import gradio as gr import random import numpy as np import os import requests import torch import torchvision.transforms as T from PIL import Image from transformers import AutoProcessor, AutoModelForVision2Seq import cv2 import ast colors = [ (0, 255, 0), (0, 0, 255), (255, 255, 0), (255, 0, 255), (0, 255, 255), (114, 128, 250), (0, 165, 255), (0, 128, 0), (144, 238, 144), (238, 238, 175), (255, 191, 0), (0, 128, 0), (226, 43, 138), (255, 0, 255), (0, 215, 255), (255, 0, 0), ] color_map = { f"{color_id}": f"#{hex(color[2])[2:].zfill(2)}{hex(color[1])[2:].zfill(2)}{hex(color[0])[2:].zfill(2)}" for color_id, color in enumerate(colors) } 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, entity_index=-1): """_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 indices = list(range(len(entities))) if entity_index >= 0: indices = [entity_index] # Not to show too many bboxes entities = entities[:len(color_map)] new_image = image.copy() previous_bboxes = [] # size of text text_size = 1 # 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 # num_bboxes = sum(len(x[-1]) for x in entities) used_colors = colors # random.sample(colors, k=num_bboxes) color_id = -1 for entity_idx, (entity_name, (start, end), bboxes) in enumerate(entities): color_id += 1 if entity_idx not in indices: continue for bbox_id, (x1_norm, y1_norm, x2_norm, y2_norm) in enumerate(bboxes): # if start is None and bbox_id > 0: # color_id += 1 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 = used_colors[color_id] # 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 pil_image def main(): ckpt = "microsoft/kosmos-2-patch14-224" model = AutoModelForVision2Seq.from_pretrained(ckpt).to("cuda") processor = AutoProcessor.from_pretrained(ckpt) def generate_predictions(image_input, text_input): # Save the image and load it again to match the original Kosmos-2 demo. # (https://github.com/microsoft/unilm/blob/f4695ed0244a275201fff00bee495f76670fbe70/kosmos-2/demo/gradio_app.py#L345-L346) user_image_path = "/tmp/user_input_test_image.jpg" image_input.save(user_image_path) # This might give different results from the original argument `image_input` image_input = Image.open(user_image_path) if text_input == "Brief": text_input = "An image of" elif text_input == "Detailed": text_input = "Describe this image in detail:" else: text_input = f"{text_input}" inputs = processor(text=text_input, images=image_input, return_tensors="pt").to("cuda") generated_ids = model.generate( pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], image_embeds=None, image_embeds_position_mask=inputs["image_embeds_position_mask"], use_cache=True, max_new_tokens=128, ) generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0] # By default, the generated text is cleanup and the entities are extracted. processed_text, entities = processor.post_process_generation(generated_text) annotated_image = draw_entity_boxes_on_image(image_input, entities, show=False) color_id = -1 entity_info = [] filtered_entities = [] for entity in entities: entity_name, (start, end), bboxes = entity if start == end: # skip bounding bbox without a `phrase` associated continue color_id += 1 # for bbox_id, _ in enumerate(bboxes): # if start is None and bbox_id > 0: # color_id += 1 entity_info.append(((start, end), color_id)) filtered_entities.append(entity) colored_text = [] prev_start = 0 end = 0 for idx, ((start, end), color_id) in enumerate(entity_info): if start > prev_start: colored_text.append((processed_text[prev_start:start], None)) colored_text.append((processed_text[start:end], f"{color_id}")) prev_start = end if end < len(processed_text): colored_text.append((processed_text[end:len(processed_text)], None)) return annotated_image, colored_text, str(filtered_entities) term_of_use = """ ### Terms of use By using this model, users are required to agree to the following terms: The model is intended for academic and research purposes. The utilization of the model to create unsuitable material is strictly forbidden and not endorsed by this work. The accountability for any improper or unacceptable application of the model rests exclusively with the individuals who generated such content. ### License This project is licensed under the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct). """ with gr.Blocks(title="Kosmos-2", theme=gr.themes.Base()).queue() as demo: gr.Markdown((""" # Kosmos-2: Grounding Multimodal Large Language Models to the World [[Paper]](https://arxiv.org/abs/2306.14824) [[Code]](https://github.com/microsoft/unilm/blob/master/kosmos-2) """)) with gr.Row(): with gr.Column(): image_input = gr.Image(type="pil", label="Test Image") text_input = gr.Radio(["Brief", "Detailed"], label="Description Type", value="Brief") run_button = gr.Button(label="Run", visible=True) with gr.Column(): image_output = gr.Image(type="pil") text_output1 = gr.HighlightedText( label="Generated Description", combine_adjacent=False, show_legend=True, ).style(color_map=color_map) with gr.Row(): with gr.Column(): gr.Examples(examples=[ ["images/two_dogs.jpg", "Detailed"], ["images/snowman.png", "Brief"], ["images/man_ball.png", "Detailed"], ], inputs=[image_input, text_input]) with gr.Column(): gr.Examples(examples=[ ["images/six_planes.png", "Brief"], ["images/quadrocopter.jpg", "Brief"], ["images/carnaby_street.jpg", "Brief"], ], inputs=[image_input, text_input]) gr.Markdown(term_of_use) # record which text span (label) is selected selected = gr.Number(-1, show_label=False, placeholder="Selected", visible=False) # record the current `entities` entity_output = gr.Textbox(visible=False) # get the current selected span label def get_text_span_label(evt: gr.SelectData): if evt.value[-1] is None: return -1 return int(evt.value[-1]) # and set this information to `selected` text_output1.select(get_text_span_label, None, selected) # update output image when we change the span (enity) selection def update_output_image(img_input, image_output, entities, idx): entities = ast.literal_eval(entities) updated_image = draw_entity_boxes_on_image(img_input, entities, entity_index=idx) return updated_image selected.change(update_output_image, [image_input, image_output, entity_output, selected], [image_output]) run_button.click(fn=generate_predictions, inputs=[image_input, text_input], outputs=[image_output, text_output1, entity_output], show_progress=True, queue=True) demo.launch(share=False) if __name__ == "__main__": main() # trigger