Test Seaborn graph, and add timings

This commit is contained in:
Ruben 2018-04-29 15:09:15 +02:00
parent 1b049b9e5a
commit da7f57185b
2 changed files with 132 additions and 19 deletions

View File

@ -7,9 +7,19 @@ import os
import pickle import pickle
import logging import logging
from scipy.ndimage.filters import gaussian_filter from scipy.ndimage.filters import gaussian_filter
import Tkinter
from PIL import Image, ImageDraw,ImageTk from PIL import Image, ImageDraw,ImageTk
import pandas as pd
import seaborn as sns
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
import sys
if sys.version_info[0] < 3:
import Tkinter as Tk
else:
import tkinter as Tk
import time
logging.basicConfig( format='%(asctime)-15s %(name)s %(levelname)s: %(message)s' ) logging.basicConfig( format='%(asctime)-15s %(name)s %(levelname)s: %(message)s' )
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -27,6 +37,7 @@ screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
# metrics matrix # metrics matrix
metricsSize = [1920,1080] metricsSize = [1920,1080]
dataframe = pd.DataFrame(columns=['x','y'])
metrics = np.zeros(metricsSize) metrics = np.zeros(metricsSize)
screenDrawCorners = np.array([[0,0], [1919,0], [0, 1079], [1919,1079]]) screenDrawCorners = np.array([[0,0], [1919,0], [0, 1079], [1919,1079]])
@ -146,22 +157,37 @@ else:
coordinates = {'tl': None, 'tr': None, 'bl': None, 'br': None} coordinates = {'tl': None, 'tr': None, 'bl': None, 'br': None}
transform = None transform = None
windowRoot = Tkinter.Tk() windowRoot = Tk.Toplevel()
windowSize = (1000,1000) windowSize = (1000,1000)
windowRoot.geometry('%dx%d+%d+%d' % (windowSize[0],windowSize[1],0,0)) windowRoot.geometry('%dx%d+%d+%d' % (windowSize[0],windowSize[1],0,0))
canvas = Tkinter.Canvas(windowRoot,width=1000,height=1000) figure = Figure(figsize=(16, 9), dpi=100)
canvas.pack() axes = figure.add_subplot(111)
axes.set_title('Tk embedding')
axes.set_xlabel('X axis label')
axes.set_ylabel('Y label')
# canvas = Tk.Canvas(windowRoot,width=1000,height=1000)
canvas = FigureCanvasTkAgg(figure,master=windowRoot)
canvas.show()
canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
imageWindowRoot = Tk.Toplevel()
imageWindowSize = (1000,1000)
imageWindowRoot.geometry('%dx%d+%d+%d' % (imageWindowSize[0],imageWindowSize[1],0,0))
imageCanvas = Tk.Canvas(imageWindowRoot,width=1000,height=1000)
while True: while True:
t1 = time.time()
_, im = c.read() _, im = c.read()
size = im.shape size = im.shape
t2 = time.time()
logger.debug("Captured frame in %fs", t2-t1)
# Docs: Ask the detector to find the bounding boxes of each face. The 1 in the # Docs: Ask the detector to find the bounding boxes of each face. The 1 in the
# second argument indicates that we should upsample the image 1 time. This # second argument indicates that we should upsample the image 1 time. This
# will make everything bigger and allow us to detect more faces. # will make everything bigger and allow us to detect more faces.
dets = detector(im, 1) dets = detector(im, 1)
t3 = time.time()
logger.debug("Number of faces detected: {}".format(len(dets))) logger.debug("Number of faces detected: {} - took {}s".format(len(dets), t3-t2))
# We use this later for calibrating # We use this later for calibrating
currentPoint = None currentPoint = None
@ -170,7 +196,10 @@ while True:
if len(dets) > 0: if len(dets) > 0:
for d in dets: for d in dets:
td1 = time.time()
shape = predictor(im, d) shape = predictor(im, d)
td2 = time.time()
logger.debug("Found face points in %fs", td2-td1)
#2D image points. If you change the image, you need to change vector #2D image points. If you change the image, you need to change vector
image_points = np.array([ image_points = np.array([
@ -280,10 +309,13 @@ while True:
currentPoint = point currentPoint = point
currentPoints.append(point) 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 # TODO only draw nose line now, so we can change color depending whether on screen or not
# processed all faces, now draw on screen: # processed all faces, now draw on screen:
te1 = time.time()
# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px) # 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, (9, 9), (51, 51), (255,255,255), 1)
cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1) cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1)
@ -298,8 +330,14 @@ while True:
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, "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, "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)) 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: else:
tm1 = time.time()
newMetrics = np.zeros(metricsSize) newMetrics = np.zeros(metricsSize)
tm2 = time.time()
for point in currentPoints: for point in currentPoints:
# check if within coordinates: # check if within coordinates:
# dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br']) # dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
@ -316,33 +354,53 @@ while True:
targetInt = (int(targetPoint[0]), int(targetPoint[1])) targetInt = (int(targetPoint[0]), int(targetPoint[1]))
# check if point fits on screen: # check if point fits on screen:
# if so, measure it # if so, measure it
if targetInt[0] >= 0 and targetInt[1] >= 0 and targetInt[0] < metrics.shape[0] and targetInt[1] < metrics.shape[1]: if targetInt[0] >= 0 and targetInt[1] >= 0 and targetInt[0] < metricsSize[0] and targetInt[1] < metricsSize[1]:
dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True)
newMetrics[targetInt[0],targetInt[1]] += 1 newMetrics[targetInt[0],targetInt[1]] += 1
# after we collected all new metrics, blur them foor smoothness # after we collected all new metrics, blur them foor smoothness
# and add to all metrics collected # and add to all metrics collected
tm3 = time.time()
metrics = metrics + gaussian_filter(newMetrics, sigma = 8) metrics = metrics + gaussian_filter(newMetrics, sigma = 8)
tm4 = time.time()
logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
# Display webcam image with overlays # Display webcam image with overlays
te2 = time.time()
logger.debug("Drew on screen in %fs", te2-te1)
cv2.imshow("Output", im) cv2.imshow("Output", im)
logger.debug("showed webcam image") te3 = time.time()
logger.debug("showed webcam image in %fs", te3-te2)
# blur smooth the heatmap # blur smooth the heatmap
logger.debug("Max blurred metrics: %f", np.max(metrics)) # logger.debug("Max blurred metrics: %f", np.max(metrics))
# update the heatmap output # update the heatmap output
tm21 = time.time()
normalisedMetrics = metrics / (np.max(metrics)/255) normalisedMetrics = metrics / (np.max(metrics)/255)
tm22 = time.time()
logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics)) logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
print(normalisedMetrics) # print(normalisedMetrics)
tm23 = time.time()
image = Image.fromarray(normalisedMetrics) image = Image.fromarray(normalisedMetrics)
wpercent = (windowSize[0] / float(image.size[0])) wpercent = (imageWindowSize[0] / float(image.size[0]))
hsize = int((float(image.size[1]) * float(wpercent))) hsize = int((float(image.size[1]) * float(wpercent)))
image = image.resize((windowSize[0], hsize)) image = image.resize((imageWindowSize[0], hsize))
tkpi = ImageTk.PhotoImage(image) tkpi = ImageTk.PhotoImage(image)
canvas.delete("IMG") imageCanvas.delete("IMG")
imagesprite = canvas.create_image(500,500,image=tkpi, tags="IMG") imagesprite = imageCanvas.create_image(500,500,image=tkpi, tags="IMG")
imageWindowRoot.update()
tm24 = time.time()
logger.debug("PIL iamge generated in %fs", tm24 - tm23)
logger.debug("Total matrix time is %fs", tm4 - tm3 + tm2 - tm1 + tm24 - tm21)
te4 = time.time()
axes.clear()
if(len(dataframe) > 2):
g = sns.kdeplot(dataframe['x'], dataframe['y'],ax=axes, n_levels=30, shade=True, cmap="rainbow")
canvas.draw()
windowRoot.update() windowRoot.update()
logger.debug("updated window") te5 = time.time()
logger.debug("Drew graph & updated window in %fs", te5-te4)
# (optionally) very slowly fade out previous metrics: # (optionally) very slowly fade out previous metrics:
# metrics = metrics * .999 # metrics = metrics * .999

57
test.py
View File

@ -1,6 +1,18 @@
import helpers import helpers
import numpy as np import numpy as np
import pickle import pickle
import random
from PIL import Image, ImageDraw,ImageTk
import pandas as pd
import seaborn as sns
import time
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
import sys
if sys.version_info[0] < 3:
import Tkinter as Tk
else:
import tkinter as Tk
screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]]) screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
coordinates = pickle.load(open("coordinates.p", "rb")) coordinates = pickle.load(open("coordinates.p", "rb"))
@ -17,4 +29,47 @@ print("Transformed point %s", transform(a))
print("Test point %s", midpointTop ) print("Test point %s", midpointTop )
print("Transformed point %s", transform(midpointTop)) print("Transformed point %s", transform(midpointTop))
print("Test point %s", midpointCenter ) print("Test point %s", midpointCenter )
print("Transformed point %s", transform(midpointCenter)) print("Transformed point %s", transform(midpointCenter))
windowRoot = Tk.Tk()
windowSize = (1000,1000)
windowRoot.geometry('%dx%d+%d+%d' % (windowSize[0],windowSize[1],0,0))
figure = Figure(figsize=(16, 9), dpi=100)
t = np.arange(0.0, 3.0, 0.01)
s = np.sin(2*np.pi*t)
d = {'col1': [1, 2,4], 'col2': [3, 4,9]}
dataframe = pd.DataFrame(data=d)
print(dataframe['col1'])
a = figure.add_subplot(111)
# a = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde")
# g = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde")
# g = sns.JointGrid(x="col1", y="col2", data=dataframe)
a.set_title('Tk embedding')
a.set_xlabel('X axis label')
a.set_ylabel('Y label')
# canvas = Tk.Canvas(windowRoot,width=1000,height=1000)
canvas = FigureCanvasTkAgg(figure,master=windowRoot)
canvas.show()
canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
for b in range(0,1000):
dataframe = dataframe.append({'col1':b,'col2':random.random()*100}, ignore_index=True)
a.clear()
# g.fig =
# a.plot(t*b, s)
# g = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde", size=1000)
# g = sns.kdeplot(dataframe['col1'], dataframe['col2'],ax=a, n_levels=30, shade=True, cmap="hsv")
g = sns.kdeplot(dataframe['col1'], dataframe['col2'],ax=a, n_levels=30, shade=True, cmap="rainbow")
# print(g, g.fig)
# g = g.plot_joint(sns.kdeplot, cmap="Blues_d")
# print(g.fig)
# canvas.figure = g.figure
canvas.draw()
windowRoot.update()
time.sleep(1)
# Tk.mainloop()