Create app.py

app.py

@@ -0,0 +1,309 @@
+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 spaces
+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)
+
+@spaces.GPU
+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
+
+
+
+
+ckpt = "microsoft/kosmos-2-patch14-224"
+
+model = AutoModelForVision2Seq.from_pretrained(ckpt).to("cuda")
+processor = AutoProcessor.from_pretrained(ckpt)
+
+@spaces.GPU
+def generate_predictions(image_input, text_input, question=None):
+
+    # 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 = "<grounding>An image of"
+    elif text_input == "Detailed":
+        text_input = "<grounding>Describe this image in detail:"
+    if question:
+        text_input = f"<grounding>{question}"
+    print(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)
+        ### This model can answer visual questions, does localize objects in a given image, and even caption the image without hallucination!
+        ### To get started, simply pick one of the images. Pick "Brief" or "Detailed" input for captioning. For visual question answering, pick "None" and enter your question.
+        """))
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil", label="Test Image")
+            text_input = gr.Radio(["Brief", "Detailed", "None"], label="Captioning Detail", value="Brief")
+            question = gr.Textbox(label="Visual Question Answering")
+            run_button = gr.Button(value="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,
+                            )
+
+    with gr.Row():
+        with gr.Column():
+            gr.Examples(examples=[
+                        ["/content/IMG_4509.jpg", "Detailed", None],
+                        ["/content/IMG_4509.jpg", "Brief", None],
+                        ["/content/IMG_4509.jpg", "None", "What is in this image?"],
+                    ], inputs=[image_input, text_input, question])
+        
+    gr.Markdown(term_of_use)
+
+    # record which text span (label) is selected
+    selected = gr.Number(-1, show_label=False, 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, question],
+                     outputs=[image_output, text_output1, entity_output],
+                     show_progress=True, queue=True)
+
+demo.launch(debug=True)
+