move stuff to merger class
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@ -9,7 +9,7 @@ from IPython import embed
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class ProducerSignals(QObject):
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finished = pyqtSignal(bool)
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# error = pyqtSignal(str)
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error = pyqtSignal(str)
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# start = pyqtSignal(float)
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# running = pyqtSignal()
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progress = pyqtSignal(float)
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@ -96,6 +96,93 @@ class Merger(QRunnable):
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self._right_cut = right_cut
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self._result = None
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self._stopRequest = False
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for df in [self._left_data, self._right_data]:
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if not self.check_dataframe(df):
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self.signals.error.emit("Merger.__init__: Error checking DataFrame structure!")
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def check_dataframe(self, df):
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"""Perform some sanity checks on the dataframe.
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Parameters
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----------
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df : pandas.DataFrame
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the DataFrame conataining the detections of the left or right camera.
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Returns
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-------
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bool
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True, if everything's all right, False otherwise.
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"""
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return True
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def to_numpy(self, df):
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"""Convert some columns of the DataFrame to numpy arrays.
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Parameters
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----------
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df : pandas.DataFrame
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The DataFrame containing the detections.
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Returns
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-------
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numpy.ndarray
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3D array containing the x,y coordinates of each detection in each frame. Shape (num_detections, num_keypoints, 2)
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numpy.ndarray
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2D array with visibility score for each of the keypoints in each frame. Shape (num_detections, num_keypoints)
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numpy.ndarray
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2D array, Coordinates of the bounding box for each detection. Shape: (num_detections, 4) x1, y1, x2, y2
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"""
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key_columns = [c for c in df.columns if "key_" in c]
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box_columns = [c for c in df.columns if "box_" in c]
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num_detections = len(df)
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num_keypoints = len(key_columns)
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dimensions = 2
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keypoints = np.empty((num_detections, num_keypoints, dimensions))
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visibility = np.empty((num_detections, num_keypoints))
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boxcoordinates = np.empty((num_detections, 4))
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for i, row in df.iterrows():
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for j, k in enumerate(key_columns):
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key_data = row[k]
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l = list(map(float, list(key_data[1:-1].split(","))))
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keypoints[i, j, :] = l
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for j, b in enumerate(box_columns):
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boxcoordinates[i, j] = row[b]
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if isinstance(row["visible"], str):
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vis = list(map(float, row["visible"][1:-1].split()))
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visibility[i, :] = vis
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else:
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visibility[i, :] = row["visible"]
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return keypoints, visibility, boxcoordinates
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def sort_detections(self, keypoints, threshold, left=True):
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"""Categorize the detections into those that are easy (not in the visual overlap zone) and those that are tricky, i.e. right across the threshold.
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Detections beyond threshold can be discarded, those across the threshold need to be treated separately.
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Parameters
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----------
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keypoints : np.ndarray
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3d array of keypoint coordinates (num detections, num keypoints, (x,y))
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threshold : int
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the threshold line at which the data should be merged
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left : bool, optional
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whether or not the data is from the left side, controls how the threshold is interpreted, by default True
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Returns
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-------
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np.ndarray
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The indices of the easy detections
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np.ndarray
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The tricky detections
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"""
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if left:
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easyindeces = np.where(np.all(keypoints[:,:,0] < threshold, axis=1))[0]
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trickyindices = np.where(np.any((keypoints[:,:,0] >= threshold) &
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(keypoints[:,:,0] < threshold), axis=1))[0]
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else:
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easyindeces = np.where(np.all(keypoints[:,:,0] >= threshold, axis=1))[0]
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trickyindices = np.where(np.any((keypoints[:,:,0] < threshold) &
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(keypoints[:,:,0] >= threshold), axis=1))[0]
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return easyindeces, trickyindices
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@pyqtSlot()
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def stop_request(self):
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@ -103,23 +190,43 @@ class Merger(QRunnable):
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@pyqtSlot()
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def run(self):
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logging.debug("Merger: running merge for %i frames", max_frames)
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self._stopRequest = False
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max_frames = max(self._left_data.frame.max(), self._right_data.frame.max())
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step = max_frames // 100
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self._result = pd.DataFrame(columns=self._left_data.columns)
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for frame in range(max_frames):
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if self._stopRequest:
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break
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lf = self._left_data[self._left_data.frame == frame]
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rf = self._right_data[self._right_data.frame == frame]
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merge_frame(lf, rf, self._left_cut, self._right_cut, self._result)
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if frame % step == 0:
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self.signals.progress.emit(frame/max_frames)
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time.sleep(0.01)
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max_frames = len(self._left_data) + len(self._right_data)
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logging.debug("Cutting left detections to limit %i", self.left_cut)
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lkeypoints, lquality, lbox = self.to_numpy(self._left_data)
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lframes = self._left_data.frame.values
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led, ltd = self.sort_detections(lkeypoints, self.left_cut, left=True)
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logging.debug("Cutting right detections to limit %i", self._right_cut_cut)
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rkeypoints, rquality, rbox = self.to_numpy(self.right_data)
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rframes = self.right_data.frame.values
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red, rtd = self.sort_detections(rkeypoints, self.right_cut, left=False)
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rkeypoints[:, :, 0] += (self.left_cut - self.right_cut)
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# here we need to decide what to do with these data points, trust the left, or trust the right perspective?
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# we could also discard them. unless it is a lot of data points, not much harm will be done...
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# next step after handling the tricky ones is to export the data again to pandas? nixtrack?
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# 1. the right coordinates have to adapted! x - right_threshold + left_threshold!
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embed()
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exit()
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# logging.debug("Merger: running merge for %i frames", max_frames)
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# self._stopRequest = False
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# max_frames = max(self._left_data.frame.max(), self._right_data.frame.max())
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# step = max_frames // 100
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# self._result = pd.DataFrame(columns=self._left_data.columns)
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# for frame in range(max_frames):
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# if self._stopRequest:
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# break
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# lf = self._left_data[self._left_data.frame == frame]
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# rf = self._right_data[self._right_data.frame == frame]
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# merge_frame(lf, rf, self._left_cut, self._right_cut, self._result)
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# if frame % step == 0:
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# self.signals.progress.emit(frame/max_frames)
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# time.sleep(0.01)
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self._signals.finished.emit(True and (not self._stopRequest))
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@property
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