frontend/__init__.py: major refactor, still wip

now uses async to pipeline images for inference, triggered by signals, generates pointclouds from depth and displays them

frontend/__init__.py

@@ -1,4 +1,11 @@
-import requests
+import signal
+from time import sleep
+
+# import requests
+import httpx as requests
+import asyncio
+import open3d as o3d
+
 from cv2 import cv2
 import numpy as np
 import json
@@ -13,6 +20,27 @@ img_dir = '../../usable_imgs/'
 cv2.namedWindow('Input Image')
 cv2.namedWindow('Predicted Disparity')
 
+vis = o3d.visualization.VisualizerWithKeyCallback()
+viscont = o3d.visualization.ViewControl()
+# vis.register_key_callback(99)
+vis.create_window()
+
+K = np.array([[567.6, 0, 324.7], [0, 570.2, 250.1], [0, 0, 1]], dtype=np.float32)
+
+# temporal_init = requests.get(f'{API_URL}/temporal_init')
+
+good_models = [260, 183]
+interesting = [214, ]
+# new ganz gut bei ca 175
+verbose = False
+
+running_tasks = set()
+minimal_data = False
+
+with open('frontend.pid', 'w+') as f:
+    print('writing pid')
+    f.write(str(os.getpid()))
+
 
 # epoch 75 ist weird
 
@@ -24,6 +52,34 @@ class NumpyEncoder(json.JSONEncoder):
         return json.JSONEncoder.default(self, obj)
 
 
+def update_vis(*args):
+    vis.poll_events()
+    vis.update_renderer()
+
+
+# signal.signal(signal.SIGALRM, update_vis)
+# signal.setitimer(signal.ITIMER_REAL, 0.1, 0.1)
+
+
+def ghetto_lcn(img):
+    # gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    gray = img
+
+    float_gray = gray.astype(np.float32) / 255.0
+
+    blur = cv2.GaussianBlur(float_gray, (0, 0), sigmaX=2, sigmaY=2)
+    num = float_gray - blur
+
+    blur = cv2.GaussianBlur(num * num, (0, 0), sigmaX=20, sigmaY=20)
+    den = cv2.pow(blur, 0.5)
+
+    gray = num / den
+
+    # cv2.normalize(gray, dst=gray, alpha=0.0, beta=1.0, norm_type=cv2.NORM_MINMAX)
+    cv2.normalize(gray, dst=gray, alpha=0.0, beta=255.0, norm_type=cv2.NORM_MINMAX)
+    return gray
+
+
 def normalize_and_colormap(img):
     ret = (img - img.min()) / (img.max() - img.min()) * 255.0
     ret = ret.astype("uint8")
@@ -31,10 +87,74 @@ def normalize_and_colormap(img):
     return ret
 
 
-def change_epoch():
+def reproject(disparity_img):
+    print('reprojecting')
+    baseline = 0.075
+    depth_img = baseline * K[0][0] / disparity_img
+    pointcloud = o3d.geometry.PointCloud()
+    intrinsics = o3d.pybind.camera.PinholeCameraIntrinsic()
+    print('setting intrinsics')
+    intrinsics.set_intrinsics(width=640, height=480, fx=K[0][0], fy=K[1][1], cx=0., cy=0.)
+    # depth = open3d.geometry.Image(depth_img.astype('float32'))
+    rgb = normalize_and_colormap(disparity_img)
+    rgb = o3d.geometry.Image(rgb * 255)
+    print(depth_img.max(), depth_img.min())
+    depth_img = np.log(depth_img + (1 - depth_img.min()) + 1)
+    print(depth_img.max(), depth_img.min())
+    depth = o3d.geometry.Image(depth_img.astype('float32'))
+
+    rgb_depth = o3d.geometry.RGBDImage().create_from_color_and_depth(
+        color=rgb,
+        depth=depth,
+        depth_scale=1,
+        convert_rgb_to_intensity=False,
+    )
+    print('creating pointcloud')
+    # depth = open3d.cpu.pybind.t.geometry.Image(depth_img.astype('float32'))
+    # depth.colorize_depth(1.0, 0., 1.)
+    # print('now really creating pointcloud')
+    # dpcd = pointcloud.create_from_depth_image(
+    #     depth=depth,
+    #     intrinsic=intrinsics,
+    # )
+    # print(type(depth))
+
+    pcd = pointcloud.create_from_rgbd_image(
+        image=rgb_depth,
+        intrinsic=intrinsics,
+        # project_valid_depth_only=False,
+    )
+
+    flip_transform = [[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]]
+    # dpcd.paint_uniform_color(np.asarray([0.5, 0.4, 0.25]))
+    pcd.transform(flip_transform)
+    # dpcd.transform(flip_transform)
+    pcd.remove_statistical_outlier(nb_neighbors=20, std_ratio=2.0)
+    print('drawing pointcloud')
+    # vis.clear_geometries()
+    # vis.add_geometry(pcd, reset_bounding_box=True)
+    # viscont.rotate(250, 500)
+    vis.update_geometry(pcd)
+    vis.poll_events()
+    vis.update_renderer()
+
+    # vis.run()
+    # o3d.visualization.draw(geometry=[rgb_depth])
+    # o3d.visualization.draw([dpcd])
+
+
+def change_epoch(epoch: int = None):
+    if epoch is None:
         epoch = input('Enter epoch number or "latest"\n')
     r = requests.post(f'{API_URL}/model/update/{epoch}')
-    print(r.text)
+    # print(r.text)
+
+
+def change_reference():
+    r = requests.post(f'{API_URL}/params/update_reference')
+    print(r.json()['status'])
+    if r.json()['status'] == 'finished':
+        change_reference()
 
 
 def extract_data(data):
@@ -42,72 +162,190 @@ def extract_data(data):
     duration = data['duration']
 
     # get result and rotate 90 deg
-    pred_disp = cv2.transpose(np.asarray(data['disp'], dtype='uint8'))
+    # pred_disp = cv2.transpose(np.asarray(data['disp'], dtype='uint8'))
+    raw_disp = np.asarray(data['disp'])
+    # print(raw_disp.min(), raw_disp.max())
+    if raw_disp.min() < 0:
+        # print('Negative disparity detected. shifting...')
+        raw_disp = raw_disp - raw_disp.min()
+    if raw_disp.max() > 255:
+        # print('Excessive disparity detected. scaling...')
+        raw_disp = raw_disp / (raw_disp.max() / 255)
+    pred_disp = np.asarray(raw_disp, dtype='uint8')
 
-    if 'input' not in data:
+    # if 'input' not in data:
+    if len(data) == 2:
         return pred_disp, duration
-
-    in_img = np.asarray(data['input'], dtype='uint8').transpose((2, 0, 1))
-    ref_pat = np.asarray(data['reference'], dtype='uint8').transpose((2, 0, 1))
+    ref_pat = data.get('reference', None)
+    in_img = np.asarray(data['input'], dtype='uint8')  # .transpose((2, 0, 1))
+    if ref_pat:
+        ref_pat = np.asarray(ref_pat, dtype='uint8')  # .transpose((2, 0, 1))
     return pred_disp, in_img, ref_pat, duration
 
 
-def downsize_input_img():
-    input_img = cv2.imread(img.path)
+def downsize_input_img(path):
+    input_img = None
+    while input_img is None:
+        input_img = cv2.imread(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]]
+    # print(input_img.shape)
+    input_img = cv2.normalize(input_img, None, 0, 255, cv2.NORM_MINMAX, cv2.CV_8U)
+    # input_img = ghetto_lcn(input_img)
 
     cv2.imwrite('buffer.png', input_img)
 
 
-def put_image(img_path):
+async def put_image(img_path):
     openBin = {'file': ('file', open(img_path, 'rb'), 'image/png')}
+    if verbose:
         print('sending image')
-    r = requests.put(f'{API_URL}/ir', files=openBin)
+    async with requests.AsyncClient() as client:
+        r = await client.put(f'{API_URL}/ir', files=openBin)
+    if verbose:
         print('received response')
     r.raise_for_status()
     data = json.loads(json.loads(r.text))
     return data
 
 
-def change_minimal_data(enabled):
-    r = requests.post(f'{API_URL}/params/minimal_data/{not enabled}')
+def change_minimal_data(current: bool = None):
+    global minimal_data
+    if current is None:
+        current = minimal_data
+    minimal_data = not current
+    r = requests.post(f'{API_URL}/params/minimal_data/{minimal_data}')
     cv2.destroyWindow('Input Image')
     cv2.destroyWindow('Reference Image')
 
 
-if __name__ == '__main__':
-    while True:
-        for img in os.scandir(img_dir):
-            start = datetime.now()
-            if 'ir' not in img.path:
-                continue
+def change_temporal_init(enabled):
+    global temporal_init
+    r = requests.post(f'{API_URL}/params/temporal_init/{not enabled}')
+    temporal_init = not temporal_init
 
-            # alternatively: use img.path for native size
-            downsize_input_img()
 
-            data = put_image('buffer.png')
-            if 'input' in data:
+def handle_keypress(key):
+    if key == 113:
+        quit()
+    elif key == 101:
+        change_epoch()
+    elif key == 109:
+        change_minimal_data()
+    elif key == 116:
+        change_temporal_init(temporal_init)
+    elif key == 99:
+        change_reference()
+
+
+async def do_inference():
+    start = datetime.now()
+    data = await put_image('buffer.png')
+    in_img = None
+    ref_pat = None
+    if len(data) == 4:
         pred_disp, in_img, ref_pat, duration = extract_data(data)
-            else:
+    elif len(data) == 2:
         pred_disp, duration = extract_data(data)
+    reproject(pred_disp)
+    show_results(duration, in_img, pred_disp, ref_pat, start)
+    # reproject(pred_disp)
 
+
+def show_results(duration, in_img, pred_disp, ref_pat, start):
+    print(f"Pred. Disparity: \n\t{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}")
+    if verbose:
         print(f'inference took {duration:1.4f}s')
         print(f'pipeline and transfer took another {(datetime.now() - start).total_seconds() - float(duration):1.4f}s')
-            print(f"Pred. Disparity: \n\t{pred_disp.min():.{2}f}/{pred_disp.max():.{2}f}\n")
-
-            if 'input' in data:
+        print(f'total {(datetime.now() - start).total_seconds():1.4f}s')
+    if in_img is not None:
         cv2.imshow('Input Image', in_img)
+    else:
+        cv2.imshow('Input Image', cv2.imread('buffer.png'))
+    if ref_pat is not None:
         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()
+    key = cv2.waitKey(1000)
+    handle_keypress(key)
 
-            if key == 113:
-                quit()
-            elif key == 101:
-                change_epoch()
-            elif key == 109:
-                change_minimal_data('input' not in data)
+
+async def fresh_img():
+    # print('running task')
+    start = datetime.now()
+    print(f'started at {start}')
+    downsize_input_img('kinect_ir.png')
+    await do_inference()
+    print(f'task took {(datetime.now() - start).total_seconds()}')
+    print()
+
+
+def create_task(*args):
+    global running_tasks
+    # print('received signal')
+    print(f'currently running: {len(running_tasks)} tasks')
+    task = asyncio.create_task(fresh_img())
+    # print(f'created task {task.get_name()}')
+    running_tasks.add(task)
+    task.add_done_callback(running_tasks.discard)
+    # await task
+    # return task
+
+
+signal.signal(signal.SIGUSR1, create_task)
+
+
+async def run_test(img_dir, iterate_checkpoints):
+    img_dir = list(os.scandir(img_dir))
+    for epoch in range(175, 270):
+        if iterate_checkpoints:
+            change_epoch(epoch)
+            print()
+            print(f'loaded epoch {epoch}')
+        for img in img_dir:
+            if 'ir' not in img.path:
+                continue
+
+            # alternatively: use img.path for native size
+            downsize_input_img(img.path)
+
+            # asyncio.run(do_inference())
+            await do_inference()
+            await asyncio.sleep(10)
+
+
+async def main():
+    use_live_data = True
+    iterate_checkpoints = False
+    # change_epoch(good_models[1])
+    # change_epoch('latest')
+    o3d.utility.set_verbosity_level(o3d.utility.VerbosityLevel.Info)
+
+    change_epoch(150)
+    change_minimal_data(False)
+    await asyncio.sleep(50000)
+
+    # signal.signal(signal.SIGBUS, lambda x: print('received sigbus'))
+    # loop = asyncio.get_running_loop()
+    # loop.run_forever()
+    # loop = asyncio.get_event_loop()
+    while True:
+        # create_task()
+        # await asyncio.sleep(0.1)
+        # await run_test(img_dir, iterate_checkpoints)
+        await asyncio.sleep(50000)
+        # print('[main] slept')
+        # if use_live_data:
+        #     signal.pause()
+
+    #     else:
+    #         await run_test(img_dir, iterate_checkpoints)
+
+
+#
+
+
+if __name__ == '__main__':
+    asyncio.run(main())