Test Seaborn graph, and add timings
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Ruben
parent
1b049b9e5a
commit
da7f57185b
2 changed files with 132 additions and 19 deletions
94
head_pose.py
94
head_pose.py
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@ -7,9 +7,19 @@ 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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import Tkinter
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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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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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logging.basicConfig( format='%(asctime)-15s %(name)s %(levelname)s: %(message)s' )
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logger = logging.getLogger(__name__)
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@ -27,6 +37,7 @@ 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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dataframe = pd.DataFrame(columns=['x','y'])
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metrics = np.zeros(metricsSize)
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screenDrawCorners = np.array([[0,0], [1919,0], [0, 1079], [1919,1079]])
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@ -146,22 +157,37 @@ else:
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coordinates = {'tl': None, 'tr': None, 'bl': None, 'br': None}
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transform = None
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windowRoot = Tkinter.Tk()
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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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canvas = Tkinter.Canvas(windowRoot,width=1000,height=1000)
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canvas.pack()
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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.show()
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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 = (1000,1000)
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imageWindowRoot.geometry('%dx%d+%d+%d' % (imageWindowSize[0],imageWindowSize[1],0,0))
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imageCanvas = Tk.Canvas(imageWindowRoot,width=1000,height=1000)
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while True:
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t1 = time.time()
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_, im = c.read()
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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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logger.debug("Number of faces detected: {}".format(len(dets)))
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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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@ -170,7 +196,10 @@ while True:
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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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@ -280,10 +309,13 @@ while True:
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currentPoint = point
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currentPoints.append(point)
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td3 = time.time()
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logger.debug("Timer: All other face drawing stuff in %fs", td3-td2)
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# TODO only draw nose line now, so we can change color depending whether on screen or not
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# processed all faces, now draw on screen:
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te1 = time.time()
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# draw little floorplan for 10 -> 50, sideplan 60 -> 100 (40x40 px)
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cv2.rectangle(im, (9, 9), (51, 51), (255,255,255), 1)
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cv2.rectangle(im, (59, 9), (101, 51), (255,255,255), 1)
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@ -298,8 +330,14 @@ while True:
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cv2.putText(im, "2", (85,70), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['tr'] is not None else (0,0,255))
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cv2.putText(im, "3", (10,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['bl'] is not None else (0,0,255))
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cv2.putText(im, "4", (85,110), cv2.FONT_HERSHEY_PLAIN, .7, (255,255,255) if coordinates['br'] is not None else (0,0,255))
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tm1 = 0
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tm2 = 0
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tm3 = 0
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tm4 = 0
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else:
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tm1 = time.time()
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newMetrics = np.zeros(metricsSize)
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tm2 = time.time()
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for point in currentPoints:
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# check if within coordinates:
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# dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
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@ -316,33 +354,53 @@ while True:
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targetInt = (int(targetPoint[0]), int(targetPoint[1]))
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# check if point fits on screen:
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# if so, measure it
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if targetInt[0] >= 0 and targetInt[1] >= 0 and targetInt[0] < metrics.shape[0] and targetInt[1] < metrics.shape[1]:
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if targetInt[0] >= 0 and targetInt[1] >= 0 and targetInt[0] < metricsSize[0] and targetInt[1] < metricsSize[1]:
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dataframe = dataframe.append({'x':targetInt[0],'y':targetInt[1]}, ignore_index=True)
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newMetrics[targetInt[0],targetInt[1]] += 1
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# after we collected all new metrics, blur them foor smoothness
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# and add to all metrics collected
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tm3 = time.time()
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metrics = metrics + gaussian_filter(newMetrics, sigma = 8)
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tm4 = time.time()
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logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
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# Display webcam image with overlays
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te2 = time.time()
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logger.debug("Drew on screen in %fs", te2-te1)
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cv2.imshow("Output", im)
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logger.debug("showed webcam image")
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te3 = time.time()
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logger.debug("showed webcam image in %fs", te3-te2)
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# blur smooth the heatmap
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logger.debug("Max blurred metrics: %f", np.max(metrics))
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# logger.debug("Max blurred metrics: %f", np.max(metrics))
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# update the heatmap output
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tm21 = time.time()
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normalisedMetrics = metrics / (np.max(metrics)/255)
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tm22 = time.time()
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logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
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print(normalisedMetrics)
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# print(normalisedMetrics)
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tm23 = time.time()
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image = Image.fromarray(normalisedMetrics)
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wpercent = (windowSize[0] / float(image.size[0]))
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wpercent = (imageWindowSize[0] / float(image.size[0]))
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hsize = int((float(image.size[1]) * float(wpercent)))
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image = image.resize((windowSize[0], hsize))
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image = image.resize((imageWindowSize[0], hsize))
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tkpi = ImageTk.PhotoImage(image)
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canvas.delete("IMG")
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imagesprite = canvas.create_image(500,500,image=tkpi, tags="IMG")
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imageCanvas.delete("IMG")
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imagesprite = imageCanvas.create_image(500,500,image=tkpi, tags="IMG")
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imageWindowRoot.update()
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tm24 = time.time()
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logger.debug("PIL iamge generated in %fs", tm24 - tm23)
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logger.debug("Total matrix time is %fs", tm4 - tm3 + tm2 - tm1 + tm24 - tm21)
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te4 = time.time()
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axes.clear()
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if(len(dataframe) > 2):
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g = sns.kdeplot(dataframe['x'], dataframe['y'],ax=axes, n_levels=30, shade=True, cmap="rainbow")
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canvas.draw()
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windowRoot.update()
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logger.debug("updated window")
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te5 = time.time()
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logger.debug("Drew graph & updated window in %fs", te5-te4)
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# (optionally) very slowly fade out previous metrics:
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# metrics = metrics * .999
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57
test.py
57
test.py
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@ -1,6 +1,18 @@
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import helpers
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import numpy as np
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import pickle
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import random
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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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import time
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
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from matplotlib.figure import Figure
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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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screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
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coordinates = pickle.load(open("coordinates.p", "rb"))
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@ -17,4 +29,47 @@ print("Transformed point %s", transform(a))
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print("Test point %s", midpointTop )
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print("Transformed point %s", transform(midpointTop))
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print("Test point %s", midpointCenter )
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print("Transformed point %s", transform(midpointCenter))
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print("Transformed point %s", transform(midpointCenter))
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windowRoot = Tk.Tk()
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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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t = np.arange(0.0, 3.0, 0.01)
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s = np.sin(2*np.pi*t)
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d = {'col1': [1, 2,4], 'col2': [3, 4,9]}
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dataframe = pd.DataFrame(data=d)
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print(dataframe['col1'])
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a = figure.add_subplot(111)
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# a = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde")
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# g = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde")
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# g = sns.JointGrid(x="col1", y="col2", data=dataframe)
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a.set_title('Tk embedding')
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a.set_xlabel('X axis label')
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a.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.show()
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canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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for b in range(0,1000):
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dataframe = dataframe.append({'col1':b,'col2':random.random()*100}, ignore_index=True)
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a.clear()
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# g.fig =
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# a.plot(t*b, s)
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# g = sns.jointplot(x="col1", y="col2", data=dataframe, kind="kde", size=1000)
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# g = sns.kdeplot(dataframe['col1'], dataframe['col2'],ax=a, n_levels=30, shade=True, cmap="hsv")
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g = sns.kdeplot(dataframe['col1'], dataframe['col2'],ax=a, n_levels=30, shade=True, cmap="rainbow")
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# print(g, g.fig)
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# g = g.plot_joint(sns.kdeplot, cmap="Blues_d")
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# print(g.fig)
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# canvas.figure = g.figure
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canvas.draw()
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windowRoot.update()
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time.sleep(1)
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# Tk.mainloop()
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