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| 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.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("cuda") | |
| 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("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 = 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() | |