Update app.py

app.py

@@ -1,313 +1,4 @@
-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
+# Use a pipeline as a high-level helper
+from transformers import pipeline
 
-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 = "ydshieh/kosmos-2-patch14-224"
-
-    model = AutoModelForVision2Seq.from_pretrained(ckpt, trust_remote_code=True).to("cuda")
-    processor = AutoProcessor.from_pretrained(ckpt, trust_remote_code=True)
-
-    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 = "<grounding>An image of"
-        elif text_input == "Detailed":
-            text_input = "<grounding>Describe this image in detail:"
-        else:
-            text_input = f"<grounding>{text_input}"
-
-        inputs = processor(text=text_input, images=image_input, return_tensors="pt")
-
-        generated_ids = model.generate(
-            pixel_values=inputs["pixel_values"].to("cuda"),
-            input_ids=inputs["input_ids"][:, :-1].to("cuda"),
-            attention_mask=inputs["attention_mask"][:, :-1].to("cuda"),
-            img_features=None,
-            img_attn_mask=inputs["img_attn_mask"][:, :-1].to("cuda"),
-            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
+pipe = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")