api_server.py: rework so it actually kinda works

results are weirdly poor, but idk

api_server.py

@@ -1,40 +1,24 @@
+import json
 from io import BytesIO
-from typing import Optional
-
-from fastapi import FastAPI, File, UploadFile
-from pydantic import BaseModel
 
 import numpy as np
-from cv2 import cv2
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from nets import Model
+from cv2 import cv2
+from fastapi import FastAPI, File, UploadFile
 from PIL import Image
 
+from nets import Model
 
 
 app = FastAPI()
 
-# TODO
-#   beide modelle laden, jeweils eine gpu zuweisen
-#   routen bauen, gegen die man bilder werfen kann, die dann jeweils von einem modell interpretiert werden
-#   ergebnisse zurueck geben
-#
-#   input validierung nicht vergessen
-#   paramter (bildgroesse etc.) konfigurierbar machen oder automatisch rausfinden?
-#       kommt ctd überhaupt mit was anderem klar?
-
-
-
-class IrImage(BaseModel):
-    image: np.array
-
-
 reference_pattern_path = '/home/nils/kinect_reference_cropped.png'
 reference_pattern = cv2.imread(reference_pattern_path)
 model_path = "train_log/models/latest.pth"
-device = torch.device('cuda:0')
+# model_path = "train_log/models/epoch-100.pth"
+device = torch.device('cuda')
 
 model = Model(max_disp=256, mixed_precision=False, test_mode=True)
 model = nn.DataParallel(model, device_ids=[device])
@@ -44,32 +28,27 @@ model.load_state_dict(state_dict, strict=True)
 model.to(device)
 model.eval()
 
+class NumpyEncoder(json.JSONEncoder):
+    def default(self, obj):
+        if isinstance(obj, np.ndarray):
+            return obj.tolist()
+        return json.JSONEncoder.default(self, obj)
 
-def normalize_and_colormap(img):
-    ret = (img - img.min()) / (img.max() - img.min()) * 255.0
-    if isinstance(ret, torch.Tensor):
-        ret = ret.cpu().detach().numpy()
-    ret = ret.astype("uint8")
-    ret = cv2.applyColorMap(ret, cv2.COLORMAP_INFERNO)
-    return ret
 
-
+def inference(left, right, model, n_iter=20):
-def inference_ctd(left, right, model, n_iter=20):
     print("Model Forwarding...")
-    # print(left.shape)
-    # left = left.cpu().detach().numpy()
-    # imgL = left
-    # imgR = right.cpu().detach().numpy()
     imgL = np.ascontiguousarray(left[None, :, :, :])
     imgR = np.ascontiguousarray(right[None, :, :, :])
 
-    # chosen for convenience
+    device = torch.device('cuda')
-    device = torch.device('cuda:0')
 
     imgL = torch.tensor(imgL.astype("float32")).to(device)
     imgR = torch.tensor(imgR.astype("float32")).to(device)
-    imgL = imgL.transpose(2, 3).transpose(1, 2)
 
+    # Funzt grob
+    imgR = imgR.transpose(1,2)
+    imgL = imgL.transpose(1,2)
+    
     imgL_dw2 = F.interpolate(
         imgL,
         size=(imgL.shape[2] // 2, imgL.shape[3] // 2),
@@ -82,15 +61,14 @@ def inference_ctd(left, right, model, n_iter=20):
         mode="bilinear",
         align_corners=True,
     )
+
     with torch.inference_mode():
         pred_flow_dw2 = model(image1=imgL_dw2, image2=imgR_dw2, iters=n_iter, flow_init=None)
         pred_flow = model(imgL, imgR, iters=n_iter, flow_init=pred_flow_dw2)
 
-    for i, (pf, pf_dw2) in enumerate(zip(pred_flow, pred_flow_dw2)):
+    pred_disp = torch.squeeze(pred_flow[:, 0, :, :]).cpu().detach().numpy()
-        pred_disp = torch.squeeze(pf[:, 0, :, :]).cpu().detach().numpy()
-        pred_disp_norm = cv2.normalize(pred_disp, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
 
-    return pred_disp_norm
+    return pred_disp
 
 
 @app.put("/ir")
@@ -99,11 +77,21 @@ async def read_ir_input(file: UploadFile = File(...)):
         img = np.array(Image.open(BytesIO(await file.read())))
     except Exception as e:
         return {'error': 'couldn\'t read file', 'exception': e}
-    print(img.shape)
+
+    # img = cv2.normalize(img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
     if len(img.shape) == 2:
-        img = np.stack((img for _ in range(3)))
+        img = cv2.merge([img for _ in range(3)])
-    pred_disp = inference_ctd(np.array(img), reference_pattern, None)
+    if img.shape == (1024, 1280, 3):
-    return {"pred_disp": pred_disp}
+        diff = (512 - 480) // 2
+        downsampled = cv2.pyrDown(img)
+        img = downsampled[diff:downsampled.shape[0]-diff, 0:downsampled.shape[1]]
+
+    img = img.transpose((1,2,0))
+    ref_pat = reference_pattern.transpose((1,2,0))
+
+    pred_disp = inference(img, ref_pat, model)
+
+    return json.dumps({'disp': pred_disp, 'reference': ref_pat, 'input': img}, cls=NumpyEncoder)
 
 
 @app.get('/')