description_ """ if isinstance(image, Image.Image): image_h = image.height image_w = image.width image = np.array(image)[:, :, [2, 1, 0]] elif isinstance(image, str): if os.path.exists(image): pil_img = Image.open(image).convert("RGB") image = np.array(pil_img)[:, :, [2, 1, 0]] image_h = pil_img.height image_w = pil_img.width else: raise ValueError(f"invaild image path, {image}") elif isinstance(image, torch.Tensor): # pdb.set_trace() image_tensor = image.cpu() reverse_norm_mean = torch.tensor([0.48145466, 0.4578275, 0.40821073])[:, None, None] reverse_norm_std = torch.tensor([0.26862954, 0.26130258, 0.27577711])[:, None, None] image_tensor = image_tensor * reverse_norm_std + reverse_norm_mean pil_img = T.ToPILImage()(image_tensor) image_h = pil_img.height image_w = pil_img.width image = np.array(pil_img)[:, :, [2, 1, 0]] else: raise ValueError(f"invaild image format, {type(image)} for {image}") if len(entities) == 0: return image new_image = image.copy() previous_bboxes = [] # size of text text_size = 2 # thickness of text text_line = 1 # int(max(1 * min(image_h, image_w) / 512, 1)) box_line = 3 (c_width, text_height), _ = cv2.getTextSize("F", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) base_height = int(text_height * 0.675) text_offset_original = text_height - base_height text_spaces = 3 for entity_name, (start, end), bboxes in entities: for (x1_norm, y1_norm, x2_norm, y2_norm) in bboxes: orig_x1, orig_y1, orig_x2, orig_y2 = int(x1_norm * image_w), int(y1_norm * image_h), int(x2_norm * image_w), int(y2_norm * image_h) # draw bbox # random color color = tuple(np.random.randint(0, 255, size=3).tolist()) new_image = cv2.rectangle(new_image, (orig_x1, orig_y1), (orig_x2, orig_y2), color, box_line) l_o, r_o = box_line // 2 + box_line % 2, box_line // 2 + box_line % 2 + 1 x1 = orig_x1 - l_o y1 = orig_y1 - l_o if y1 < text_height + text_offset_original + 2 * text_spaces: y1 = orig_y1 + r_o + text_height + text_offset_original + 2 * text_spaces x1 = orig_x1 + r_o # add text background (text_width, text_height), _ = cv2.getTextSize(f" {entity_name}", cv2.FONT_HERSHEY_COMPLEX, text_size, text_line) text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2 = x1, y1 - (text_height + text_offset_original + 2 * text_spaces), x1 + text_width, y1 for prev_bbox in previous_bboxes: while is_overlapping((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2), prev_bbox): text_bg_y1 += (text_height + text_offset_original + 2 * text_spaces) text_bg_y2 += (text_height + text_offset_original + 2 * text_spaces) y1 += (text_height + text_offset_original + 2 * text_spaces) if text_bg_y2 >= image_h: text_bg_y1 = max(0, image_h - (text_height + text_offset_original + 2 * text_spaces)) text_bg_y2 = image_h y1 = image_h break alpha = 0.5 for i in range(text_bg_y1, text_bg_y2): for j in range(text_bg_x1, text_bg_x2): if i < image_h and j < image_w: if j < text_bg_x1 + 1.35 * c_width: # original color bg_color = color else: # white bg_color = [255, 255, 255] new_image[i, j] = (alpha * new_image[i, j] + (1 - alpha) * np.array(bg_color)).astype(np.uint8) cv2.putText( new_image, f" {entity_name}", (x1, y1 - text_offset_original - 1 * text_spaces), cv2.FONT_HERSHEY_COMPLEX, text_size, (0, 0, 0), text_line, cv2.LINE_AA ) # previous_locations.append((x1, y1)) previous_bboxes.append((text_bg_x1, text_bg_y1, text_bg_x2, text_bg_y2)) pil_image = Image.fromarray(new_image[:, :, [2, 1, 0]]) if save_path: pil_image.save(save_path) if show: pil_image.show() return new_image # (The same image from the previous code example) url = "https://huggingface.co./ydshieh/kosmos-2-patch14-224/resolve/main/snowman.jpg" image = Image.open(requests.get(url, stream=True).raw) # From the previous code example entities = [('a snowman', (12, 21), [(0.390625, 0.046875, 0.984375, 0.828125)]), ('a fire', (41, 47), [(0.171875, 0.015625, 0.484375, 0.890625)])] # Draw the bounding bboxes draw_entity_boxes_on_image(image, entities, show=True) ``` Here is the annotated image: