Error correction for circle detection, autocrop modification, report …

…modification

AMi_Image_Analysis.py

@@ -47,7 +47,7 @@
 
 def Citation():
     print('''
-Program written for in python3/PyQt5 by
+Program written for python3/PyQt5 by
 Ludovic Pecqueur
 Laboratoire de Chimie des Processus Biologiques
 Collège de France.
@@ -688,14 +688,14 @@ def buttonClicked(self):
                                               ClassificationColor[self.classifications[well]]["text"]))
 
 
-    def LoadWellImage(self,path):
-
-        QtGui.QPixmapCache.clear()
-        label=QLabel(self)
-        pixmap=QPixmap(path)
-        #resize pixmap to size of the QscrollArea Temporary?
-        label.setPixmap(pixmap.scaled(860, 630, QtCore.Qt.KeepAspectRatio, QtCore.Qt.FastTransformation))
-        self.ImageViewer.setWidget(label)
+    # def LoadWellImage(self,path):
+    #     ''' '''
+    #     QtGui.QPixmapCache.clear()
+    #     label=QLabel(self)
+    #     pixmap=QPixmap(path)
+    #     #resize pixmap to size of the QscrollArea Temporary?
+    #     label.setPixmap(pixmap.scaled(860, 630, QtCore.Qt.KeepAspectRatio, QtCore.Qt.FastTransformation))
+    #     self.ImageViewer.setWidget(label)
 
     def open_image(self, path):
         '''based on https://vincent-vande-vyvre.developpez.com/tutoriels/pyqt/manipulation-images/'''

Check_Circle_detection.py

@@ -36,7 +36,7 @@ def DetectCircle(_file):
     # Apply Hough transform on the blurred image. 
     detected_circles = cv2.HoughCircles(gray_blurred,  
                        cv2.HOUGH_GRADIENT, 1, pref.minDistance , param1 = pref.param1, 
-                   param2 = pref.param1, minRadius =pref.minRadius, maxRadius = pref.maxRadius) 
+                   param2 = pref.param2, minRadius =pref.minRadius, maxRadius = pref.maxRadius) 
 
     euclidians=[]
 

autocrop.py

@@ -12,7 +12,7 @@
     python3 autocrop.py
     
     Function is now called within GUI.
-    Can still be used as standalone
+    Can still be used as standalone script
 """
 
 import os
@@ -31,19 +31,32 @@ def natural_sort_key(s):
 
 
 def crop_ROI(image, output_dir, well):
-    x, y, r = find_best_circle(image)    
-    r=r+100
-    cropped=image[y-r:y+r, x-r:x+r]
-    # print("CROPPED image size well %s "%well, cropped.shape)
+    x, y, r = find_best_circle(image)
+    if x==0 or y==0: #If No circle was detected 
+        print("CROPPED image for well %s is empty, NOT SAVED"%well)
+        return False
+    r=r+20
+    Ymax, Xmax =image.shape[0], image.shape[1]
+    if y-r<0:ymin=0
+    else:ymin=y-r
+    if y+r>Ymax:ymax=Ymax
+    else:ymax=y+r
+    if x-r<0:xmin=0
+    else:xmin=x-r
+    if x+r>Xmax:xmax=Xmax
+    else: xmax=x+r
+    cropped=image[ymin:ymax, xmin:xmax]
+    # cropped=image[y-r:y+r, x-r:x+r]
+    # print("CROPPED image size well %s "%well, cropped.shape, "r=", r)
 
     path=Path(output_dir).joinpath("cropped",well+".jpg")
 
     #Only save images with bytes otherwise print error message
     if cropped.shape[0] != 0 and  cropped.shape[1] != 0:
         cv2.imwrite(str(path), cropped)
-        print("well %s saved to %s"%(well, path))
+        # print("well %s saved to %s"%(well, path))
     else:
-        # print("CROPPED image for well %s is empty"%well)
+        print("CROPPED image for well %s is empty, NOT SAVED"%well)
         return False
 
 
@@ -58,9 +71,7 @@ def find_best_circle(image):
                     cv2.HOUGH_GRADIENT, 1, pref.minDistance, param1 = pref.param1, 
                 param2 = pref.param2, minRadius = pref.minRadius, maxRadius = pref.maxRadius)
 
-    R = 0
-    X = 0
-    Y = 0
+    R, X, Y = 0, 0, 0
     euclidians=[]
 
     #Find detected circle with max radius closest to center of image, temporary bad solution
@@ -75,8 +86,8 @@ def find_best_circle(image):
                 R = int(i[1])
                 X = int(i[2])
                 Y = int(i[3])
-    # print("Rmax, X, Y ", R, X, Y)
-    # print("DISTANCES to image center ", euclidians, "MIN distance to image center", min(euclidians))
+    # if circles is not None:
+    #     print("DISTANCES to image center for %s"%well, euclidians, "MIN distance to image center", min(euclidians))
     del euclidians
     return X,Y,R
 
@@ -106,7 +117,7 @@ def find_best_circle(image):
         output=crop_ROI(img, directory, well)
         if output is False:
             errors +=1
-            error_list.append(well)
+            error_list.append(os.path.basename(_file))
         del img, output
 
     log=Path(directory).joinpath("cropped","autocrop.log")

pdf_writer.py

@@ -11,6 +11,8 @@
 2019
 '''
 
+__date__ = "04-02-2020"
+
 class PDF(fpdf.FPDF):
 
     def header(self):

preferences.py

@@ -16,7 +16,7 @@
 
 #Comment/uncomment the two lines below according to your need
 keyboard_layout="qwerty"
-#keyboard_layout="azerty"
+# keyboard_layout="azerty"
 
 #autoMARCO acceptance probability criterium
 autoMARCO_threshold=0.60
@@ -58,8 +58,8 @@ class Shortcut():
 class DetectCircle():
     '''parameters for circle detection used in autocrop.py
        and Check_Circle_detection.py'''
-    param1=35
+    param1=60
     param2=25
-    minDistance=120
-    minRadius=100
-    maxRadius=300
+    minDistance=800
+    minRadius=250
+    maxRadius=400