import cv2
import torch
import numpy as np
from transformers import DPTImageProcessor
import gradio as gr
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import torch.nn as nn


device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load your custom trained model
class CompressedStudentModel(nn.Module):
    def __init__(self):
        super(CompressedStudentModel, self).__init__()
        self.encoder = nn.Sequential(
            nn.Conv2d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(128, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(),
        )
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(256, 128, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 64, kernel_size=3, stride=2, padding=1, output_padding=1),
            nn.ReLU(),
            nn.Conv2d(64, 1, kernel_size=3, padding=1),
        )

    def forward(self, x):
        features = self.encoder(x)
        depth = self.decoder(features)
        return depth

# Initialize and load weights into the student model
model = CompressedStudentModel().to(device)
model.load_state_dict(torch.load("huntrezz_depth_v2.pt", map_location=device))
model.eval()

processor = DPTImageProcessor.from_pretrained("Intel/dpt-swinv2-tiny-256")

def preprocess_image(image):
    image = cv2.resize(image, (200, 200))
    image = torch.from_numpy(image).permute(2, 0, 1).unsqueeze(0).float().to(device)
    return image / 255.0

def plot_depth_map(depth_map, original_image):
    fig = plt.figure(figsize=(16, 9))
    ax = fig.add_subplot(111, projection='3d')
    x, y = np.meshgrid(range(depth_map.shape[1]), range(depth_map.shape[0]))
    
    # Normalize depth map to [0, 1] range
    norm = plt.Normalize(depth_map.min(), depth_map.max())
    colors = plt.cm.viridis(norm(depth_map))
    
    ax.plot_surface(x, y, depth_map, facecolors=colors, shade=False)
    ax.set_zlim(0, 1)
    
    # Adjust the view to look down at an angle from a higher position
    ax.view_init(elev=70, azim=90) 
    plt.axis('off')
    plt.close(fig)
    
    fig.canvas.draw()
    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
    
    return img

@torch.inference_mode()
def process_frame(image):
    if image is None:
        return None
    preprocessed = preprocess_image(image)
    predicted_depth = model(preprocessed).squeeze().cpu().numpy()
    
    depth_map = (predicted_depth - predicted_depth.min()) / (predicted_depth.max() - predicted_depth.min())
    
    if image.shape[2] == 3:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    
    return plot_depth_map(depth_map, image)

interface = gr.Interface(
    fn=process_frame,
    inputs=gr.Image(sources="webcam", streaming=True),
    outputs="image",
    live=True
)

interface.launch()