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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()
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