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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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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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for img in os.scandir(img_dir): |
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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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start = datetime.now() |
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r = requests.put(f'{API_URL}/ir', files=openBin) |
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end = datetime.now() |
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print('received response') |
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print(f'processing took {end - start}') |
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r.raise_for_status() |
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# FIXME yuck, don't json the json |
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pred_disp = np.asarray(json.loads(json.loads(r.text))['disp'], dtype='uint8') |
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ref_pat = np.asarray(json.loads(json.loads(r.text))['reference'], dtype='uint8').transpose((2,0,1)).astype('uint8') |
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pred_disp = cv2.transpose(pred_disp) |
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cv2.imshow('Input Image', input_img) |
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# cv2.imshow('Reference Image', ref_pat) |
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cv2.imshow('Predicted Disparity', normalize_and_colormap(pred_disp)) |
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cv2.waitKey() |
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