add bdcn res to detect structures

detect_structures.py

@@ -27,6 +27,7 @@ DISPLAY_RASTER_ELEMENTS = 500
 OUT_DIR = 'images/out/images_imdahl/main_structure_global_line'
 #IN_DIR = "images/first_email/"     # images from first email
 IN_DIR = "images/images_imdahl/"    # images from imdahl
+IN_DIR_BDCN = "images/out/images_imdahl/bdcn_output/"
 
 SKIP_OPENPOSE = False
 OPENPOSE_DEMO_KEYPOINTS = np.array([[[4.7613028e+02, 3.3695804e+02, 9.0203685e-01],[5.3667474e+02, 3.8633786e+02, 6.6615295e-01],[5.1645105e+02, 3.8405157e+02, 5.1514143e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[5.5459924e+02, 3.8859457e+02, 6.4240879e-01],[5.6353766e+02, 4.7384988e+02, 1.8810490e-01],[5.3886292e+02, 5.2543573e+02, 9.0144195e-02],[5.4566248e+02, 5.3215259e+02, 3.6083767e-01],[5.2768524e+02, 5.3213129e+02, 3.1196830e-01],[5.4556714e+02, 6.3534674e+02, 1.8182488e-01],[5.8149310e+02, 7.2958716e+02, 1.3625422e-01],[5.6579541e+02, 5.3216382e+02, 3.6866242e-01],[5.8822272e+02, 6.2862476e+02, 1.7708556e-01],[6.0843213e+02, 7.2955762e+02, 2.2736737e-01],[4.7597812e+02, 3.2798129e+02, 5.7176876e-01],[4.8729745e+02, 3.3027243e+02, 9.1296065e-01],[0.0000000e+00, 0.0000000e+00, 0.0000000e+00],[5.2090784e+02, 3.3472034e+02, 7.7942842e-01],[5.7928674e+02, 7.5646222e+02, 2.0351715e-01],[5.9049512e+02, 7.5648248e+02, 2.0819387e-01],[6.2183606e+02, 7.3853394e+02, 1.7312977e-01],[5.8145673e+02, 7.5420642e+02, 1.2660497e-01],[5.7701074e+02, 7.5417773e+02, 1.2881383e-01],[5.8374255e+02, 7.3627380e+02, 9.4869599e-02]]
@@ -58,9 +59,13 @@ if not SKIP_OPENPOSE:
     opWrapper.start()
 
 images = [os.path.join(os.getcwd(), IN_DIR, f) for f in os.listdir(IN_DIR)] #make path absolute so os.chdir has no side effects
+images_bdcn = [os.path.join(os.getcwd(), IN_DIR_BDCN, f) for f in os.listdir(IN_DIR_BDCN)]
+images.sort()
+images_bdcn.sort()
 
 os.chdir(OUT_DIR) #save images in this dir
-for img_name in images:
+#for img_name in images:
+for img_name, img_bdcn in list(zip(images, images_bdcn)):
     # Process Image
     print("calculating: "+img_name)
     img = cv2.imread(img_name)

playground/bdcn_postprocessing.py

@@ -46,15 +46,15 @@ datum.poseKeypoints = OPENPOSE_DEMO_KEYPOINTS
 # opWrapper.configure(params)
 # opWrapper.start()
 
-for img_name, img_bdcn in list(zip(images, images_bdcn)): #christus only
+for img_name, img_bdcn in list(zip(images, images_bdcn))[15:16]: #christus only
     img = cv2.imread(img_name)
     bdcn = cv2.imread(img_bdcn)
     max_lw = max(len(img),len(img[0]))
     esz = max_lw / DISPLAY_RASTER_ELEMENTS
 
-    datum = op.Datum()
-    datum.cvInputData = img
-    opWrapper.emplaceAndPop([datum])
+    # datum = op.Datum()
+    # datum.cvInputData = img
+    # opWrapper.emplaceAndPop([datum])
 
     fposes = np.array([np.array([line[:2] for line in pose if line[2] > 0]) for pose in datum.poseKeypoints]) #filtered poses without zero lines
     
@@ -75,29 +75,31 @@ for img_name, img_bdcn in list(zip(images, images_bdcn)): #christus only
 
     cv2.namedWindow(img_name, cv2.WINDOW_NORMAL)
     cv2.namedWindow(img_name+'bdcn', cv2.WINDOW_NORMAL)
-    cv2.namedWindow(img_name+'blurred', cv2.WINDOW_NORMAL)
+    # cv2.namedWindow(img_name+'blurred', cv2.WINDOW_NORMAL)
 
-    rho=10
-    theta=180
+    rho=3
+    theta=360
     threshold=2950
-    minline=int(max_lw/4) #minline 1/6 of image width/heigth
-    maxgap=int(max_lw/50) #minline 1/12 of image width/heigth
+    minline=int(max_lw/2) #minline 1/6 of image width/heigth
+    maxgap=int(max_lw/100) #minline 1/12 of image width/heigth
     print("params",rho,theta,threshold,minline,maxgap)
     #blurred = bdcn.copy()
     #cv2.imshow(img_name+'blurred', blurred)
     gray = cv2.cvtColor(bdcn, cv2.COLOR_BGR2GRAY)
-    blurred = cv2.GaussianBlur(gray,(5,5),0)
+    blurred = cv2.GaussianBlur(gray,(1,1),0)
     # cv2.imshow(img_name+'blurred', blurred)
     # cv2.waitKey(0)
-    lines = cv2.HoughLinesP(image=blurred, rho=rho, theta=np.pi/theta, threshold=threshold, minLineLength=minline, maxLineGap=maxgap)
+    lines = cv2.HoughLines(image=blurred, rho=rho, theta=np.pi/theta, threshold=threshold, minLineLength=minline, maxLineGap=maxgap)
+    
     # if(lines):
     if lines is not None:
+        print("len",len(lines))
         for line in lines:
             x1, y1, x2, y2 = line[0]
             cv2.line(bdcn, (x1, y1), (x2, y2), (255, 0, 0), 3)
     
     cv2.imshow(img_name, img)
-    cv2.imshow(img_name+'blurred', blurred)
+    # cv2.imshow(img_name+'blurred', blurred)
     cv2.imshow(img_name+'bdcn', bdcn)
     cv2.waitKey(0)
 

playground/threshold_slider.py

@@ -31,47 +31,83 @@ theta=4
 threshold=100
 minline=220
 maxgap=30
+gauss=11
 
 def on_rho(val):
+    if(val<=0): return
     global rho
     rho=val
     update()
 def on_theta(val):
+    if(val<=0): return
     global theta
     theta=val
     update()
 def on_threshold(val):
+    if(val<=0): return
     global threshold
     threshold=val
     update()
 def on_minline(val):
+    if(val<=0): return
     global minline
     minline=val
     update()
 def on_maxgap(val):
+    if(val<=0): return
     global maxgap
     maxgap=val
     update()
+def on_gauss(val):
+    if(val<=0): return
+    global gauss
+    gauss=val
+    update()
+
 def update():
     image = cv.imread('../images/out/fuse_1_bsds500/1.png')
-    lines = cv.HoughLinesP(image=image, rho=rho, theta=np.pi/theta, threshold=threshold, minLineLength=minline, maxLineGap=maxgap)
+    image = cv.bitwise_not(image)
+    blurred = cv.bilateralFilter(image,50,100,100)
+    blurred = cv.medianBlur(blurred,gauss)
+    blurred = cv.GaussianBlur(blurred, (gauss, gauss), 0)
+    gray = cv.cvtColor(blurred, cv.COLOR_BGR2GRAY)
+    # auto = auto_canny(blurred)
+    
+    # lines = cv.HoughLinesP(image=gray, rho=rho, theta=np.pi/theta, threshold=threshold, minLineLength=minline, maxLineGap=maxgap)
     # if(lines):
+    # if lines is not None:
+    #     for line in lines:
+    #         x1, y1, x2, y2 = line[0]
+    #         cv.line(blurred, (x1, y1), (x2, y2), (255, 0, 0), 3)
+    #     cv.imshow("Original", blurred)
+    # else:
+    #     print("dont show")
+    #     cv.imshow("Original", blurred)
+    print(rho,np.pi/theta,threshold)
+    lines = cv.HoughLines(gray,rho,np.pi/theta,threshold)
     if lines is not None:
-        for line in lines:
-            x1, y1, x2, y2 = line[0]
-            cv.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3)
-        cv.imshow("Original", image)
-    else:
-        print("dont show")
+        for rho2,theta2 in lines[:,0]:
+            a = np.cos(theta2)
+            b = np.sin(theta2)
+            x0 = a*rho2
+            y0 = b*rho2
+            x1 = int(x0 + 1000*(-b))
+            y1 = int(y0 + 1000*(a))
+            x2 = int(x0 - 1000*(-b))
+            y2 = int(y0 - 1000*(a))
+
+            cv.line(blurred,(x1,y1),(x2,y2),(0,0,255),2)
+    cv.imshow("Original", blurred)
 
 
 cv.namedWindow("Original", cv.WINDOW_NORMAL)
 
 cv.createTrackbar("rho", "Original" , 0, 50, on_rho)
 cv.createTrackbar("theta", "Original" , 0, 360, on_theta)
-cv.createTrackbar("threshold", "Original" , 0, 500, on_threshold)
+cv.createTrackbar("threshold", "Original" , 0, 9000, on_threshold)
 cv.createTrackbar("minline", "Original" , 0, 500, on_minline)
 cv.createTrackbar("maxgap", "Original" , 0, 100, on_maxgap)
+cv.createTrackbar("gauss", "Original" , 0, 100, on_gauss)
 update()
 
 cv.waitKey(0)