add new bise cone work

lib/bisection.py

 import numpy as np
 import numpy.linalg as la
+from shapely.geometry import Polygon
+import itertools
+
+def polyToArr(poly):
+    if poly is not None and poly.exterior is not None:
+        return np.array(poly.exterior.coords.xy).transpose().astype(int)
+    else:
+        return None
 
 def getAngle(a,b,c):
     ba = a - b
@@ -11,7 +19,6 @@ def getAngle(a,b,c):
 def getBisecPoint(a,b,c):
     angle = getAngle(a,b,c)
     if(a[0]-b[0]<0): #check wich direction (left/right) the vector should point
-    # if(angle < 180):
         angle = 360 - angle
     else:
         angle = 180 - angle
@@ -21,6 +28,20 @@ def getBisecPoint(a,b,c):
     d = (int(dist * np.cos(angle)), int(dist * np.sin(angle))) #with origin zero
     return (d[0]+b[0],d[1]+b[1]) #with origin b
 
+def getBisecCone(a,b,c,width):
+    angle = getAngle(a,b,c)
+    if(a[0]-b[0]<0): #check wich direction (left/right) the vector should point
+        angle = 360 - angle
+    else:
+        angle = 180 - angle
+    angle = angle / 2
+    #dist = la.norm(a-b)
+    dist = 1500
+    # dist = 100
+    conePoint1 = (int(dist * np.cos(angle-(width/2))), int(dist * np.sin(angle-(width/2)))) #with origin zero
+    conePoint2 = (int(dist * np.cos(angle+(width/2))), int(dist * np.sin(angle+(width/2)))) #with origin zero
+    return ((conePoint1[0]+b[0],conePoint1[1]+b[1]),(conePoint2[0]+b[0],conePoint2[1]+b[1]))
+
 def poseToBisectVector(pose):
     points = pose[[0,1,8]]
     if(0.0 in points[:,2:]): #if one point has confidence zero, we can not generate the vector
@@ -28,4 +49,28 @@ def poseToBisectVector(pose):
     a,b,c = points[:,:2] # cut of confidence score so we have normal coordinate points
     bisecPoint = getBisecPoint(a,b,c)
     return np.array([bisecPoint,b])
+    #return Polygon([bisecPoint,b])
+
+def poseToBisectCone(pose, angle):
+    width = np.deg2rad(angle)
+    points = pose[[0,1,8]]
+    if(0.0 in points[:,2:]): #if one point has confidence zero, we can not generate the vector
+        return None
+    a,b,c = points[:,:2] # cut of confidence score so we have normal coordinate points
+    conePoint1, conePoint2 = getBisecCone(a,b,c,width)
+    #return np.array([conePoint1, conePoint2, b])
+    return Polygon([conePoint1, conePoint2, b])
 
+def coneIntersections(bisecCones):
+    out = {}
+    for r in range(2,len(bisecCones)):
+        pc = list(itertools.combinations(range(0,len(bisecCones)),r))
+        #print(pc)
+        for combi in pc:
+            # print(combi)
+            intersect = bisecCones[combi[0]]
+            for i in combi[1:]:
+                intersect = intersect.intersection(bisecCones[i])
+            if not intersect.is_empty:
+                out[combi] = intersect
+    return out
\ No newline at end of file

playground/brg_means.py

+import cv2
+import numpy as np
+import os
+
+IN_DIR = "../images/images_imdahl/"
+images = [os.path.join(os.getcwd(), IN_DIR, f) for f in os.listdir(IN_DIR)]
+
+means = np.array([[0,0,0]]*len(images))
+i=0
+for img_name in images:
+    img = cv2.imread(img_name)
+    if img is None:
+        continue
+#    print(img_name)
+    r = img[:,:,0].flatten()
+    g = img[:,:,1].flatten()
+    b = img[:,:,2].flatten()
+
+    rm = r.sum()/len(r)
+    gm = g.sum()/len(g)
+    bm = b.sum()/len(b)
+    means[i][0] = rm
+    means[i][1] = gm
+    means[i][2] = bm
+    i+=1;
+    print(rm,gm,bm)
+
+print("global mean",means)
+print("r",means[:,0].sum()/len(means));
+print("g",means[:,1].sum()/len(means));
+print("b",means[:,2].sum()/len(means));
\ No newline at end of file

playground/threshold_slider.py

@@ -20,17 +20,17 @@ def auto_canny(image, sigma=0.33):
 
 #src1 = cv.imread('/Users/Tilman/Documents/Programme/Python/forschungspraktikum/openpose/examples/media/art/28165_gw.jpg')
 #src2 = cv.imread('/Users/Tilman/Documents/Programme/Python/forschungspraktikum/openpose/examples/media/art/38172_gw.jpg')
-image = cv.imread('../images/first_email/page_5_item_17_annunci1.jpg')
+image = cv.imread('../images/out/fuse_1_bsds500/0.png')
 gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
 blurred = cv.GaussianBlur(gray, (3, 3), 0)
 
 auto = auto_canny(blurred)
 
 rho=3
-theta=360
+theta=4
 threshold=100
-minline=100
-maxgap=10
+minline=220
+maxgap=30
 
 def on_rho(val):
     global rho
@@ -53,7 +53,7 @@ def on_maxgap(val):
     maxgap=val
     update()
 def update():
-    image = cv.imread('../images/first_email/page_5_item_17_annunci1.jpg')
+    image = cv.imread('../images/out/fuse_1_bsds500/1.png')
     lines = cv.HoughLinesP(image=auto, rho=rho, theta=np.pi/theta, threshold=threshold, minLineLength=minline, maxLineGap=maxgap)
     # if(lines):
     if lines is not None: