Test Seaborn graph, and add timings

head_pose.py

@@ -7,9 +7,19 @@ import os
 import pickle
 import logging
 from scipy.ndimage.filters import gaussian_filter
-import Tkinter
 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' )
 logger = logging.getLogger(__name__)
 
@@ -27,6 +37,7 @@ screenDrawCorners = np.array([[10,60], [90, 60], [10, 110], [90, 110]])
 
 # metrics matrix
 metricsSize = [1920,1080]
+dataframe = pd.DataFrame(columns=['x','y'])
 metrics = np.zeros(metricsSize)
 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}
     transform = None
 
-windowRoot = Tkinter.Tk()
+windowRoot = Tk.Toplevel()
 windowSize = (1000,1000)
 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:
+    t1 = time.time()
     _, im = c.read()
     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
     # second argument indicates that we should upsample the image 1 time. This
     # will make everything bigger and allow us to detect more faces.
     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
     currentPoint = None
@@ -170,7 +196,10 @@ while True:
     if len(dets) > 0:
 
         for d in dets:
+            td1 = time.time()
             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
             image_points = np.array([
@@ -280,10 +309,13 @@ while True:
             currentPoint = 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
     
     # processed all faces, now draw on screen:
-
+    te1 = time.time()
     # 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)
@@ -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, "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:
+        tm1 = time.time()
         newMetrics = np.zeros(metricsSize)
+        tm2 = time.time()
         for point in currentPoints:
             # check if within coordinates:
             # dot1 = np.dot(coordinates['tl'] - point, coordinates['tl'] - coordinates['br'])
@@ -316,33 +354,53 @@ while True:
             targetInt = (int(targetPoint[0]), int(targetPoint[1]))
             # check if point fits on screen:
             # 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
         # 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 = 8)
+        tm4 = time.time()
+        logger.debug("Updated matrix with blur in %f", tm4 - tm3 + tm2 - tm1)
 
     # Display webcam image with overlays
+    te2 = time.time()
+    logger.debug("Drew on screen in %fs", te2-te1)
     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
-    logger.debug("Max blurred metrics: %f", np.max(metrics))
+    # logger.debug("Max blurred metrics: %f", np.max(metrics))
 
     # update the heatmap output
+    tm21 = time.time()
     normalisedMetrics = metrics / (np.max(metrics)/255)
+    tm22 = time.time()
     logger.debug("Max normalised metrics: %f", np.max(normalisedMetrics))
-    print(normalisedMetrics)
+    # print(normalisedMetrics)
+    tm23 = time.time()
     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)))
-    image = image.resize((windowSize[0], hsize))
+    image = image.resize((imageWindowSize[0], hsize))
-    
     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()
-    logger.debug("updated window")
+    te5 = time.time()
+    logger.debug("Drew graph & updated window in %fs", te5-te4)
 
     # (optionally) very slowly fade out previous metrics:
     # metrics = metrics * .999

test.py

@@ -1,6 +1,18 @@
 import helpers
 import numpy as np
 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]])
 coordinates = pickle.load(open("coordinates.p", "rb"))
@@ -17,4 +29,47 @@ print("Transformed point %s", transform(a))
 print("Test point %s", midpointTop )
 print("Transformed point %s", transform(midpointTop))
 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()