import torch
import torch.nn.functional as F
import numpy as np
import cv2
from imread_from_url import imread_from_url

from nets import Model

#Ref: https://github.com/megvii-research/CREStereo/blob/master/test.py
def inference(left, right, model, n_iter=20):
	
	print("Model Forwarding...")
	imgL = left.transpose(2, 0, 1)
	imgR = right.transpose(2, 0, 1)
	imgL = np.ascontiguousarray(imgL[None, :, :, :])
	imgR = np.ascontiguousarray(imgR[None, :, :, :])

	imgL = torch.tensor(imgL.astype("float32"))
	imgR = torch.tensor(imgR.astype("float32"))

	imgL_dw2 = F.interpolate(
		imgL,
		size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
		mode="bilinear",
		align_corners=True,
	)
	imgR_dw2 = F.interpolate(
		imgR,
		size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
		mode="bilinear",
		align_corners=True,
	)
	# print(imgR_dw2.shape)

	pred_flow_dw2 = model(imgL_dw2, imgR_dw2, iters=n_iter, flow_init=None)

	pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
	pred_disp = torch.squeeze(pred_flow[:, 0, :, :]).detach().numpy()

	return pred_disp

if __name__ == '__main__':

	left_img = imread_from_url("https://vision.middlebury.edu/stereo/data/scenes2003/newdata/cones/im2.png")
	right_img = imread_from_url("https://vision.middlebury.edu/stereo/data/scenes2003/newdata/cones/im6.png")

	model_path = "models/crestereo_eth3d.pth"

	model = Model(max_disp=256, mixed_precision=False, test_mode=True)
	model.load_state_dict(torch.load(model_path), strict=True)
	model.eval()

	disp = inference(left_img, right_img, model, n_iter=20)
	disp_vis = (disp - disp.min()) / (disp.max() - disp.min()) * 255.0
	disp_vis = disp_vis.astype("uint8")
	disp_vis = cv2.applyColorMap(disp_vis, cv2.COLORMAP_INFERNO)
	disp_vis = cv2.resize(disp_vis, left_img.shape[1::-1])

	combined_img = np.hstack((left_img, disp_vis))
	cv2.imshow("output", combined_img)
	cv2.imwrite("output.jpg", combined_img)
	cv2.waitKey(0)