app.py

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
import torch
from efficientnet_pytorch import EfficientNet
from torchvision import transforms
from PIL import Image
import gradio as gr
from super_gradients.training import models


class Kosmos2:
    def __init__(self):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.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),    
        ]

        self.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(self.colors)
        }

        self.ckpt = "ydshieh/kosmos-2-patch14-224"
        self.model = AutoModelForVision2Seq.from_pretrained(self.ckpt, trust_remote_code=True).to(self.device)
        self.processor = AutoProcessor.from_pretrained(self.ckpt, trust_remote_code=True)

    def is_overlapping(self, 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(self, 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(self.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 = self.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 self.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 generate_predictions(self, 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 = self.processor(text=text_input, images=image_input, return_tensors="pt")

        generated_ids = self.model.generate(
            pixel_values=inputs["pixel_values"].to(self.device),
            input_ids=inputs["input_ids"][:, :-1].to(self.device),
            attention_mask=inputs["attention_mask"][:, :-1].to(self.device),
            img_features=None,
            img_attn_mask=inputs["img_attn_mask"][:, :-1].to(self.device),
            use_cache=True,
            max_new_tokens=128,
        )
        generated_text = self.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 = self.processor.post_process_generation(generated_text)

        annotated_image = self.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)

class VehiclePredictor:
    def __init__(self, model_path):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.yolo_nas_l = models.get("yolo_nas_l", pretrained_weights="coco")
        self.classifier_model = torch.load(model_path)
        self.classifier_model = self.classifier_model.to(self.device)
        self.classifier_model.eval()  # Set the model to evaluation mode

    def bounding_boxes_overlap(self, box1, box2):
        """Check if two bounding boxes overlap or touch."""
        x1, y1, x2, y2 = box1
        x3, y3, x4, y4 = box2
        return not (x3 > x2 or x4 < x1 or y3 > y2 or y4 < y1)
    
    def merge_boxes(self, box1, box2):
        """Return the encompassing bounding box of two boxes."""
        x1, y1, x2, y2 = box1
        x3, y3, x4, y4 = box2
        x = min(x1, x3)
        y = min(y1, y3)
        w = max(x2, x4)
        h = max(y2, y4)
        return (x, y, w, h)
    
    def save_merged_boxes(self, predictions, image_np):
        """Save merged bounding boxes as separate images."""
        processed_boxes = set()
        roi = None  # Initialize roi to None

        for image_prediction in predictions:
            bboxes = image_prediction.prediction.bboxes_xyxy  
            for box1 in bboxes:
                for box2 in bboxes:
                    if np.array_equal(box1, box2):
                        continue
                    if self.bounding_boxes_overlap(box1, box2) and tuple(box1) not in processed_boxes and tuple(box2) not in processed_boxes:
                        merged_box = self.merge_boxes(box1, box2)
                        roi = image_np[int(merged_box[1]):int(merged_box[3]), int(merged_box[0]):int(merged_box[2])]
                        processed_boxes.add(tuple(box1))
                        processed_boxes.add(tuple(box2))
                        break  # Exit the inner loop once a match is found
                if roi is not None:
                    break  # Exit the outer loop once a match is found
        return roi

    # Perform inference on an image
    def predict_image(self, image, model):
        # First, get the ROI using YOLO-NAS
        image_np = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
        predictions = self.yolo_nas_l.predict(image_np, iou=0.3, conf=0.35)
        roi_new = self.save_merged_boxes(predictions, image_np)
        
        if roi_new is None:
            roi_new = image_np  # Use the original image if no ROI is found

        # Convert ROI back to PIL Image for EfficientNet
        roi_image = Image.fromarray(cv2.cvtColor(roi_new, cv2.COLOR_BGR2RGB))

        # Define the image transformations
        transform = transforms.Compose([
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])

        # Convert PIL Image to Tensor
        roi_image_tensor = transform(roi_image).unsqueeze(0).to(self.device)

        with torch.no_grad():
            outputs = self.classifier_model(roi_image_tensor)
            _, predicted = outputs.max(1)
            prediction_text = 'Accident' if predicted.item() == 0 else 'No accident'
        
        return roi_image, prediction_text  # Return both the roi_image and the prediction text


def main():
    kosmos2 = Kosmos2()
    vehicle_predictor = VehiclePredictor('vehicle.pt')

    with gr.Blocks(title="Advanced Vehicle Contextualization & Collision Prediction", theme=gr.themes.Base()).queue() as demo:
        gr.Markdown(("""
            # Models used - 
            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)
            YOLO-NAS [[Code]](https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md)
            EfficientNet-b0
            """))
        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_kosmos = gr.Image(type="pil", label="Kosmos-2 Output Image")
                text_output_kosmos = gr.HighlightedText(
                                    label="Generated Description by Kosmos-2",
                                    combine_adjacent=False,
                                    show_legend=True,
                                ).style(color_map=kosmos2.color_map)
                
                image_output_vehicle = gr.Image(type="pil", label="Collision Predictor Output Image", size=(112, 112))
                text_output_vehicle = gr.Textbox(label="Collision Predictor Result")

        # 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_output_kosmos.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 = kosmos2.draw_entity_boxes_on_image(img_input, entities, entity_index=idx)
            return updated_image
        selected.change(update_output_image, [image_input, image_output_kosmos, entity_output, selected], [image_output_kosmos])

        def combined_predictions(img, description_type):
            # Kosmos2 predictions
            kosmos_image, kosmos_text, entities = kosmos2.generate_predictions(img, description_type)
            
            # VehiclePredictor predictions
            vehicle_image, vehicle_text = vehicle_predictor.predict_image(img, vehicle_predictor.classifier_model)
            
            return kosmos_image, kosmos_text, entities, vehicle_image, vehicle_text

        run_button.click(fn=combined_predictions,
                         inputs=[image_input, text_input],
                         outputs=[image_output_kosmos, text_output_kosmos, entity_output, image_output_vehicle, text_output_vehicle],
                         show_progress=True, queue=True)

    demo.launch(share=True)

if __name__ == "__main__":
    main()