Image processing code added

iot/eyebot/automatic_navigation.py

@@ -6,6 +6,9 @@ from picamera import PiCamera
 import time
 import mysql.connector
 from mysql.connector import errorcode
+import cv2   
+import matplotlib.pyplot
+import numpy as np
 
 # Direction pin from controller
 DIR = 10
@@ -51,11 +54,102 @@ cursor = conn.cursor()
 sql_insert_blob_query = """ INSERT INTO plant_images
                           (img_ID, plant_ID, date, image) VALUES (%s,%s,%s,%s)"""
 
+sql_insert_blob_query_procssed = """ INSERT INTO processed_images
+                          (img_ID, plant_ID, date, image) VALUES (%s,%s,%s,%s)"""
+
 #cnx = mysql.connector.connect(user="ThyagaSenatilaka@azdbmysqltest", password="Thagi.12345", host="azdbmysqltest.mysql.database.azure.com", port=3306, database="agroengine", ssl_verify_cert=False)
 
 #if cnx.is_connected():
 #        print("Connection established")
 
+def processImage(image):
+        #Read the path of the image
+        #------------------------------------------------------
+        # read image, support bmp,jpg,png,tiff format
+        #Read the path of the image in the computer
+        img = cv2.imread(image)#Change the image path to your own
+        #Set the RGB value in the image to a 64-bit floating point
+        img=np.array(img,dtype='float64')
+        #Declare the three 0 matrices, and put the R,G, and B values of the picture into the three matrices.
+        b = np.zeros((img.shape[0],img.shape[1]), dtype=img.dtype)
+        g = np.zeros((img.shape[0],img.shape[1]), dtype=img.dtype)
+        r = np.zeros((img.shape[0],img.shape[1]), dtype=img.dtype)
+        b[:,:] = img[:,:,0]
+        g[:,:] = img[:,:,1]
+        r[:,:] = img[:,:,2]
+        #A formula for generating grayscale images
+        new=2*g-r-b
+        w=new.min()
+        e=new.max()
+        new=new-w
+        new=new/e*255
+        new=np.array(new,dtype='uint8') 
+        #-----------------------------------------------------------------------
+
+        # Otsu filtering method and fill the hole
+        #-----------------------------------------------------------------------
+        # A Otsu filtering method is used for filtering
+        ret2, th2 = cv2.threshold(new, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        #Check the threshold of Otsu filtering method
+        print("threshold of Otsu filtering:",ret2)
+        # Otsu filtering's results are replicated to hole
+        hole=th2.copy()
+        #Find the hole and fill it
+        cv2.floodFill(hole,None,(0,0),255) 
+        hole=cv2.bitwise_not(hole)
+        filledEdgesOut=cv2.bitwise_or(th2,hole)
+        #---------------------------------------------------------------------
+
+        #The image of corrosion
+        #-------------------------------------------------------------------------------
+        #A circle of diameter 5 is used as the corrosion structure
+        kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(5,5))
+        #image of corrosion
+        eroded = cv2.erode(filledEdgesOut,kernel)
+        #-------------------------------------------------------------------------------
+
+        #Eliminate connected region
+        #-------------------------------------------------------------------------------
+        def baweraopen(image,size):
+            output=image.copy()
+            nlabels, labels, stats, centroids = cv2.connectedComponentsWithStats(image)
+            for i in range(1,nlabels-1):
+                regions_size=stats[i,4]
+                if regions_size<size:
+                    x0=stats[i,0]
+                    y0=stats[i,1]
+                    x1=stats[i,0]+stats[i,2]
+                    y1=stats[i,1]+stats[i,3]
+                    for row in range(y0,y1):
+                        for col in range(x0,x1):
+                            if labels[row,col]==i:
+                                output[row,col]=0
+            return output
+        im2=baweraopen(eroded,180)#200 is the size of the connected region to be eliminated.
+        #---------------------------------------------------------------------------------
+
+        # Count the number of pixels in the plant
+        #---------------------------------------------------------------------------------
+        print("number of pixels in the plant:",len(im2.nonzero()[0]))
+        #distance is 50
+        distance_top=50
+        Area=(pow((0.000122*(distance_top-0.304)/0.304),2)*len(im2.nonzero()[0]))
+        print("leaf area:",round(Area, 2))
+        #---------------------------------------------------------------------------------
+
+        #show the new image.
+        #---------------------------------------------------------------------------------
+        img[:,:,2]=im2*r
+        img[:,:,1]=im2*g
+        img[:,:,0]=im2*b
+        matplotlib.pyplot.imshow((img * 255).astype(np.uint8))
+        #---------------------------------------------------------------------------------
+
+        #save the new image
+        filename = "p_"+image
+        cv2.imwrite(filename, img)
+
+
 def tower_rotate_cw(x):
         print("tower cw rotate ",x)
         GPIO.output(DIR,CW)
@@ -122,7 +216,21 @@ def capture(index):
         insert_blob_tuple = (name, str(index), date, blob)
         result = cursor.execute(sql_insert_blob_query, insert_blob_tuple)
         conn.commit()
-        print("Uploaded Successfully")
+        print("Raw Image Uploaded Successfully")
+
+        time.sleep(2)
+
+        processImage(pic_name)
+
+        processed_img = "p_"+pic_name
+
+        blob = convertToBinaryData(processed_img)
+   
+        # Convert data into tuple format
+        insert_blob_tuple = ("p_"+name, str(index), date, blob)
+        result = cursor.execute(sql_insert_blob_query_procssed, insert_blob_tuple)
+        conn.commit()
+        print("Processed Image Uploaded Successfully")
 
         time.sleep(2)
 
@@ -144,49 +252,49 @@ try:
 
 
         
-        #tower_rotate_cw(0)
+        tower_rotate_cw(0)
         capture(0)
-        #camera_movement_cw(1)
+        camera_movement_cw(1)
         capture(1)
-        #camera_movement_cw(2)
+        camera_movement_cw(2)
         capture(2)
-        #camera_movement_cw(3)
+        camera_movement_cw(3)
         capture(3)
-        #camera_movement_cw(4)
+        camera_movement_cw(4)
         capture(4)
-        
-        #tower_rotate_cw(12200)
+
+        tower_rotate_cw(12200)
 
         capture(5)
-        #camera_movement_ccw(1)
+        camera_movement_ccw(1)
         capture(6)
-        #camera_movement_ccw(2)
+        camera_movement_ccw(2)
         capture(7)
-        #camera_movement_ccw(3)
+        camera_movement_ccw(3)
         capture(8)
-        #camera_movement_ccw(4)
+        camera_movement_ccw(4)
         capture(9)
       
         
-        #tower_rotate_cw(12200)
+        tower_rotate_cw(12200)
 
         capture(10)
-        #camera_movement_cw(1)
+        camera_movement_cw(1)
         capture(11)
-        #camera_movement_cw(2)
+        camera_movement_cw(2)
         capture(12)
-        #camera_movement_cw(3)
+        camera_movement_cw(3)
         capture(13)
-        #camera_movement_cw(4)
+        camera_movement_cw(4)
         capture(14)
         
-        #camera_movement_ccw(1)
-        #camera_movement_ccw(2)
-        #camera_movement_ccw(3)
-        #camera_movement_ccw(4)
+        camera_movement_ccw(1)
+        camera_movement_ccw(2)
+        camera_movement_ccw(3)
+        camera_movement_ccw(4)
         
   
-        #tower_rotate_ccw(24400)
+        tower_rotate_ccw(24400)
 
         if conn.is_connected():
             cursor.close()
@@ -199,3 +307,6 @@ except KeyboardInterrupt:
 	GPIO.cleanup()
 
 
+    
+
+