import requests from cv2 import cv2 import numpy as np import json import os from datetime import datetime API_URL = 'http://127.0.0.1:8000' img_dir = '../../usable_imgs/' cv2.namedWindow('Input Image') cv2.namedWindow('Predicted Disparity') # epoch 75 ist weird def normalize_and_colormap(img): ret = (img - img.min()) / (img.max() - img.min()) * 255.0 ret = ret.astype("uint8") ret = cv2.applyColorMap(ret, cv2.COLORMAP_INFERNO) return ret while True: for img in os.scandir(img_dir): start = datetime.now() if 'ir' not in img.path: continue input_img = cv2.imread(img.path) if input_img.shape == (1024, 1280, 3): diff = (512 - 480) // 2 downsampled = cv2.pyrDown(input_img) input_img = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]] openBin = {'file': ('file', open(img.path, 'rb'), 'image/png')} print('sending image') r = requests.put(f'{API_URL}/ir', files=openBin) print('received response') r.raise_for_status() data = json.loads(json.loads(r.text)) # FIXME yuck, don't json the json pred_disp = np.asarray(data['disp'], dtype='uint8') in_img = np.asarray(data['input'], dtype='uint8').transpose((2,0,1)) ref_pat = np.asarray(data['reference'], dtype='uint8').transpose((2,0,1)).astype('uint8') duration = data['duration'] pred_disp = cv2.transpose(pred_disp) print(f'inference took {duration}s') print(f'pipeline and transfer took another {(datetime.now() - start).total_seconds() - float(duration)}s\n') cv2.imshow('Input Image', in_img) cv2.imshow('Reference Image', ref_pat) cv2.imshow('Normalized Predicted Disparity', normalize_and_colormap(pred_disp)) cv2.imshow('Predicted Disparity', pred_disp) key = cv2.waitKey() if key == 113: quit() elif key == 101: epoch = input('Enter epoch number or "latest"\n') r = requests.post(f'{API_URL}/model/update/{epoch}') print(r.text)