import numpy as np
import matplotlib.pyplot as plt
import cv2
import torch
from PIL import Image
from app.model import pth_model_static, cam, pth_processing
from app.face_utils import get_box
import mediapipe as mp

mp_face_mesh = mp.solutions.face_mesh

def preprocess_frame_and_predict_aus(frame):
    if len(frame.shape) == 2:
        frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2RGB)
    elif frame.shape[2] == 4:
        frame = cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB)

    with mp_face_mesh.FaceMesh(
        max_num_faces=1,
        refine_landmarks=False,
        min_detection_confidence=0.5,
        min_tracking_confidence=0.5
    ) as face_mesh:
        results = face_mesh.process(frame)
        
        if results.multi_face_landmarks:
            h, w = frame.shape[:2]
            for fl in results.multi_face_landmarks:
                startX, startY, endX, endY = get_box(fl, w, h)
                cur_face = frame[startY:endY, startX:endX]
                cur_face_n = pth_processing(Image.fromarray(cur_face))
                
                with torch.no_grad():
                    features = pth_model_static(cur_face_n)
                    au_intensities = features_to_au_intensities(features)
                
                grayscale_cam = cam(input_tensor=cur_face_n)
                grayscale_cam = grayscale_cam[0, :]
                cur_face_hm = cv2.resize(cur_face, (224, 224))
                cur_face_hm = np.float32(cur_face_hm) / 255
                heatmap = show_cam_on_image(cur_face_hm, grayscale_cam, use_rgb=True)
                
                return cur_face, au_intensities, heatmap
    
    return None, None, None

def features_to_au_intensities(features):
    features_np = features.detach().cpu().numpy()[0]
    au_intensities = (features_np - features_np.min()) / (features_np.max() - features_np.min())
    return au_intensities[:24]  # Assuming we want 24 AUs

def au_statistics_plot(frames, au_intensities_list):
    fig, ax = plt.subplots(figsize=(12, 6))
    au_intensities_array = np.array(au_intensities_list)
    
    for i in range(au_intensities_array.shape[1]):
        ax.plot(frames, au_intensities_array[:, i], label=f'AU{i+1}')
    
    ax.set_xlabel('Frame')
    ax.set_ylabel('AU Intensity')
    ax.set_title('Action Unit Intensities Over Time')
    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.tight_layout()
    return fig