[project] resturcture package
This commit is contained in:
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4e7ddd468c
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681a085f4a
@ -2,7 +2,7 @@ from PySide6.QtCore import QSize, Qt
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from PySide6.QtWidgets import QMainWindow, QWidget, QToolBar, QMenu, QMenuBar, QSizePolicy, QFileDialog
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from PySide6.QtGui import QKeySequence, QAction, QIcon
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from fixtracks.centralwidget import CentralWidget
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from fixtracks.widgets.centralwidget import CentralWidget
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import fixtracks.resources
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# Subclass QMainWindow to customize your application's main window
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@ -1,5 +1,5 @@
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import cv2
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import json
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import cv2 as cv
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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@ -7,7 +7,7 @@ import matplotlib.pyplot as plt
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from IPython import embed
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def show_video(filename):
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cap = cv.VideoCapture('2024.11.13_0_converted_right_undistorted_fixed.mp4')
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cap = cv2.VideoCapture('2024.11.13_0_converted_right_undistorted_fixed.mp4')
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count = 0
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while cap.isOpened():
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ret, frame = cap.read()
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@ -18,13 +18,13 @@ def show_video(filename):
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count += 1
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if count < 2000:
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continue
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gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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print(count)
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cv.imshow('frame', gray[::2,::2])
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if cv.waitKey(1) == ord('q'):
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cv2.imshow('frame', gray[::2,::2])
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if cv2.waitKey(1) == ord('q'):
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break
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cap.release()
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cv.destroyAllWindows()
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cv2.destroyAllWindows()
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def topandas(filename):
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temp = []
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@ -62,6 +62,7 @@ def topandas(filename):
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df = pd.DataFrame(tracking_data)
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return df
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# Test code
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def main():
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# import left and right tracks into a pandas dataframe
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right_tracks = "2024.11.13_0_converted_right_undistorted_fixed_pose.json"
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@ -1,326 +0,0 @@
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import logging
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import pandas as pd
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import cv2 as cv
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import time
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import pickle
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import numpy as np
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from PySide6.QtCore import QRunnable, Signal, QObject, Slot
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from IPython import embed
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class ProducerSignals(QObject):
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finished = Signal(bool)
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error = Signal(str)
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# start = pyqtSignal(float)
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# running = pyqtSignal()
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progress = Signal(float)
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progress2 = Signal((str, float, float))
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class ImageReader(QRunnable):
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finished = Signal(bool)
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def __init__(self, filename, frame=1000) -> None:
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super().__init__()
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self._filename = filename
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self._framenumber = frame
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self._signals = ProducerSignals()
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self._frame = None
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@Slot()
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def run(self):
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'''
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Your code goes in this function
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'''
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logging.debug("ImageReader: trying to open file %s", self._filename)
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cap = cv.VideoCapture(self._filename)
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framecount = int(cap.get(cv.CAP_PROP_FRAME_COUNT))
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if self._framenumber >= framecount:
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logging.warning("ImageReader: desired frame number (%i) exceeds the frame count (%i)! Redefined to frame count." % (self._framenumber, framecount))
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if not cap.isOpened():
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logging.debug("ImageReader: failed to open file %s", self._filename)
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self._signals.finished.emit(False)
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fn = 0
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while cap.isOpened() and fn < self._framenumber:
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ret, frame = cap.read()
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if not ret:
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logging.warning("ImageReader: failed to read frame %i", fn)
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self._signals.finished.emit(False)
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break
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fn += 1
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self._frame = frame # cv.cvtColor(frame, cv.COLOR_BGR2RGB)
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cap.release()
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self._signals.finished.emit(True)
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@property
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def signals(self):
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return self._signals
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@property
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def frame(self):
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return self._frame
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class DataFrameReader(QRunnable):
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finished = Signal(bool)
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def __init__(self, filename) -> None:
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super().__init__()
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self._filename = filename
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self._signals = ProducerSignals()
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self._dataframe = None
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@Slot()
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def run(self):
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'''
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Your code goes in this function
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'''
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logging.debug("DataFrameReader: trying to open file %s", self._filename)
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self._dataframe = pd.read_csv(self._filename, sep=";", index_col=0)
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self._signals.finished.emit(True)
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@property
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def signals(self):
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return self._signals
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@property
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def dataframe(self):
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return self._dataframe
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class Merger(QRunnable):
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def __init__(self, left_data, right_data, left_cut, right_cut) -> None:
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super().__init__()
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self._signals = ProducerSignals()
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self._left_data = left_data
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self._right_data = right_data
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self._left_cut = left_cut
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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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self._merged = None
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self._current_task = ""
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self._mergeprogress = 0.0
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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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logging.info("Converting to numpy ...")
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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_frames = 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_frames, num_keypoints, dimensions))
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visibility = np.empty((num_frames, num_keypoints))
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boxcoordinates = np.empty((num_frames, 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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logging.info("Converting to numpy done!")
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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 are ignored, 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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logging.info("Sorting detections")
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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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def select_and_transform(self, df, keypoints, boxes, quality, frames, valid_detections,
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left_threshold=None, right_threshold=None):
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keypoints = keypoints[valid_detections, :, :]
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boxes = boxes[valid_detections, :]
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quality = quality[valid_detections, :]
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frames = frames[valid_detections]
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df = df.iloc[valid_detections]
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if all([left_threshold, right_threshold]):
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keypoints[:, :, 0] += (left_threshold - right_threshold)
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boxes[:, [0, 2]] += (left_threshold - right_threshold)
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return df, keypoints, quality, boxes, frames
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def to_dataframe(self, old_left, old_right, lkeypoints, rkeypoints, lboxes, rboxes,
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lqualities, rqualities, lframes, rframes):
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frames = np.concatenate([lframes, rframes])
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sorting = np.argsort(frames)
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frames = frames[sorting]
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confidences = np.concatenate([old_left.confidence.values, old_right.confidence.values])
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confidences = confidences[sorting]
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classes = np.concatenate([old_left.cls.values, old_right.cls.values])
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classes = classes[sorting]
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names = np.concatenate([old_left.name.values, old_right.name.values])
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names = names[sorting]
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keypoints = np.concatenate([lkeypoints, rkeypoints], axis=0)
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keypoints = keypoints[sorting, :, :]
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boxes = np.concatenate([lboxes, rboxes], axis=0)
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boxes = boxes[sorting, :]
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qualities = np.concatenate([lqualities, rqualities], axis=0)
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qualities = qualities[sorting, :]
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tracks = np.concatenate([old_left.track_id.values, old_right.track_id.values], axis=0)
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tracks = tracks[sorting]
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# sort before converting to df
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q = []; b=[]; k = []
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for i in range(len(frames)):
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q.append(qualities[i, :])
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b.append(boxes[i, :])
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k.append(keypoints[i, :])
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d = {"frame":frames, "cls": classes, "name":names, "keypoint_score": q, "track": tracks,
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"keypoints": k, "box":b, "confidence":confidences}
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df = pd.DataFrame(d)
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return df
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def save(self, filename):
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if self._merged is None:
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logging.error("Saving/pickling merged dataFrame is None!")
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return
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logging.info("Saving/pickling merged file to %s" % filename)
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with open(filename, 'wb') as f:
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pickle.dump(self._merged, f)
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@Slot()
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def stop_request(self):
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self._stopRequest = True
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@Slot()
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def run(self):
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logging.info("Cutting left detections to limit %i", self._left_cut)
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self.signals.progress.emit(0.0)
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self.signals.progress2.emit("Merging", self._mergeprogress, 0.)
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if not self.check_dataframe(self._left_data) or not self.check_dataframe(self._right_data):
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logging.error("Left or right dataframe structure does not match my expectations")
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return None
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self.signals.progress.emit(0.05)
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if not self._stopRequest:
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logging.info("Converting to numpy... %s", "Left camera")
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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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self.signals.progress.emit(0.3)
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else:
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self.signals.finished(False)
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return
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if not self._stopRequest:
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logging.info("Converting to numpy... %s", "Right camera")
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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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self.signals.progress.emit(0.6)
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else:
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self.signals.finished(False)
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return
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logging.info("Filtering detections")
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left_easy, _ = self.sort_detections(lkeypoints, self._left_cut, left=True)
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right_easy, _ = self.sort_detections(rkeypoints, self._right_cut, left=False)
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self.signals.progress.emit(0.7)
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logging.info("Merging and transformation")
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ldf, lkeypoints, lquality, lboxes, lframes = self.select_and_transform(self._left_data, lkeypoints, lbox,
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lquality, lframes, left_easy)
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self.signals.progress.emit(0.8)
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rdf, rkeypoints, rquality, rboxes, rframes = self.select_and_transform(self._right_data, rkeypoints, rbox,
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rquality, rframes, right_easy,
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self._left_cut, self._right_cut)
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self.signals.progress.emit(0.9)
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if not self._stopRequest:
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self._merged = self.to_dataframe(ldf, rdf, lkeypoints, rkeypoints, lboxes, rboxes, lquality, rquality,
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lframes, rframes)
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self.signals.progress.emit(1.0)
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else:
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self.signals.finished(False)
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return
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logging.info("Merging done!")
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self._signals.finished.emit(True and (not self._stopRequest))
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@property
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def signals(self):
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return self._signals
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@property
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def result(self):
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return self._result
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# TEST code
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def main():
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logging.info("Loading data left")
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left = pd.read_csv("../data/left_tracks.csv", sep=";", index_col=0)
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logging.info("Loading data right")
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right = pd.read_csv("../data/right_tracks.csv", sep=";", index_col=0)
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# merge_detections(left, right, 2000, 300)
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merger = Merger(left, right, 2000, 300 )
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merger.run()
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if __name__ == "__main__":
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logging.basicConfig(level=logging.INFO, force=True)
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main()
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@ -2,9 +2,9 @@ import logging
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from PySide6.QtWidgets import QWidget, QStackedLayout, QSizePolicy
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from PySide6.QtCore import Qt
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from fixtracks.taskwidgets import FixTracks
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from fixtracks.detectionmerge import MergeDetections
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from fixtracks.taskwidget import TasksWidget
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from fixtracks.widgets.taskwidgets import FixTracks
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from fixtracks.widgets.detectionmerge import MergeDetections
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from fixtracks.widgets.taskwidget import TasksWidget
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class CentralWidget(QWidget):
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@ -4,7 +4,8 @@ from PySide6.QtCore import Qt, QThreadPool, Signal, Slot
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from PySide6.QtWidgets import QWidget, QGridLayout, QVBoxLayout, QHBoxLayout, QLabel, QPushButton, QComboBox, QSizePolicy, QSpinBox, QGraphicsView, QGraphicsScene, QGraphicsLineItem, QSpacerItem, QProgressDialog, QFileDialog
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from PySide6.QtGui import QImage, QPixmap, QColor, QPen
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from fixtracks.util import ImageReader, DataFrameReader, Merger
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from fixtracks.utils.reader import ImageReader, DataFrameReader
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from fixtracks.utils.merger import Merger
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class VideoPreview(QWidget):
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@ -329,4 +330,3 @@ class MergeDetections(QWidget):
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self._left_data = None
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self._right_data = None
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self.back.emit()
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@ -4,7 +4,7 @@ from PySide6.QtWidgets import QWidget, QGridLayout, QVBoxLayout, QLabel, QPushBu
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from PySide6.QtCore import QThreadPool
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from PySide6.QtGui import QImage, QPixmap
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from fixtracks.util import ImageReader
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from fixtracks.utils.reader import ImageReader
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class FixTracks(QWidget):
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def __init__(self, parent=None):
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