Cpt.Captain/CREStereo-pytorch-nxt
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

frontend/__init__.py: loop endlessly, show more info, allow model change

Browse Source
This commit is contained in:
Nils Koch 2022-06-02 12:13:29 +02:00
parent bb15dcd0a1
commit b614fcfd74
1 changed files with 37 additions and 24 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

61
frontend/__init__.py
View File

@ -14,6 +14,8 @@ 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
@ -22,31 +24,42 @@ def normalize_and_colormap(img):
return ret
for img in os.scandir(img_dir):
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]]
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')}
openBin = {'file': ('file', open(img.path, 'rb'), 'image/png')}
print('sending image')
start = datetime.now()
r = requests.put(f'{API_URL}/ir', files=openBin)
end = datetime.now()
print('received response')
print(f'processing took {end - start}')
r.raise_for_status()
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(json.loads(json.loads(r.text))['disp'], dtype='uint8')
ref_pat = np.asarray(json.loads(json.loads(r.text))['reference'], dtype='uint8').transpose((2,0,1)).astype('uint8')
pred_disp = cv2.transpose(pred_disp)
# 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', input_img)
# cv2.imshow('Reference Image', ref_pat)
cv2.imshow('Predicted Disparity', normalize_and_colormap(pred_disp))
cv2.waitKey()
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)