Read videos from HIM project files

background_subtraction_heat.py

@@ -1,17 +1,25 @@
 import numpy as np
-from typing import List
 import cv2
+from typing import List
+from data.library import ArkiteData, ArkiteDetectionHEF
+from cv2util import to_8_bit_image
+import time
 
 class BackgroundHeatmap:
-    def __init__(self, capture):
+    def __init__(self, capture, bg_teach_iters=50):
         self.heatmap = np.array([])
         self.cap = capture
-        self.backsub = cv2.createBackgroundSubtractorMOG2()
+        # self.backsub = cv2.createBackgroundSubtractorMOG2()
+        self.backsub = cv2.createBackgroundSubtractorKNN()
         # self.backsub = cv2.bgsegm_BackgroundSubtractorGSOC()
         # self.backsub = cv2.back()
 
         # Fill up with first frame
         ret, frame = self.cap.read()
+        # Suppose the first frame is background, teach it for a few iterations
+        for i in range(bg_teach_iters):
+            ret, frame = self.cap.read()
+            self.backsub.apply(cv2.blur(frame,(5,5)))
         self.lastframe = self.backsub.apply(frame)
         self.lastsum = self.to_floats(self.lastframe)
 
@@ -33,7 +41,7 @@ class BackgroundHeatmap:
 
     def update(self):
         ret, frame = self.cap.read()
-        self.lastframe = self.backsub.apply(frame)
+        self.lastframe = self.backsub.apply(cv2.blur(frame,(5,5)))
         self.lastsum += self.to_floats(self.lastframe)
         self.heatmap = self.gray_to_heat(
             self.float_to_gray(
@@ -41,26 +49,43 @@ class BackgroundHeatmap:
             )
         )
 
-cap = cv2.VideoCapture(0)
-diffsum = BackgroundHeatmap(cap)
-
-# Load diffsum up with first
-first = True
-while(True):
-    # Update heatmap
-    diffsum.update()
-
-    # Display the resulting frame
-    cv2.imshow('Heatmap', diffsum.heatmap)
-    cv2.imshow('Backsub', diffsum.lastframe)
-
-    if first:
-        cv2.moveWindow("Backsub", 1000, 100)
-        first = False
-
+if __name__ == '__main__':
+    # cap = cv2.VideoCapture(0)
+    projects_path = "C:\\UntrackedGit\\opencv_test\\him_projects"
+    data = ArkiteData(projects_path, 1)
+    for uc in data.use_cases():
+        for detection in data.detections(uc):
+            for recording in detection.recordings():
+                print("New recording: " + str(recording.name))
+                with recording.ir() as ir:
+                    for frame in ir.frame_sequence():
+                        converted = to_8_bit_image(frame, display_min=100, display_max=8000)
+                        # converted = cv2.applyColorMap(frame, colormap=cv2.COLORMAP_JET)
+                        cv2.imshow("IR", converted)
                         if cv2.waitKey(1) & 0xFF == ord('q'):
                             break
+                        # print("Showing frame...")
+                        # time.sleep(0.1)
 
-# When everything done, release the capture
-diffsum.cap.release()
     cv2.destroyAllWindows()
+
+# # Load diffsum up with first
+# first = True
+# while(True):
+#     # Update heatmap
+#     diffsum.update()
+
+#     # Display the resulting frame
+#     cv2.imshow('Heatmap', diffsum.heatmap)
+#     cv2.imshow('Backsub', diffsum.lastframe)
+
+#     if first:
+#         cv2.moveWindow("Backsub", 1000, 100)
+#         first = False
+
+#     if cv2.waitKey(1) & 0xFF == ord('q'):
+#         break
+
+# # When everything done, release the capture
+# diffsum.cap.release()
+# cv2.destroyAllWindows()

cv2util.py

@@ -0,0 +1,67 @@
+from typing import Optional, Iterable
+
+import cv2
+import numpy as np
+
+import util as abu
+from data.xhdfs import MIN_PIXEL_VALUE, MAX_PIXEL_VALUE
+
+_WHITE = 255
+
+
+def to_8_bit_image(src_image: np.ndarray,
+                   display_min: Optional[int] = None,
+                   display_max: Optional[int] = None) -> np.ndarray:
+    """Copies the image and normalizes pixel values to [0; 255].
+
+    See https://stackoverflow.com/questions/14464449/using-numpy-to-efficiently-convert-16-bit-image-data-to-8-bit-for-display-with"""
+
+    if src_image.dtype == np.uint8:  # Do nothing with images that are already 8-bit
+        return src_image
+
+    copy = np.array(src_image, copy=True)
+
+    # Clip pixel values if necessary
+    need_to_clip = display_min is not None or display_max is not None
+
+    if display_min is None:
+        display_min = MIN_PIXEL_VALUE
+    if display_max is None:
+        display_max = MAX_PIXEL_VALUE
+
+    if need_to_clip:
+        copy.clip(display_min, display_max, out=copy)
+
+    copy -= display_min
+    np.floor_divide(copy, (display_max - display_min + 1) / 256, out=copy, casting='unsafe')
+
+    return copy.astype(np.uint8)
+
+
+def imshow_xhdfs_frame(frame: np.ndarray,
+                       window_name: Optional[str] = None,
+                       color_map: Optional[int] = None,
+                       display_min: Optional[int] = None,
+                       display_max: Optional[int] = None) -> None:
+    frame_as_8_bit = to_8_bit_image(frame, display_min=display_min, display_max=display_max)
+    if color_map:
+        frame_as_8_bit = cv2.applyColorMap(frame_as_8_bit, color_map)
+
+    if not window_name:
+        window_name = __name__
+
+    cv2.imshow(window_name, frame_as_8_bit)
+    del frame_as_8_bit
+
+
+def box_mask(box_points: Iterable[abu.FloatPoint],
+             image_width: Optional[int] = None,
+             image_height: Optional[int] = None) -> np.ndarray:
+    image_width = image_width or abu.FRAME_WIDTH  # FIXME: hard-coded dimensions
+    image_height = image_height or abu.FRAME_HEIGHT
+
+    mask = np.zeros(shape=(image_height, image_width), dtype=np.uint8)
+    box_corners = np.asarray(box_points, dtype=np.int32)
+    cv2.fillConvexPoly(mask, box_corners, _WHITE)
+
+    return mask

data/box.py

@@ -0,0 +1,93 @@
+import json
+import os.path
+import re
+import zipfile
+from typing import NamedTuple, Optional, Iterable, List
+
+from numpy import ndarray
+
+from arkite_booster.cv2util import box_mask
+from arkite_booster.util import FloatPoint
+from arkite_booster.util import bounding_box_centre
+
+ArkiteBox = NamedTuple(typename='ArkiteBox',
+                       fields=[('name', str),
+                               ('points', List[FloatPoint]),
+                               ('depth', int),
+                               ('distance', int),
+                               ('centre', FloatPoint),
+                               ('mask', ndarray)])
+
+HEF_BOX_LIST_FILE = 'data/Model.Entities.Box.json'
+
+CALIBRATION_BOX_REGEX = re.compile('(Depth|IR) Cal ([0-9]+|- Center).*')
+GRAB_BOX_PREFIX = 'grab_'
+
+
+def load_boxes(model_entities_box_path: str,
+               project_ids: Optional[Iterable[int]] = None,
+               box_ids: Optional[Iterable[int]] = None,
+               keep_grab_boxes: bool = True,
+               keep_container_boxes: bool = True,
+               skip_calibration_boxes: bool = True) -> Iterable[ArkiteBox]:
+    model_entities_box_path = os.path.abspath(model_entities_box_path)
+
+    if model_entities_box_path.endswith('.json'):
+        with open(model_entities_box_path, 'r') as mebf:
+            all_boxes = json.load(mebf)
+    elif model_entities_box_path.endswith('.hef'):
+        with zipfile.ZipFile(model_entities_box_path, mode='r') as hef:
+            with hef.open(HEF_BOX_LIST_FILE) as mebf:
+                all_boxes = json.loads(mebf.read().decode('utf-8'))
+    else:
+        raise ValueError('Unknown file format: ' + model_entities_box_path)
+
+    boxes = []
+    for box_json in all_boxes:
+        if project_ids is not None and box_json['ProjectId'] not in project_ids:
+            continue
+
+        if box_ids is not None and box_json['Id'] not in box_ids:
+            continue
+
+        box_name = box_json['Name']
+        if skip_calibration_boxes and CALIBRATION_BOX_REGEX.match(box_name) is not None:
+            continue
+
+        if not keep_grab_boxes and box_name.startswith(GRAB_BOX_PREFIX):
+            continue
+
+        if not keep_container_boxes and not box_name.startswith(GRAB_BOX_PREFIX):
+            continue
+
+        points = [(coordinates["X"], coordinates["Y"]) for coordinates in box_json["DataPoints"]]
+        mask = box_mask(points)
+        centre = bounding_box_centre(points)
+
+        box = ArkiteBox(name=box_name,
+                        depth=box_json['Depth'],
+                        distance=box_json['Distance'],
+                        points=points,
+                        mask=mask,
+                        centre=centre)
+        boxes.append(box)
+
+    return boxes
+
+
+def boxes_for_use_case(box_metadata_root_dir: str,
+                       use_case: str,
+                       keep_grab_boxes: bool = True,
+                       keep_container_boxes: bool = True,
+                       skip_calibration_boxes: bool = True) -> Iterable[ArkiteBox]:
+    """Ad-hoc method to match the detection libraty with additional `.hef` files provided by Stijn."""
+    box_metadata_root_dir = os.path.abspath(box_metadata_root_dir)
+
+    use_case_name = use_case.split('/')[-1]
+    use_case_hef = os.path.join(box_metadata_root_dir, use_case_name + '.hef')
+    use_case_boxes = load_boxes(use_case_hef,
+                                keep_grab_boxes=keep_grab_boxes,
+                                keep_container_boxes=keep_container_boxes,
+                                skip_calibration_boxes=skip_calibration_boxes)
+
+    return use_case_boxes

data/hands.py

@@ -0,0 +1,71 @@
+import os.path
+from collections import OrderedDict
+from glob import glob
+from typing import Generator, Union, Tuple
+
+import cv2
+import numpy as np
+import pandas as pd
+
+HandMask = np.ndarray
+HandMaskMaybeWithFileName = Union[HandMask, Tuple[HandMask, str]]
+
+_CSV_FILE_NAME = 'data.csv'
+_CSV_COLUMNS = OrderedDict([('frame_number', int),
+                            ('hand_id', int),
+                            ('x', float),
+                            ('y', float),
+                            ('direction_x', float),
+                            ('direction_y', float),
+                            ('distance', float),
+                            ('detected', bool),
+                            ('frames_without_detection', int)])
+
+_HAND_MASK_FILE_GLOB = 'hand_labels_*.png'
+
+
+def load_coordinates(data_csv_path: str) -> pd.DataFrame:
+    data_csv_path = os.path.abspath(data_csv_path)
+    hand_data = pd.read_csv(data_csv_path,
+                            sep=',',
+                            header=None,
+                            names=_CSV_COLUMNS.keys(),
+                            dtype=_CSV_COLUMNS)
+
+    return hand_data
+
+
+def load_hand_masks(data_dir_path: str,
+                    return_file_names: bool = False) -> Generator[HandMaskMaybeWithFileName, None, None]:
+    data_dir_path = os.path.abspath(data_dir_path)
+
+    mask_files = glob(os.path.join(data_dir_path, _HAND_MASK_FILE_GLOB))
+    mask_files = sorted(mask_files)
+
+    for mf in mask_files:
+        hand_mask = cv2.imread(mf, cv2.IMREAD_UNCHANGED)
+
+        if return_file_names:
+            yield hand_mask, mf
+        else:
+            yield hand_mask
+
+
+class RecordingHandTracking:
+    def __init__(self, path: str):
+        self._path = os.path.abspath(path)
+
+    def hand_coordinates(self) -> pd.DataFrame:
+        return load_coordinates(os.path.join(self._path, _CSV_FILE_NAME))
+
+    def hand_mask_sequence(self, return_file_names: bool = False) -> Generator[HandMaskMaybeWithFileName, None, None]:
+        return load_hand_masks(self._path, return_file_names)
+
+
+        for mf in mask_files:
+            hand_mask = cv2.imread(mf, cv2.IMREAD_UNCHANGED)
+
+            if return_file_names:
+                yield hand_mask, mf
+            else:
+                yield hand_mask

data/library.py

@@ -0,0 +1,214 @@
+import json
+import os.path
+import zipfile
+from abc import ABC, abstractmethod
+from contextlib import contextmanager
+from glob import iglob
+from os import listdir
+from typing import Iterable, Optional, Union
+
+from data.xhdfs import XHDFS
+
+HEF_RECORDING_LIST_FILE = 'data/Model.Entities.Resource.json'
+
+TYPES = ['Activity', 'Button', 'Container', 'Process', 'Tool']
+TYPE_SHORTHANDS = {t[0]: t for t in TYPES}
+
+
+def is_old_format_detection(path: str) -> bool:
+    """Returns `True` if `path` points to a directory that contains a `.him` file and a recordings directory."""
+    if not os.path.isdir(path):
+        return False
+    return os.path.isdir(os.path.join(path, 'resources', 'recordings')) \
+           and any(f.endswith('.him') for f in listdir(path))
+
+
+def is_new_format_detection(path: str) -> bool:
+    """Returns `True` if `path` points to a directory that contains a `.hef` file."""
+    if not os.path.isdir(path):
+        return False
+    return any(f.endswith('.hef') for f in listdir(path))
+
+
+class ArkiteRecording(ABC):
+    def __init__(self, full_name: str):
+        self._full_name = full_name
+        self._name = os.path.split(full_name)[-1]
+        self._detection_type = TYPE_SHORTHANDS.get(self._name[0], None)
+
+    @property
+    def name(self) -> str:
+        return self._name
+
+    @property
+    def full_name(self) -> str:
+        return self._full_name
+
+    @property
+    def detection_type(self) -> str:
+        return self._detection_type
+
+    @abstractmethod
+    def depth(self) -> XHDFS:
+        raise NotImplementedError
+
+    @abstractmethod
+    def ir(self) -> XHDFS:
+        raise NotImplementedError
+
+
+class ArkiteRecordingDirectory(ArkiteRecording):
+    def __init__(self, full_name: str):
+        super().__init__(full_name)
+
+    def _data_file_path(self, data_type: str) -> str:
+        *location, name = os.path.split(self._full_name)
+        return os.path.join(*location, 'resources', 'recordings', name, data_type + os.path.extsep + 'xhdfs')
+
+    @contextmanager
+    def depth(self):
+        with XHDFS(open(self._data_file_path('Depth'), mode='rb')) as depth_xhdfs:
+            yield depth_xhdfs
+
+    @contextmanager
+    def ir(self):
+        with XHDFS(open(self._data_file_path('IR'), mode='rb')) as ir_xhdfs:
+            yield ir_xhdfs
+
+
+class ArkiteRecordingHEF(ArkiteRecording):
+    def __init__(self, full_name: str, hef_file_name: str, depth_file_name: str, ir_file_name: str):
+        super().__init__(full_name)
+        self._hef = hef_file_name
+        self._depth = depth_file_name
+        self._ir = ir_file_name
+
+    @staticmethod
+    def _data_file_path(file_name: str) -> str:
+        return "resources/" + file_name
+
+    @contextmanager
+    def depth(self) -> XHDFS:
+        with zipfile.ZipFile(self._hef) as hef, \
+                XHDFS(hef.open(self._data_file_path(self._depth))) as depth_xhdfs:
+            yield depth_xhdfs
+
+    @contextmanager
+    def ir(self) -> XHDFS:
+        with zipfile.ZipFile(self._hef) as hef, \
+                XHDFS(hef.open(self._data_file_path(self._ir))) as ir_xhdfs:
+            yield ir_xhdfs
+
+
+class ArkiteDetection(ABC):
+    def __init__(self, relative_path: str):
+        self._path = relative_path
+        self._full_name = relative_path
+        self._name = os.path.split(relative_path)[-1]
+
+    @property
+    def full_name(self) -> str:
+        return self._full_name
+
+    @property
+    def detection_name(self) -> str:
+        return self._name
+
+    @abstractmethod
+    def recordings(self) -> Iterable[ArkiteRecording]:
+        raise NotImplementedError
+
+
+class ArkiteDetectionDirectory(ArkiteDetection):
+    def __init__(self, relative_path: str):
+        super().__init__(relative_path)
+
+    def recordings(self) -> Iterable[ArkiteRecordingDirectory]:
+        recordings_dir = os.path.join(self._path, 'resources', 'recordings')
+
+        for rec_dir_name in listdir(recordings_dir):
+            name = rec_dir_name
+            full_name = os.path.join(self.full_name, name)
+            rec = ArkiteRecordingDirectory(full_name)
+            yield rec
+
+
+class ArkiteDetectionHEF(ArkiteDetection):
+    def __init__(self, relative_path: str):
+        super().__init__(relative_path)
+
+        hef_file_name = next(f for f in listdir(self._path) if f.endswith('.hef'))
+        self._hef = os.path.join(self._path, hef_file_name)
+
+    def recordings(self) -> Iterable[ArkiteRecordingHEF]:
+        with zipfile.ZipFile(self._hef, mode='r') as hef:
+            with hef.open(HEF_RECORDING_LIST_FILE) as recording_list:
+                file_list = json.loads(recording_list.read().decode('utf-8'))
+
+                previous_metadata = None
+                for file_metadata in file_list:
+                    if file_metadata['Extension'] != '.xhdfs' or file_metadata['RemovedId'] != 0:
+                        continue
+
+                    if previous_metadata is None:
+                        previous_metadata = file_metadata
+                        continue
+
+                    name, data_type = file_metadata['Name'].rsplit(' ', 1)
+                    prev_name, prev_type = previous_metadata['Name'].rsplit(' ', 1)
+
+                    assert name == prev_name and data_type != prev_type
+                    full_name = os.path.join(self._full_name, name)
+                    depth_name = (file_metadata if data_type == 'Depth' else previous_metadata)['FileName']
+                    ir_name = (file_metadata if data_type == 'IR' else previous_metadata)['FileName']
+
+                    previous_metadata = None
+
+                    rec = ArkiteRecordingHEF(full_name, self._hef, depth_file_name=depth_name, ir_file_name=ir_name)
+                    yield rec
+
+
+class ArkiteData:
+    def __init__(self, root_dir: str, use_case_depth: int):
+        self._root = os.path.abspath(root_dir)
+        self._use_case_depth = use_case_depth
+
+    def use_cases(self) -> Iterable[str]:
+        use_case_glob = os.path.sep.join('*' * self._use_case_depth)
+        full_glob = os.path.join(self._root, use_case_glob)
+        for d in iglob(full_glob):
+            if os.path.isdir(d):
+                yield d.replace(self._root, '')[1:]
+
+    def detections(self, use_cases: Optional[Union[Iterable[str], str]] = None) -> Iterable[ArkiteDetection]:
+        if isinstance(use_cases, str):
+            use_cases = (use_cases,)
+        if isinstance(use_cases, Iterable):
+            use_cases = set(use_cases)
+
+        for uc in self.use_cases():
+            if use_cases is not None and uc not in use_cases:
+                continue
+
+            use_case_dir = os.path.join(self._root, uc)
+
+            # Check if the directory itself is a detection
+            detection = self._dir_to_detection(use_case_dir)
+            if detection:
+                yield detection
+
+            # Check if subdirectories are detections themselves
+            for dd in listdir(use_case_dir):
+                detection_dir = os.path.join(use_case_dir, dd)
+                detection = self._dir_to_detection(detection_dir)
+                if detection:
+                    yield detection
+
+    @staticmethod
+    def _dir_to_detection(directory: str) -> Optional[ArkiteDetection]:
+        if is_old_format_detection(directory):
+            return ArkiteDetectionDirectory(directory)
+        elif is_new_format_detection(directory):
+            return ArkiteDetectionHEF(directory)
+        else:
+            return None

data/xhdfs.py

@@ -0,0 +1,92 @@
+from typing import NamedTuple, Generator, Any
+
+import numpy as np
+
+CAMERA_NODE_TYPES = ('unknown', 'depth', 'ir', 'rgb', 'rgb_luminance', 'rgb_mapped')
+
+HEADER_BYTES = 32
+BYTES_PER_SHORT = 2
+TIMESTAMP_BYTES = 8
+RAW_UNRELIABLE_PIXEL_VALUE = -11
+
+ENDIANNESS = 'little'
+
+# Pixels are stored as signed 16-bit integers with little endianness.
+# However, the only valid negative value is `-11` (unreliable pixel), which should just be converted into the
+# corresponding unsigned value, without any loss of information
+SOURCE_NUMPY_DATATYPE = np.dtype('<i2')
+TARGET_NUMPY_DATATYPE = np.dtype('=u2')  # TODO: is this cross-platform?
+MIN_PIXEL_VALUE = np.iinfo(TARGET_NUMPY_DATATYPE).min
+MAX_PIXEL_VALUE = np.iinfo(TARGET_NUMPY_DATATYPE).max
+UNRELIABLE_PIXEL_VALUE = np.cast[TARGET_NUMPY_DATATYPE](RAW_UNRELIABLE_PIXEL_VALUE).min()
+
+XHDFSHeader = NamedTuple(typename='XHDFSHeader',
+                         fields=[('n_frames', int),
+                                 ('width', int),
+                                 ('height', int),
+                                 ('version_number', int),
+                                 ('camera_node_type', str)])
+
+
+def read_unsigned_integer(fileobj, n_bytes: int = 4, signed=False) -> int:
+    return int.from_bytes(fileobj.read(n_bytes), ENDIANNESS, signed=signed)
+
+
+def bytes_to_image(frame_bytes: bytes, width: int, height: int) -> np.ndarray:
+    frame_bytes = frame_bytes[8:]  # Cut off the timestamp
+
+    # Height & width in that order match what OpenCV expects
+    frame_image = np.frombuffer(frame_bytes, SOURCE_NUMPY_DATATYPE).reshape(height, width)
+    frame_image = frame_image.astype(TARGET_NUMPY_DATATYPE, casting='unsafe', copy=False)
+    frame_image[frame_image == UNRELIABLE_PIXEL_VALUE] = 0
+
+    return frame_image
+
+
+class XHDFS:
+    def __init__(self, fileobj):
+        self._fileobj = fileobj
+
+        self._header = None
+        self._pixels_per_frame = -1
+
+    def __enter__(self):
+        header = self._read_header()
+
+        self._header = header
+        self._pixels_per_frame = header.width * header.height * BYTES_PER_SHORT + TIMESTAMP_BYTES
+
+        return self
+
+    def _read_header(self) -> XHDFSHeader:
+        n_frames = read_unsigned_integer(self._fileobj)
+        width = read_unsigned_integer(self._fileobj)
+        height = read_unsigned_integer(self._fileobj)
+
+        # Skip `CameraNodeId` & `WithBodies`
+        _ = self._fileobj.read(4 + 1)
+
+        version = read_unsigned_integer(self._fileobj)
+        node_type = read_unsigned_integer(self._fileobj, n_bytes=2)
+        node_type = CAMERA_NODE_TYPES[node_type]
+
+        # Skip `CameraNodeSerial` & 3 "spare" bytes
+        _ = self._fileobj.read(6 + 3)
+
+        header = XHDFSHeader(n_frames, width, height, version, node_type)
+        return header
+
+    def _read_frame_bytes(self) -> bytes:
+        return self._fileobj.read(self._pixels_per_frame)
+
+    def frame_sequence(self) -> Generator[np.ndarray, None, None]:
+        w, h = self._header.width, self._header.height
+        for _ in range(self._header.n_frames):
+            frame_bytes = self._read_frame_bytes()
+            frame = bytes_to_image(frame_bytes, w, h)
+            del frame_bytes
+            yield frame
+        pass
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self._fileobj.close()

util.py

@@ -0,0 +1,26 @@
+from typing import Dict, Any, Tuple, Union, Iterable
+
+IntPoint = Tuple[int, int]
+FloatPoint = Tuple[float, float]
+Point = Union[IntPoint, FloatPoint]
+
+AlgorithmParams = Dict[str, Any]
+
+FRAME_SHAPE = (512, 424)  # TODO: hardcoded
+FRAME_WIDTH, FRAME_HEIGHT = FRAME_SHAPE
+
+
+def bounding_box_centre(points: Iterable[FloatPoint]) -> FloatPoint:
+    n = 0
+    x_sum = 0.0
+    y_sum = 0.0
+
+    for x, y in points:
+        n += 1
+        x_sum += x
+        y_sum += y
+
+    x_centre = x_sum / n
+    y_centre = y_sum / n
+
+    return x_centre, y_centre