766 lines
31 KiB
Python
766 lines
31 KiB
Python
#!/usr/bin/env python
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import cv2
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import dlib
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import numpy as np
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import os
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import pickle
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import logging
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from scipy.ndimage.filters import gaussian_filter
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from PIL import Image, ImageDraw,ImageTk
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import pandas as pd
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import seaborn as sns
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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from matplotlib.figure import Figure
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from matplotlib import cm
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import sys
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if sys.version_info[0] < 3:
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import Tkinter as Tk
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else:
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import tkinter as Tk
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import time
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import datetime
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import Queue
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import coloredlogs
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import argparse
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import multiprocessing
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cur_dir = os.path.dirname(__file__)
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argParser = argparse.ArgumentParser(description='Draw a heatmap')
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argParser.add_argument(
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'--camera',
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'-c',
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default=0,
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type=int,
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help='The id of the camera'
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)
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argParser.add_argument(
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'--verbose',
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'-v',
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action="store_true",
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)
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argParser.add_argument(
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'--hide-graph',
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action="store_true",
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)
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argParser.add_argument(
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'--hide-preview',
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action="store_true",
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)
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argParser.add_argument(
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'--output-dir',
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'-o',
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help="directory in which to store evey x files",
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)
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argParser.add_argument(
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'--save-interval',
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type=int,
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default=15,
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help="Interval at which to save heatmap frames (in seconds)"
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)
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argParser.add_argument(
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'--queue-length',
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type=int,
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default=0,
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help="Nr of frames to keep in queue (adds a delay)"
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)
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argParser.add_argument(
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'--processes',
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type=int,
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default=4,
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help="Nr of total processes (min 3)"
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)
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argParser.add_argument(
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'--only-metrics',
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action="store_true",
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help="Render only metrics instead of the heatmap. Convenient for debugging."
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)
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args = argParser.parse_args()
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coloredlogs.install(
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level=logging.DEBUG if args.verbose else logging.INFO,
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# format='%(asctime)-15s %(name)s %(levelname)s: %(message)s'
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)
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logger = logging.getLogger(__name__)
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# im = cv2.imread("headPose.jpg");
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predictor_path = os.path.join(cur_dir,"shape_predictor_68_face_landmarks.dat")
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if args.output_dir:
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lastMetricsFilename = os.path.join(args.output_dir, 'last_metrics.p')
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else:
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lastMetricsFilename = None
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screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
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# metrics matrix
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metricsSize = [1920,1080]
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# metricsSize = [1280,800]
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# metricsSize = [960,600]
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metricsSize = [1080,1080] # no point in having it different from to the render size
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dataframe = pd.DataFrame(columns=['x','y'])
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renderSize = [1280,800]
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renderSize = [1080,1080]
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# Used to create a black backdrop, instead of the ugly Qt-gray, if neccessary
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screenSize = [1920,1080]
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spotS = int(100./720*renderSize[1])
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spotSize = (spotS, spotS)
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spot = Image.open(os.path.join(cur_dir,"spot.png")).convert('L')
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spot = spot.resize(spotSize)
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spot = np.array(spot)
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backdrop = None
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if screenSize != renderSize:
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shape = [screenSize[1],screenSize[0], 3]
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backdrop = np.zeros(shape, dtype=np.uint8)
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metrics = None
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if lastMetricsFilename and os.path.isfile(lastMetricsFilename):
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try:
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with open(lastMetricsFilename, "rb") as fp:
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metrics = pickle.load(fp)
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logger.warn("Loaded metrics from {}".format(lastMetricsFilename))
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except Exception as e:
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logger.exception(e)
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if metrics is None:
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metrics = np.zeros((metricsSize[1], metricsSize[0])) # (y, x)
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logger.warn("New metrics")
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screenDrawCorners = np.array([[0,0], [metricsSize[0]-1,0], [0, metricsSize[1]-1], [metricsSize[0]-1,metricsSize[1]-1]])
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def create_perspective_transform_matrix(src, dst):
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""" Creates a perspective transformation matrix which transforms points
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in quadrilateral ``src`` to the corresponding points on quadrilateral
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``dst``.
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Will raise a ``np.linalg.LinAlgError`` on invalid input.
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"""
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# See:
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# * http://xenia.media.mit.edu/~cwren/interpolator/
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# * http://stackoverflow.com/a/14178717/71522
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in_matrix = []
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for (x, y), (X, Y) in zip(src, dst):
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in_matrix.extend([
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[x, y, 1, 0, 0, 0, -X * x, -X * y],
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[0, 0, 0, x, y, 1, -Y * x, -Y * y],
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])
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A = np.matrix(in_matrix, dtype=np.float)
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B = np.array(dst).reshape(8)
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af = np.dot(np.linalg.inv(A.T * A) * A.T, B)
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m = np.append(np.array(af).reshape(8), 1).reshape((3, 3))
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logger.info("Created transformmatrix: src {} dst {} m {}".format( src, dst, m))
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return m
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# got this amazing thing from here: https://stackoverflow.com/a/24088499
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def create_perspective_transform(src, dst, round=False, splat_args=False):
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""" Returns a function which will transform points in quadrilateral
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``src`` to the corresponding points on quadrilateral ``dst``::
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... )
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>>> transform((5, 5))
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(74.99999999999639, 74.999999999999957)
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If ``round`` is ``True`` then points will be rounded to the nearest
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integer and integer values will be returned.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... round=True,
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... )
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>>> transform((5, 5))
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(75, 75)
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If ``splat_args`` is ``True`` the function will accept two arguments
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instead of a tuple.
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>>> transform = create_perspective_transform(
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... [(0, 0), (10, 0), (10, 10), (0, 10)],
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... [(50, 50), (100, 50), (100, 100), (50, 100)],
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... splat_args=True,
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... )
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>>> transform(5, 5)
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(74.99999999999639, 74.999999999999957)
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If the input values yield an invalid transformation matrix an identity
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function will be returned and the ``error`` attribute will be set to a
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description of the error::
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>>> tranform = create_perspective_transform(
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... np.zeros((4, 2)),
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... np.zeros((4, 2)),
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... )
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>>> transform((5, 5))
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(5.0, 5.0)
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>>> transform.error
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'invalid input quads (...): Singular matrix
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"""
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try:
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transform_matrix = create_perspective_transform_matrix(src, dst)
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error = None
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except np.linalg.LinAlgError as e:
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transform_matrix = np.identity(3, dtype=np.float)
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error = "invalid input quads (%s and %s): %s" %(src, dst, e)
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error = error.replace("\n", "")
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to_eval = "def perspective_transform(%s):\n" %(
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splat_args and "*pt" or "pt",
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)
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to_eval += " res = np.dot(transform_matrix, ((pt[0], ), (pt[1], ), (1, )))\n"
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to_eval += " res = res / res[2]\n"
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if round:
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to_eval += " return (int(round(res[0][0])), int(round(res[1][0])))\n"
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else:
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to_eval += " return (res[0][0], res[1][0])\n"
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locals = {
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"transform_matrix": transform_matrix,
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}
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locals.update(globals())
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exec to_eval in locals, locals
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res = locals["perspective_transform"]
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res.matrix = transform_matrix
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res.error = error
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return res
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def coordinatesToSrc(coordinates):
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return np.array([coordinates['tl'], coordinates['tr'],coordinates['bl'], coordinates['br']])
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# coordinates of the screen boundaries
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if os.path.exists("coordinates.p"):
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coordinates = pickle.load(open("coordinates.p", "rb"))
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transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
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a = [np.array([ 1312.15541183]), np.array([ 244.56278002]), 0]
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logger.info("Loaded coordinates: %s", coordinatesToSrc(coordinates))
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logger.debug("Corners: %s", screenDrawCorners)
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logger.debug("Test point %s", a)
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logger.info("Transformed point %s", transform(a[0:2]))
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# exit()
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else:
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coordinates = {'tl': None, 'tr': None, 'bl': None, 'br': None}
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transform = None
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if not args.hide_graph:
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windowRoot = Tk.Toplevel()
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windowSize = (1000,1000)
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windowRoot.geometry('%dx%d+%d+%d' % (windowSize[0],windowSize[1],0,0))
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figure = Figure(figsize=(16, 9), dpi=100)
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axes = figure.add_subplot(111)
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axes.set_title('Tk embedding')
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axes.set_xlabel('X axis label')
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axes.set_ylabel('Y label')
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# canvas = Tk.Canvas(windowRoot,width=1000,height=1000)
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canvas = FigureCanvasTkAgg(figure,master=windowRoot)
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canvas.draw()
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canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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imageWindowRoot = Tk.Toplevel()
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imageWindowSize = tuple(renderSize)
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imageWindowRoot.geometry('%dx%d+%d+%d' % (imageWindowSize[0],imageWindowSize[1],0,0))
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imageWindowRoot.attributes("-fullscreen", True)
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# imageCanvas is where the heatmap image is drawn
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imageCanvas = Tk.Canvas(imageWindowRoot,width=renderSize[0],height=renderSize[1])
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imageCanvas.pack()
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cv2.namedWindow("test", cv2.WND_PROP_FULLSCREEN)
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cv2.setWindowProperty("test", cv2.WND_PROP_FULLSCREEN, cv2.WINDOW_FULLSCREEN)
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if args.output_dir:
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startTime = time.time()
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lastSaveTime = startTime
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if args.queue_length:
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imageQueue = []
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lock = multiprocessing.Lock()
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photoQueue = multiprocessing.Queue(maxsize=args.processes)
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pointsQueue = multiprocessing.Queue(maxsize=args.processes)
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def captureFacesPoints(i):
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logger.info("Start capturer {}".format( i))
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# dedicated detector & predictor instances:
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detector = dlib.get_frontal_face_detector()
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predictor = dlib.shape_predictor(predictor_path)
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while True:
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t1 = time.time()
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im = photoQueue.get(block=True, timeout=10)
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if im is None:
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continue
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logger.debug("Got foto in {}".format( i))
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size = im.shape
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t2 = time.time()
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logger.debug("Captured frame in %fs", t2-t1)
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# Docs: Ask the detector to find the bounding boxes of each face. The 1 in the
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# second argument indicates that we should upsample the image 1 time. This
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# will make everything bigger and allow us to detect more faces.
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dets = detector(im, 1)
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t3 = time.time()
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logger.debug("Number of faces detected: {} - took {}s".format(len(dets), t3-t2))
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# We use this later for calibrating
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currentPoint = None
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currentVectors = None
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currentPoints = []
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if len(dets) > 0:
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for d in dets:
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td1 = time.time()
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shape = predictor(im, d)
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td2 = time.time()
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logger.debug("Found face points in %fs", td2-td1)
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#2D image points. If you change the image, you need to change vector
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image_points = np.array([
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(shape.part(30).x,shape.part(30).y), # Nose tip
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(shape.part(8).x,shape.part(8).y), # Chin
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(shape.part(36).x,shape.part(36).y), # Left eye left corner
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(shape.part(45).x,shape.part(45).y), # Right eye right corne
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(shape.part(48).x,shape.part(48).y), # Left Mouth corner
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(shape.part(54).x,shape.part(54).y) # Right mouth corner
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], dtype="double")
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# 3D model points.
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model_points = np.array([
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(0.0, 0.0, 0.0), # Nose tip
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(0.0, -330.0, -65.0), # Chin
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(-225.0, 170.0, -135.0), # Left eye left corner
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(225.0, 170.0, -135.0), # Right eye right corne
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(-150.0, -150.0, -125.0), # Left Mouth corner
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(150.0, -150.0, -125.0) # Right mouth corner
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])
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# Camera internals
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focal_length = size[1]
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center = (size[1]/2, size[0]/2)
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camera_matrix = np.array(
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[[focal_length, 0, center[0]],
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[0, focal_length, center[1]],
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[0, 0, 1]], dtype = "double"
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)
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# logger.info ("Camera Matrix :\n {0}".format(camera_matrix))
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dist_coeffs = np.zeros((4,1)) # Assuming no lens distortion
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(success, rotation_vector, translation_vector) = cv2.solvePnP(model_points, image_points, camera_matrix, dist_coeffs, flags=cv2.SOLVEPNP_ITERATIVE)
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if not success:
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logger.info("Error determening PnP {}".format(success) )
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continue
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logger.debug ("Rotation Vector:\n %s", rotation_vector)
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logger.debug ("Translation Vector:\n {0}".format(translation_vector))
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# Project a 3D point (0, 0, 1000.0) onto the image plane.
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# We use this to draw a line sticking out of the nose
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(nose_end_point2D, jacobian) = cv2.projectPoints(np.array([(0.0, 0.0, 1000.0)]), rotation_vector, translation_vector, camera_matrix, dist_coeffs)
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for p in image_points:
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# face points
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cv2.circle(im, (int(p[0]), int(p[1])), 3, (0,0,255), -1)
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p1 = ( int(image_points[0][0]), int(image_points[0][1]))
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p2 = ( int(nose_end_point2D[0][0][0]), int(nose_end_point2D[0][0][1]))
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cv2.line(im, p1, p2, (255,0,0), 2)
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rotMatrix = np.zeros([3,3])
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cv2.Rodrigues(rotation_vector, rotMatrix, jacobian=0)
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# Find rotation: https://stackoverflow.com/a/15029416
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# not used anymore :-)
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# rx = np.arctan2(rotMatrix[2,1], rotMatrix[2,2])
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# ry = np.arctan2(-rotMatrix[2,0], np.sqrt(np.square(rotMatrix[2,1]) + np.square(rotMatrix[2,2])))
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# rz = np.arctan2(rotMatrix[1,0],rotMatrix[0,0])
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# logger.info("rotation {} {} {}".format(rx, ry, rz) )
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# ry = - np.arcsin(rotMatrix[0,2])
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# rx = np.arctan2(rotMatrix[1,2]/np.cos(ry), rotMatrix[2,2]/np.cos(ry))
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# rz = np.arctan2(rotMatrix[0,1]/np.cos(ry), rotMatrix[0,0]/np.cos(ry))
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# logger.info("rotation ml {} {} {}".format(rx, ry, rz) )# seems better?
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viewDirectionVector = np.dot(np.array([0.0, 0.0, 100.0]), rotMatrix)
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if not args.hide_preview:
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# draw little floorplan for x: 10 -> 50 maps to z: 0 -> 10000, x: -2000 -> 2000
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mapPosX = int((translation_vector[0] + 500) / 1000 * 40)
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mapPosY = int((translation_vector[1] + 500) / 1000 * 40)
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mapPosZ = int((translation_vector[2] + 0 ) / 10000 * 40)
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cv2.circle(im, (mapPosZ + 10, mapPosX + 10), 2, (0,0,255), -1)
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cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
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# make it an _amazing_ stick figurine for the side view
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cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60, mapPosY + 20), (0,0,255), 1)
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cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 55, mapPosY + 25), (0,0,255), 1)
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cv2.line(im, (mapPosZ + 60, mapPosY + 20), (mapPosZ + 65, mapPosY + 25), (0,0,255), 1)
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cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 55, mapPosY + 10), (0,0,255), 1)
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cv2.line(im, (mapPosZ + 60, mapPosY + 15), (mapPosZ + 65, mapPosY + 10), (0,0,255), 1)
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# draw rotation vector
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cv2.circle(im, (mapPosZ + 60, mapPosY + 10), 2, (0,0,255), -1)
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cv2.line(im, (mapPosZ + 10, mapPosX + 10), (mapPosZ + 10 + int(viewDirectionVector[2] * 100), mapPosX + 10 + int(viewDirectionVector[0] * 100)), (255,255,0), 1)
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cv2.line(im, (mapPosZ + 60, mapPosY + 10), (mapPosZ + 60 + int(viewDirectionVector[2] * 100), mapPosY + 10 - int(viewDirectionVector[1] * 100)), (255,0,255), 1)
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# Translation vector gives position in space:
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# x, y z: 0,0,0 is center of camera
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# line: (x,y,z) = f(a) = (t1 + r1*a, t2+r2*a, t3 + r3*a)
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# Screen: (x,y,z) = (x,y,0)
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# Interesection:
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# x = t1 + r1 * a
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# y = t2 + r2 * a
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# z = t3 * r3 * a = 0
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# => a = -t3 / r3
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# substitute found a in x,y
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# seems to be wrong?
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# a = - translation_vector[2]# / rotation_vector[2]
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# x = translation_vector[0] + rotation_vector[0]* a
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# y = translation_vector[1] + rotation_vector[1] * a
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# logger.warn("First {} {},{}".format(a,x,y))
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a = translation_vector[2] / viewDirectionVector[2]
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x = translation_vector[0] + viewDirectionVector[0] * a
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y = translation_vector[1] + viewDirectionVector[1] * a
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|
# logger.warn("Second {} {},{}".format(a,x,y))
|
|
point = np.array([x,y])
|
|
|
|
currentPoint = point
|
|
currentVectors = {'translation': translation_vector, 'rotation': viewDirectionVector}
|
|
currentPoints.append(point)
|
|
|
|
td3 = time.time()
|
|
logger.debug("Timer: All other face drawing stuff in %fs", td3-td2)
|
|
|
|
# TODO only draw nose line now, so we can change color depending whether on screen or not
|
|
|
|
results = {'currentPoint': currentPoint, 'currentPoints': currentPoints, 'currentVectors': currentVectors}
|
|
results['im'] = im if not args.hide_preview else None
|
|
|
|
try:
|
|
pointsQueue.put_nowait(results)
|
|
except Queue.Full as e:
|
|
logger.warn("Result queue full?")
|
|
# not applicable to multiprocessing.queue in p2.7: photoQueue.task_done()
|
|
|
|
def captureVideo():
|
|
c = cv2.VideoCapture(args.camera)
|
|
# if not ding this we only have jittery 10fps
|
|
c.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(*"MJPG"))
|
|
|
|
# set camera resoltion
|
|
c.set(3, 1280)
|
|
c.set(4, 720)
|
|
# c.set(3, 960)
|
|
# c.set(4, 540)
|
|
logger.debug("Camera FPS: {}".format(c.get(5)))
|
|
|
|
while True:
|
|
_, im = c.read()
|
|
try:
|
|
photoQueue.put_nowait(im)
|
|
except Queue.Full as e:
|
|
logger.debug("Photo queue full")
|
|
time.sleep(.05)
|
|
logger.debug("Que sizes: image: {}, points: {} ".format(photoQueue.qsize(), pointsQueue.qsize()))
|
|
|
|
|
|
if __name__ == '__main__':
|
|
processes = []
|
|
for i in range(args.processes - 2):
|
|
p = multiprocessing.Process(target=captureFacesPoints, args=(i,))
|
|
p.daemon = True
|
|
p.start()
|
|
processes.append(p)
|
|
|
|
p = multiprocessing.Process(target=captureVideo, args=())
|
|
p.daemon = True
|
|
p.start()
|
|
processes.append(p)
|
|
|
|
newMetrics = np.zeros((metricsSize[1], metricsSize[0]))
|
|
lastRunTime = 0
|
|
|
|
while True:
|
|
result = None
|
|
te1 = time.time()
|
|
try:
|
|
result = pointsQueue.get()
|
|
te1b = time.time()
|
|
im = result['im']
|
|
currentPoint = result['currentPoint']
|
|
currentPoints = result['currentPoints']
|
|
currentVectors = result['currentVectors']
|
|
except Queue.Empty as e:
|
|
logger.warn('Result queue empty')
|
|
|
|
tr1 = time.time()
|
|
if result is not None:
|
|
if not args.hide_preview:
|
|
# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
|
|
cv2.rectangle(im, (9, 9), (51, 51), (255,255,255), 1)
|
|
cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1)
|
|
cv2.line(im, (10,10), (10,50), (200,200,200), 2)
|
|
cv2.line(im, (60,10), (60,50), (200,200,200), 2)
|
|
|
|
# screen is 16:10
|
|
cv2.rectangle(im, (9, 59), (91, 111), (255,255,255), 1)
|
|
|
|
if transform is None:
|
|
if not args.hide_preview:
|
|
cv2.putText(im, "1", (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tl'] is not None else (0,0,255))
|
|
cv2.putText(im, "2", (85,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tr'] is not None else (0,0,255))
|
|
cv2.putText(im, "3", (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['bl'] is not None else (0,0,255))
|
|
cv2.putText(im, "4", (85,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['br'] is not None else (0,0,255))
|
|
tm1 = 0
|
|
tm2 = 0
|
|
tm3 = 0
|
|
tm4 = 0
|
|
else:
|
|
for point in currentPoints:
|
|
# check if within coordinates:
|
|
# dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
|
|
# dot2 = np.dot(coordinates['bl'] - point, coordinates['tl'] - coordinates['br'])
|
|
# pointIn3 = [point[0], point[1], 0]
|
|
# targetPoint = np.dot(pointIn3, transformationMatrix)
|
|
# logger.info("Looking at", pointIn3, np.dot( transformationMatrix, pointIn3))
|
|
targetPoint = transform(point)
|
|
logger.info("Looking at {} {}".format(point, targetPoint) )
|
|
# cv2.circle(im, (int(targetPoint[0]), int(targetPoint[1])), 2, (0,255,0), -1)
|
|
# from 1920x1080 to 80x50
|
|
if not args.hide_preview:
|
|
miniTargetPoint = (int(targetPoint[0] / metricsSize[0] * 80 + 10), int(targetPoint[1] / metricsSize[1] * 50 + 60))
|
|
cv2.circle(im, miniTargetPoint, 2, (0,255,0), -1)
|
|
targetInt = (int(targetPoint[0]), int(targetPoint[1]))
|
|
# check if point fits on screen:
|
|
# if so, measure it
|
|
if targetInt[0]+spotSize[0] >= 0 and targetInt[1]+spotSize[1] >= 0 and targetInt[0]-spotSize[0] < metricsSize[0] and targetInt[1]-spotSize[1] < metricsSize[1]:
|
|
if not args.hide_graph:
|
|
dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True)
|
|
|
|
logger.info("Put metric {},{} in metrics of {},{}".format(targetInt[1],targetInt[0], metricsSize[1], metricsSize[0]))
|
|
# newMetrics[targetInt[1]-1,targetInt[0]-1] += 1
|
|
|
|
#TODO: make it one numpy array action:
|
|
for sx in range(spotSize[0]):
|
|
for sy in range(spotSize[1]):
|
|
mx = targetInt[0] + sx - (spotSize[0]-1)/2
|
|
my = targetInt[1] + sy - (spotSize[1]-1)/2
|
|
|
|
if mx >= 0 and my >= 0 and mx < metricsSize[0] and my < metricsSize[1]:
|
|
newMetrics[my,mx] += spot[sx,sy] #/ 20
|
|
|
|
|
|
# after we collected all new metrics, blur them foor smoothness
|
|
# and add to all metrics collected
|
|
tm3 = time.time()
|
|
# metrics = metrics + gaussian_filter(newMetrics, sigma = 13)
|
|
|
|
tm4 = time.time()
|
|
# logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
|
|
|
|
# Display webcam image with overlays
|
|
te2 = time.time()
|
|
if result is not None and not args.hide_preview:
|
|
cv2.imshow("Output", im)
|
|
te3 = time.time()
|
|
logger.debug("Pre processing took: {}s".format(te2-tr1))
|
|
logger.debug("showed webcam image in %fs", te3-te2)
|
|
logger.debug("Rendering took %fs", te3-te1)
|
|
logger.debug("Waited took %fs", te1b-te1)
|
|
|
|
|
|
# blur smooth the heatmap
|
|
# logger.debug("Max blurred metrics: %f", np.max(metrics))
|
|
|
|
# update the heatmap output
|
|
tm21 = time.time()
|
|
t = tm21
|
|
|
|
diffT = min(1, t - lastRunTime)
|
|
lastRunTime = t
|
|
# animDuration = 1
|
|
# factor = animDuration
|
|
|
|
metrics = metrics + newMetrics*diffT
|
|
newMetrics *= (1-diffT)
|
|
print('MAXES', np.max(metrics), np.max(newMetrics), diffT, t - lastRunTime)
|
|
|
|
# smooth impact of first hits by having at least 0.05
|
|
normalisedMetrics = metrics / (max(255*4 ,np.max(metrics)))
|
|
# convert to colormap, thanks to: https://stackoverflow.com/a/10967471
|
|
|
|
if args.only_metrics:
|
|
# output only metrics instead of heatmap. Usefull for debugging O:)
|
|
nmax = np.max(newMetrics)
|
|
renderMetrics = newMetrics/nmax if nmax > 0 else newMetrics
|
|
normalisedMetricsColored = np.uint8(renderMetrics *255 )
|
|
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_GRAY2BGR)
|
|
# draw grid lines
|
|
for i in range(int(metricsSize[0]/100)):
|
|
cv2.line(normalisedMetricsColoredBGR, (i*100, 0), (i*100, metricsSize[1]), (150,150,150), 1)
|
|
else:
|
|
normalisedMetricsColored = np.uint8(cm.nipy_spectral(normalisedMetrics)*255)
|
|
normalisedMetricsColoredBGR = cv2.cvtColor(normalisedMetricsColored, cv2.COLOR_RGB2BGR)
|
|
|
|
|
|
if currentPoint is not None and args.verbose:
|
|
cv2.putText(normalisedMetricsColoredBGR, "x: {}".format(currentPoint[0]), (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
|
|
cv2.putText(normalisedMetricsColoredBGR, "y: {}".format(currentPoint[1]), (10,90), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
|
|
|
|
cv2.putText(normalisedMetricsColoredBGR, "pos: x: {}, y: {}, z: {}".format(
|
|
currentVectors['translation'][0],
|
|
currentVectors['translation'][1],
|
|
currentVectors['translation'][2]
|
|
), (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
|
|
cv2.putText(
|
|
normalisedMetricsColoredBGR,
|
|
"rot: x: {}, y: {}, z{}".format(
|
|
currentVectors['rotation'][0],
|
|
currentVectors['rotation'][1],
|
|
currentVectors['rotation'][2],
|
|
), (10,130), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
|
|
targetPoint = transform(currentPoint)
|
|
logger.info("Are we really looking at {}".format(targetPoint))
|
|
logger.info("Size: {}".format(normalisedMetricsColoredBGR.shape))
|
|
cv2.circle(normalisedMetricsColoredBGR, targetPoint, 2, (0,255,0), -1)
|
|
cv2.line(
|
|
normalisedMetricsColoredBGR,
|
|
(metricsSize[0]/2,metricsSize[1]/2),
|
|
tuple(targetPoint),
|
|
(255,0,0), 2
|
|
)
|
|
|
|
# cv2.putText(normalisedMetricsColoredBGR, "z: {}".format(currentPoint[2]), (10,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255))
|
|
|
|
tm22 = time.time()
|
|
logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
|
|
# logger.info(normalisedMetrics)
|
|
tm23 = time.time()
|
|
|
|
# normalisedMetricsColoredBGR = cv2.resize(normalisedMetricsColoredBGR, tuple(renderSize))
|
|
if backdrop is not None:
|
|
dx = (screenSize[0] - renderSize[0]) / 2
|
|
dy = (screenSize[1] - renderSize[1]) / 2
|
|
print(dx, dy)
|
|
backdrop[dy:dy+renderSize[1], dx:dx+renderSize[0]] = normalisedMetricsColoredBGR
|
|
renderImage = backdrop
|
|
else:
|
|
renderImage = normalisedMetricsColoredBGR
|
|
cv2.imshow("test", renderImage)
|
|
# image = Image.fromarray(normalisedMetricsColored)
|
|
# wpercent = (imageWindowSize[0] / float(image.size[0]))
|
|
# hsize = int((float(image.size[1]) * float(wpercent)))
|
|
# renderImage = image.resize((renderSize[0], renderSize[1]))
|
|
# print(renderImage.size, "lala")
|
|
|
|
# if args.queue_length:
|
|
# imageQueue.append(image)
|
|
# if len(imageQueue) > args.queue_length:
|
|
# logger.warn("Use image from queue :-)")
|
|
# image = imageQueue.pop(0)
|
|
|
|
# tkpi = ImageTk.PhotoImage(renderImage)
|
|
# imageCanvas.delete("IMG")
|
|
# imagesprite = imageCanvas.create_image(renderSize[0]/2, renderSize[1]/2,image=tkpi, tags="IMG")
|
|
# imageWindowRoot.update()
|
|
tm24 = time.time()
|
|
logger.debug("Render in generated in %fs", tm24 - tm23)
|
|
# logger.debug("Total matrix time is %fs", tm4 - tm3 + tm2 - tm1 + tm24 - tm21)
|
|
|
|
if not args.hide_graph:
|
|
te4 = time.time()
|
|
axes.clear()
|
|
if(len(dataframe) > 2):
|
|
g = sns.kdeplot(dataframe['x'], dataframe['y'],ax=axes, n_levels=30, shade=True, cmap=cm.rainbow)
|
|
canvas.draw()
|
|
windowRoot.update()
|
|
te5 = time.time()
|
|
logger.debug("Drew graph & updated window in %fs", te5-te4)
|
|
|
|
if args.output_dir:
|
|
# save output to dir
|
|
now = tm24 # time.time()
|
|
if now - lastSaveTime > args.save_interval:
|
|
filename = os.path.join(
|
|
args.output_dir,
|
|
"frame{}.png".format(
|
|
datetime.datetime.now().replace(microsecond=0).isoformat()
|
|
)
|
|
)
|
|
cv2.imwrite(filename, normalisedMetricsColoredBGR)
|
|
# image.save(filename)
|
|
|
|
with open(lastMetricsFilename, 'wb') as fp:
|
|
pickle.dump( metrics, fp )
|
|
|
|
logger.debug("Saved frame to {}".format(filename))
|
|
lastSaveTime = now
|
|
|
|
# (optionally) very slowly fade out previous metrics:
|
|
# Don't fade for now: metrics = metrics * .9997
|
|
|
|
keyPress = cv2.waitKey(5)
|
|
|
|
if keyPress==27:
|
|
break
|
|
elif keyPress == ord('d'):
|
|
logger.setLevel(logging.DEBUG)
|
|
elif keyPress > -1 and currentPoint is not None:
|
|
recalculate = False
|
|
if keyPress == ord('1'):
|
|
coordinates['tl'] = currentPoint
|
|
recalculate = True
|
|
logger.warn('Calibrate 1')
|
|
elif keyPress == ord('2'):
|
|
coordinates['tr'] = currentPoint
|
|
recalculate = True
|
|
logger.warn('Calibrate 2')
|
|
elif keyPress == ord('3'):
|
|
coordinates['bl'] = currentPoint
|
|
recalculate = True
|
|
logger.warn('Calibrate 3')
|
|
elif keyPress == ord('4'):
|
|
coordinates['br'] = currentPoint
|
|
recalculate = True
|
|
logger.warn('Calibrate 4')
|
|
elif keyPress == ord('t') and transform is not None:
|
|
logger.info("Coordinates {}".format(coordinates) )
|
|
logger.info("Drawing area {}".format(screenDrawCorners))
|
|
logger.info("Test point {}".format(currentPoint ))
|
|
logger.info("Transformed point {}".format(transform(currentPoint)))
|
|
|
|
if recalculate is True and not any (x is None for x in coordinates.values()):
|
|
logger.debug(coordinates.values())
|
|
pickle.dump( coordinates, open( "coordinates.p", "wb" ) )
|
|
logger.info("Saved coordinates")
|
|
|
|
transform = create_perspective_transform(coordinatesToSrc(coordinates), screenDrawCorners, True)
|
|
|
|
duration = time.time()-te1
|
|
fps = 1/duration
|
|
logger.info("Rendering loop %fs %ffps", duration, fps)
|
|
|
|
cv2.destroyAllWindows()
|