initial commit

.gitignore

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+temp*
+
+
+# python temp files
+__pycache__
+*.pyc
+.vscode

README.md

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+---
+license: mit
+---

app.py

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+import io
+import os
+
+import json
+import base64
+import random
+import numpy as np
+import pandas as pd
+import gradio as gr
+from pathlib import Path
+from PIL import Image
+
+from plots import get_pre_define_colors
+from utils.load_model import load_xclip
+from utils.predict import xclip_pred
+
+
+DEVICE = "cpu"
+XCLIP, OWLVIT_PRECESSOR = load_xclip(DEVICE)
+XCLIP_DESC_PATH = "data/jsons/bs_cub_desc.json"
+XCLIP_DESC = json.load(open(XCLIP_DESC_PATH, "r"))
+PREPROCESS = lambda x: OWLVIT_PRECESSOR(images=x, return_tensors='pt')
+IMAGES_FOLDER = "data/images"
+# XCLIP_RESULTS = json.load(open("data/jsons/xclip_org.json", "r"))
+# correct_predictions = [k for k, v in XCLIP_RESULTS.items() if v['prediction']]
+
+# get the intersection of sachit and xclip (revised)
+# INTERSECTION = []
+# IMAGE_RES = 400 * 400 # minimum resolution
+# TOTAL_SAMPLES = 20
+# for file_name in XCLIP_RESULTS:
+#     image = Image.open(os.path.join(IMAGES_FOLDER, 'org', file_name)).convert('RGB')
+#     w, h = image.size
+#     if w * h < IMAGE_RES:
+#         continue
+#     else:
+#         INTERSECTION.append(file_name)
+
+# IMAGE_FILE_LIST = random.sample(INTERSECTION, TOTAL_SAMPLES)
+IMAGE_FILE_LIST = json.load(open("data/jsons/file_list.json", "r"))
+# IMAGE_FILE_LIST = IMAGE_FILE_LIST[:19]
+# IMAGE_FILE_LIST.append('Eastern_Bluebird.jpg')
+IMAGE_GALLERY = [Image.open(os.path.join(IMAGES_FOLDER, 'org', file_name)).convert('RGB') for file_name in IMAGE_FILE_LIST]
+
+ORG_PART_ORDER = ['back', 'beak', 'belly', 'breast', 'crown', 'forehead', 'eyes', 'legs', 'wings', 'nape', 'tail', 'throat']
+ORDERED_PARTS = ['crown', 'forehead', 'nape', 'eyes', 'beak', 'throat', 'breast', 'belly', 'back', 'wings', 'legs', 'tail']
+COLORS = get_pre_define_colors(12, cmap_set=['Set2', 'tab10'])
+SACHIT_COLOR = "#ADD8E6"
+# CUB_BOXES = json.load(open("data/jsons/cub_boxes_owlvit_large.json", "r"))
+VISIBILITY_DICT = json.load(open("data/jsons/cub_vis_dict_binary.json", 'r'))
+VISIBILITY_DICT['Eastern_Bluebird.jpg'] = dict(zip(ORDERED_PARTS, [True]*12))
+
+# --- Image related functions ---
+def img_to_base64(img):
+    img_pil = Image.fromarray(img) if isinstance(img, np.ndarray) else img
+    buffered = io.BytesIO()
+    img_pil.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue())
+    return img_str.decode()
+
+def create_blank_image(width=500, height=500, color=(255, 255, 255)):
+    """Create a blank image of the given size and color."""
+    return np.array(Image.new("RGB", (width, height), color))
+
+# Convert RGB colors to hex
+def rgb_to_hex(rgb):
+    return f"#{''.join(f'{x:02x}' for x in rgb)}"
+
+def load_part_images(file_name: str) -> dict:
+    part_images = {}
+    # start_time = time.time()
+    for part_name in ORDERED_PARTS:
+        base_name = Path(file_name).stem
+        part_image_path = os.path.join(IMAGES_FOLDER, "boxes", f"{base_name}_{part_name}.jpg")
+        if not Path(part_image_path).exists():
+            continue
+        image = np.array(Image.open(part_image_path))
+        part_images[part_name] = img_to_base64(image)
+    # print(f"Time cost to load 12 images: {time.time() - start_time}")
+    # This takes less than 0.01 seconds. So the loading time is not the bottleneck.
+    return part_images
+
+def generate_xclip_explanations(result_dict:dict, visibility: dict, part_mask: dict = dict(zip(ORDERED_PARTS, [1]*12))):
+    """
+    The result_dict needs three keys: 'descriptions', 'pred_scores', 'file_name'
+    descriptions: {part_name1: desc_1, part_name2: desc_2, ...}
+    pred_scores: {part_name1: score_1, part_name2: score_2, ...}
+    file_name: str
+    """
+    
+    descriptions = result_dict['descriptions']
+    image_name = result_dict['file_name']
+    part_images = PART_IMAGES_DICT[image_name]
+    MAX_LENGTH = 50
+    exp_length = 400
+    fontsize = 15
+
+    # Start the SVG inside a div
+    svg_parts = [f'<div style="width: {exp_length}px; height: 450px; background-color: white;">',
+                 "<svg width=\"100%\" height=\"100%\">"]
+
+    # Add a row for each visible bird part
+    y_offset = 0
+    for part in ORDERED_PARTS:
+        if visibility[part] and part_mask[part]:
+            # Calculate the length of the bar (scaled to fit within the SVG)
+            part_score = max(result_dict['pred_scores'][part], 0)
+            bar_length = part_score * exp_length
+
+            # Modify the overlay image's opacity on mouseover and mouseout
+            mouseover_action1 = f"document.getElementById('overlayImage').src = 'data:image/jpeg;base64,{part_images[part]}'; document.getElementById('overlayImage').style.opacity = 1;"
+            mouseout_action1 = "document.getElementById('overlayImage').style.opacity = 0;"
+
+            combined_mouseover = f"javascript: {mouseover_action1};"
+            combined_mouseout = f"javascript: {mouseout_action1};"
+
+            # Add the description
+            num_lines = len(descriptions[part]) // MAX_LENGTH + 1
+            for line in range(num_lines):
+                desc_line = descriptions[part][line*MAX_LENGTH:(line+1)*MAX_LENGTH]
+                y_offset += fontsize
+                svg_parts.append(f"""
+                <text x="0" y="{y_offset}" font-size="{fontsize}" 
+                    onmouseover="{combined_mouseover}"
+                    onmouseout="{combined_mouseout}">
+                    {desc_line}
+                </text>
+                """)
+
+            # Add the bars
+            svg_parts.append(f"""
+            <rect x="0" y="{y_offset +3}" width="{bar_length}" height="{fontsize*0.7}" fill="{PART_COLORS[part]}"
+                onmouseover="{combined_mouseover}"
+                onmouseout="{combined_mouseout}">
+            </rect>
+            """)
+            # Add the scores
+            svg_parts.append(f'<text x="{exp_length - 50}" y="{y_offset+fontsize+3}" font-size="{fontsize}" fill="{PART_COLORS[part]}">{part_score:.2f}</text>')
+
+            y_offset += fontsize + 3
+    svg_parts.extend(("</svg>", "</div>"))
+    # Join everything into a single string
+    html = "".join(svg_parts)
+
+
+    return html
+
+
+
+def generate_sachit_explanations(result_dict:dict):
+    descriptions = result_dict['descriptions']
+    scores = result_dict['scores']
+    MAX_LENGTH = 50
+    exp_length = 400
+    fontsize = 15
+
+    descriptions = zip(scores, descriptions)
+    descriptions = sorted(descriptions, key=lambda x: x[0], reverse=True)
+
+    # Start the SVG inside a div
+    svg_parts = [f'<div style="width: {exp_length}px; height: 450px; background-color: white;">',
+                 "<svg width=\"100%\" height=\"100%\">"]
+
+    # Add a row for each visible bird part
+    y_offset = 0
+    for score, desc in descriptions:
+
+        # Calculate the length of the bar (scaled to fit within the SVG)
+        part_score = max(score, 0)
+        bar_length = part_score * exp_length
+
+        # Split the description into two lines if it's too long
+        num_lines = len(desc) // MAX_LENGTH + 1
+        for line in range(num_lines):
+            desc_line = desc[line*MAX_LENGTH:(line+1)*MAX_LENGTH]
+            y_offset += fontsize
+            svg_parts.append(f"""
+            <text x="0" y="{y_offset}" font-size="{fontsize}" fill="black">
+                {desc_line}
+            </text>
+            """)
+
+        # Add the bar
+        svg_parts.append(f"""
+        <rect x="0" y="{y_offset+3}" width="{bar_length}" height="{fontsize*0.7}" fill="{SACHIT_COLOR}">
+        </rect>
+        """)
+
+        # Add the score
+        svg_parts.append(f'<text x="{exp_length - 50}" y="{y_offset+fontsize+3}" font-size="fontsize" fill="{SACHIT_COLOR}">{part_score:.2f}</text>') # Added fill color
+
+        y_offset += fontsize + 3
+
+
+    svg_parts.extend(("</svg>", "</div>"))
+    # Join everything into a single string
+    html = "".join(svg_parts)
+
+
+    return html
+
+# --- Constants created by the functions above ---
+BLANK_OVERLAY = img_to_base64(create_blank_image())
+PART_COLORS = {part: rgb_to_hex(COLORS[i]) for i, part in enumerate(ORDERED_PARTS)}
+blank_image = np.array(Image.open('data/images/final.png').convert('RGB'))
+PART_IMAGES_DICT = {file_name: load_part_images(file_name) for file_name in IMAGE_FILE_LIST}
+
+# --- Gradio Functions ---
+def update_selected_image(event: gr.SelectData):
+    image_height = 400
+    index = event.index
+
+    image_name = IMAGE_FILE_LIST[index]
+    current_image.state = image_name
+    org_image = Image.open(os.path.join(IMAGES_FOLDER, 'org', image_name)).convert('RGB')
+    img_base64 = f"""
+    <div style="position: relative; height: {image_height}px; display: inline-block;">
+        <img id="birdImage" src="data:image/jpeg;base64,{img_to_base64(org_image)}" style="height: {image_height}px; width: auto;">
+        <img id="overlayImage" src="data:image/jpeg;base64,{BLANK_OVERLAY}" style="position:absolute; top:0; left:0; width:auto; height: {image_height}px; opacity: 0;">
+    </div>
+    """
+    gt_label = XCLIP_RESULTS[image_name]['ground_truth']
+    gt_class.state = gt_label
+
+    # --- for initial value only ---
+    out_dict = xclip_pred(new_desc=None, new_part_mask=None, new_class=None, org_desc=XCLIP_DESC_PATH, image=Image.open(os.path.join(IMAGES_FOLDER, 'org', current_image.state)).convert('RGB'), model=XCLIP, owlvit_processor=OWLVIT_PRECESSOR, device=DEVICE, image_name=current_image.state)
+    xclip_label = out_dict['pred_class']
+    clip_pred_scores = out_dict['pred_score']
+    xclip_part_scores = out_dict['pred_desc_scores']
+    result_dict = {'descriptions': dict(zip(ORG_PART_ORDER, out_dict["descriptions"])), 'pred_scores': xclip_part_scores, 'file_name': current_image.state}
+    xclip_exp = generate_xclip_explanations(result_dict, VISIBILITY_DICT[current_image.state], part_mask=dict(zip(ORDERED_PARTS, [1]*12)))
+    # --- end of intial value ---
+    
+    xclip_color = "green" if xclip_label.strip() == gt_label.strip() else "red"
+    xclip_pred_markdown = f"""
+        ### <span style='color:{xclip_color}'>XCLIP: {xclip_label} &nbsp;&nbsp;&nbsp; {clip_pred_scores:.4f}</span>
+    """
+
+    gt_label = f"""
+        ## {gt_label}
+    """
+    current_predicted_class.state = xclip_label
+    
+    # Populate the textbox with current descriptions
+    custom_class_name = "class name: custom"
+    descs = XCLIP_DESC[xclip_label]
+    descs = {k: descs[i] for i, k in enumerate(ORG_PART_ORDER)}
+    descs = {k: descs[k] for k in ORDERED_PARTS}
+    custom_text = [custom_class_name] + list(descs.values())
+    descriptions = ";\n".join(custom_text)
+    textbox = gr.Textbox.update(value=descriptions, lines=12, visible=True, label="XCLIP descriptions", interactive=True, info='Please use ";" to separate the descriptions for each part, and keep the format of {part name}: {descriptions}', show_label=False)
+    # modified_exp = gr.HTML().update(value="", visible=True)
+    return gt_label, img_base64, xclip_pred_markdown, xclip_exp, current_image, textbox
+
+def on_edit_button_click_xclip():
+    empty_exp = gr.HTML.update(visible=False)
+
+    # Populate the textbox with current descriptions
+    descs = XCLIP_DESC[current_predicted_class.state]
+    descs = {k: descs[i] for i, k in enumerate(ORG_PART_ORDER)}
+    descs = {k: descs[k] for k in ORDERED_PARTS}
+    custom_text = ["class name: custom"] + list(descs.values())
+    descriptions = ";\n".join(custom_text)
+    textbox = gr.Textbox.update(value=descriptions, lines=12, visible=True, label="XCLIP descriptions", interactive=True, info='Please use ";" to separate the descriptions for each part, and keep the format of {part name}: {descriptions}', show_label=False)
+    
+    return textbox, empty_exp
+
+def convert_input_text_to_xclip_format(textbox_input: str):
+
+    # Split the descriptions by newline to get individual descriptions for each part
+    descriptions_list = textbox_input.split(";\n")
+    # the first line should be "class name: xxx"
+    class_name_line = descriptions_list[0]
+    new_class_name = class_name_line.split(":")[1].strip()
+    
+    descriptions_list = descriptions_list[1:]
+    
+    # construct descripion dict with part name as key
+    descriptions_dict = {}
+    for desc in descriptions_list:
+        if desc.strip() == "":
+            continue
+        part_name, _ = desc.split(":")
+        descriptions_dict[part_name.strip()] = desc
+    # fill with empty string if the part is not in the descriptions
+    part_mask = {}
+    for part in ORDERED_PARTS:
+        if part not in descriptions_dict:
+            descriptions_dict[part] = ""
+            part_mask[part] = 0
+        else:
+            part_mask[part] = 1
+    return descriptions_dict, part_mask, new_class_name
+
+def on_predict_button_click_xclip(textbox_input: str):
+    descriptions_dict, part_mask, new_class_name = convert_input_text_to_xclip_format(textbox_input)
+    
+    # Get the new predictions and explanations
+    out_dict = xclip_pred(new_desc=descriptions_dict, new_part_mask=part_mask, new_class=new_class_name, org_desc=XCLIP_DESC_PATH, image=Image.open(os.path.join(IMAGES_FOLDER, 'org', current_image.state)).convert('RGB'), model=XCLIP, owlvit_processor=OWLVIT_PRECESSOR, device=DEVICE, image_name=current_image.state)
+    xclip_label = out_dict['pred_class']
+    xclip_pred_score = out_dict['pred_score']
+    xclip_part_scores = out_dict['pred_desc_scores']
+    custom_label = out_dict['modified_class']
+    custom_pred_score = out_dict['modified_score']
+    custom_part_scores = out_dict['modified_desc_scores']
+
+    # construct a result dict to generate xclip explanations
+    result_dict = {'descriptions': dict(zip(ORG_PART_ORDER, out_dict["descriptions"])), 'pred_scores': xclip_part_scores, 'file_name': current_image.state}
+    xclip_explanation = generate_xclip_explanations(result_dict, VISIBILITY_DICT[current_image.state], part_mask)
+    modified_result_dict = {'descriptions': dict(zip(ORG_PART_ORDER, out_dict["modified_descriptions"])), 'pred_scores': custom_part_scores, 'file_name': current_image.state}
+    modified_explanation = generate_xclip_explanations(modified_result_dict, VISIBILITY_DICT[current_image.state], part_mask)
+
+    xclip_color = "green" if xclip_label.strip() == gt_class.state.strip() else "red"
+    xclip_pred_markdown = f"""
+        ### <span style='color:{xclip_color}'>XCLIP: {xclip_label} &nbsp;&nbsp;&nbsp; {xclip_pred_score:.4f}</span>
+    """
+    custom_color = "green" if custom_label.strip() == gt_class.state.strip() else "red"
+    custom_pred_markdown = f"""
+        ### <span style='color:{custom_color}'>XCLIP: {custom_label} &nbsp;&nbsp;&nbsp; {custom_pred_score:.4f}</span>
+    """
+    textbox = gr.Textbox.update(visible=False)
+    # return textbox, xclip_pred_markdown, xclip_explanation, custom_pred_markdown, modified_explanation
+    
+    modified_exp = gr.HTML().update(value=modified_explanation, visible=True)
+    return textbox, xclip_pred_markdown, xclip_explanation, custom_pred_markdown, modified_exp
+
+
+custom_css = """
+        html, body {
+            margin: 0;
+            padding: 0;
+        }
+
+        #container {
+            position: relative;
+            width: 400px;
+            height: 400px;
+            border: 1px solid #000;
+            margin: 0 auto; /* This will center the container horizontally */
+        }
+
+        #canvas {
+            position: absolute;
+            top: 0;
+            left: 0;
+            width: 100%;
+            height: 100%;
+            object-fit: cover;
+        }
+
+"""
+
+# Define the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft(), css=custom_css, title="PEEB") as demo:
+    current_image = gr.State("")
+    current_predicted_class = gr.State("")
+    gt_class = gr.State("")
+    
+    with gr.Column():
+        title_text = gr.Markdown("# PEEB - demo")
+        gr.Markdown(
+            "- In this demo, you can edit the descriptions of a class and see how to model react to it."
+        )
+
+    # display the gallery of images
+    with gr.Column():
+        
+        gr.Markdown("## Select an image to start!")
+        image_gallery = gr.Gallery(value=IMAGE_GALLERY, label=None, preview=False, allow_preview=False, columns=10, height=250)
+        gr.Markdown("### Custom descritions: \n The first row should be **class name: {some name};**, where you can name your descriptions. \n For the remianing descriptions, please use **;** to separate the descriptions for each part, and use the format **{part name}: {descriptions}**. \n Note that you can delete a part completely, in such cases, all descriptions will remove the corresponding part.")
+        
+        with gr.Row():
+            with gr.Column():
+                image_label = gr.Markdown("### Class Name")
+                org_image = gr.HTML()
+            
+            with gr.Column():
+                with gr.Row():
+                    # xclip_predict_button = gr.Button(label="Predict", value="Predict")
+                    xclip_predict_button = gr.Button(value="Predict")
+                xclip_pred_label = gr.Markdown("### XCLIP:")
+                xclip_explanation = gr.HTML()
+
+            with gr.Column():
+                # xclip_edit_button = gr.Button(label="Edit", value="Reset Descriptions")
+                xclip_edit_button = gr.Button(value="Reset Descriptions")
+                custom_pred_label = gr.Markdown(
+                    "### Custom Descritpions:"
+                )
+                xclip_textbox = gr.Textbox(lines=12, placeholder="Edit the descriptions here", visible=False)
+                # ai_explanation = gr.Image(type="numpy", visible=True, show_label=False, height=500)
+                custom_explanation = gr.HTML()
+
+    gr.HTML("<br>")
+
+    image_gallery.select(update_selected_image, inputs=None, outputs=[image_label, org_image, xclip_pred_label, xclip_explanation, current_image, xclip_textbox])
+    xclip_edit_button.click(on_edit_button_click_xclip, inputs=[], outputs=[xclip_textbox, custom_explanation])
+    xclip_predict_button.click(on_predict_button_click_xclip, inputs=[xclip_textbox], outputs=[xclip_textbox, xclip_pred_label, xclip_explanation, custom_pred_label, custom_explanation])
+
+demo.launch(server_port=5000, share=True)

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