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