import gradio as gr
from PIL import Image
from torchvision import models, transforms
import gradio as gr
import subprocess
import torch
import cv2
import numpy as np
from models.with_mobilenet import PoseEstimationWithMobileNet
from modules.keypoints import extract_keypoints, group_keypoints
from modules.load_state import load_state
from modules.pose import Pose
import demo
from recon import reconWrapper


def get_rect(net, image, height_size):
    net = net.eval()

    stride = 8
    upsample_ratio = 4
    num_keypoints = Pose.num_kpts
    previous_poses = []
    delay = 33
    image = image[0]
    rect_path = image.replace('.%s' % (image.split('.')[-1]), '_rect.txt')
    print('Processing', image)
    img = cv2.imread(image, cv2.IMREAD_COLOR)
    orig_img = img.copy()
    heatmaps, pafs, scale, pad = demo.infer_fast(
        net, img, height_size, stride, upsample_ratio, cpu=False)

    total_keypoints_num = 0
    all_keypoints_by_type = []
    for kpt_idx in range(num_keypoints):  # 19th for bg
        total_keypoints_num += extract_keypoints(
            heatmaps[:, :, kpt_idx], all_keypoints_by_type, total_keypoints_num)

    pose_entries, all_keypoints = group_keypoints(
        all_keypoints_by_type, pafs)
    for kpt_id in range(all_keypoints.shape[0]):
        all_keypoints[kpt_id, 0] = (
            all_keypoints[kpt_id, 0] * stride / upsample_ratio - pad[1]) / scale
        all_keypoints[kpt_id, 1] = (
            all_keypoints[kpt_id, 1] * stride / upsample_ratio - pad[0]) / scale
    current_poses = []

    rects = []
    for n in range(len(pose_entries)):
        if len(pose_entries[n]) == 0:
            continue
        pose_keypoints = np.ones((num_keypoints, 2), dtype=np.int32) * -1
        valid_keypoints = []
        for kpt_id in range(num_keypoints):
            if pose_entries[n][kpt_id] != -1.0:  # keypoint was found
                pose_keypoints[kpt_id, 0] = int(
                    all_keypoints[int(pose_entries[n][kpt_id]), 0])
                pose_keypoints[kpt_id, 1] = int(
                    all_keypoints[int(pose_entries[n][kpt_id]), 1])
                valid_keypoints.append(
                    [pose_keypoints[kpt_id, 0], pose_keypoints[kpt_id, 1]])
        valid_keypoints = np.array(valid_keypoints)

        if pose_entries[n][10] != -1.0 or pose_entries[n][13] != -1.0:
            pmin = valid_keypoints.min(0)
            pmax = valid_keypoints.max(0)

            center = (0.5 * (pmax[:2] + pmin[:2])).astype('int')
            radius = int(0.65 * max(pmax[0]-pmin[0], pmax[1]-pmin[1]))
        elif pose_entries[n][10] == -1.0 and pose_entries[n][13] == -1.0 and pose_entries[n][8] != -1.0 and pose_entries[n][11] != -1.0:
            # if leg is missing, use pelvis to get cropping
            center = (
                0.5 * (pose_keypoints[8] + pose_keypoints[11])).astype('int')
            radius = int(
                1.45*np.sqrt(((center[None, :] - valid_keypoints)**2).sum(1)).max(0))
            center[1] += int(0.05*radius)
        else:
            center = np.array([img.shape[1]//2, img.shape[0]//2])
            radius = max(img.shape[1]//2, img.shape[0]//2)

        x1 = center[0] - radius
        y1 = center[1] - radius

        rects.append([x1, y1, 2*radius, 2*radius])

    np.savetxt(rect_path, np.array(rects), fmt='%d')
    print('Cropping boxes are saved at', rect_path)
    print(rect_path[0:7] +
          'apps/'+rect_path[7:])


def run_simple_test():
    resolution = str(256)
    start_id = -1
    end_id = -1
    cmd = ['--dataroot', 'pifuhd/sample_images', '--results_path', './results',
           '--loadSize', '1024', '--resolution', resolution, '--load_netMR_checkpoint_path',
           './checkpoints/pifuhd.pt',
           '--start_id', '%d' % start_id, '--end_id', '%d' % end_id]
    mesh_path = reconWrapper(cmd, True)
    print('Mesh is saved at', mesh_path)
    return mesh_path


def predict(image):
    # Save the input image to a file
    image_path = 'input_image.png'
    cv2.imwrite(image_path, cv2.cvtColor(image, cv2.COLOR_RGB2BGR))

    net = PoseEstimationWithMobileNet()
    checkpoint = torch.load(
        'pifuhd/checkpoint_iter_370000.pth', map_location='cpu')
    load_state(net, checkpoint)

    get_rect(net.cuda(), [image_path], 512)
    mesh_path = run_simple_test()
    return mesh_path

# Create the Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy"),
    outputs="text",
    title="3D Reconstruction from Image",
    description="Upload an image to get the path of the resulting .obj 3D file."
)

# Launch the app
iface.launch()