Changed algorithm to used to detect houses. Removed redundant code in recolour.py

Author: daniel-stoneuk

.gitignore

@@ -8,6 +8,13 @@ __pycache__/
 *.py[cod]
 *$py.class
 
+tapplacement/*
+venv/*
+
+credentials.py
+
+*.pyc
+
 # C extensions
 *.so
 
@@ -126,4 +133,5 @@ dmypy.json
 # End of https://www.gitignore.io/api/python
 
 # Visual Studio Code
-.vscode/
+.vscode/
+.pylintrc

TapWork.py

@@ -1,6 +1,9 @@
 import math
 from PIL import Image, ImageChops
 import networkx as nx
+import matplotlib
+# matplotlib.use('TkAgg')
+matplotlib.use('agg')
 import matplotlib.pyplot as plt
 import itertools
 import cv2
@@ -124,4 +127,4 @@ def greedy_brute(houses,amount_of_taps,grid_size):
 
                     min_total_differnces = total_differnce
             stored_taps.append(happy_taps[-1])
-    return stored_taps
+    return stored_taps

api.py

@@ -4,9 +4,7 @@
 import cv2
 import base64
 import numpy
-import TapWork, recolour, resize, contours, map_downloader
-
-API_KEY = 'AgqByPum4K6T5wlV3oIAhSDvFHVRuoPs6cwipRdvprWtmvqld0poyLI54AP0e6HI'
+import TapWork, recolour, resize, contours, map_downloader, credentials
 
 app = Flask(__name__)
 cors = CORS(app)
@@ -38,7 +36,7 @@ def giveLocation():
         response = Response()
         response.status_code = 400
         return response
-    response = map_downloader.download_patch((lat,lon),API_KEY)
+    response = map_downloader.download_patch((lat,lon), credentials.API_KEY)
     image_array = numpy.asarray(bytearray(response.content), dtype=numpy.uint8)
     map_image = cv2.imdecode(image_array, -1)
     if max_size is not None and taps is not None:

contours.py

@@ -4,13 +4,13 @@
 
 # construct the argument parse and parse the arguments
 def get_contour_nodes(image):
-    height, width, channels = image.shape
-    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
-    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
-    thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
+    height, width = image.shape
+    # gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    # blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+    # thresh = cv2.threshold(blurred, 60, 255, cv2.THRESH_BINARY)[1]
 
     # find contours in the thresholded image
-    cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
+    cnts = cv2.findContours(image.copy(), cv2.RETR_EXTERNAL,
         cv2.CHAIN_APPROX_SIMPLE)
     cnts = imutils.grab_contours(cnts)
 
@@ -20,7 +20,7 @@ def get_contour_nodes(image):
     for c in cnts:
         # compute the center of the contour
         try:
-            if cv2.contourArea(c) > 40:
+            if cv2.contourArea(c) > 5:
                 M = cv2.moments(c)
                 cX = int(M["m10"] / M["m00"])
                 cY = int(M["m01"] / M["m00"])

figureTaps(hsv).png

@@ -1,20 +1,31 @@
+import cv2
+import numpy as np
 import resize
 import recolour
 import contours
 import TapWork
 import map_downloader
-import cv2
-import numpy as np
+import credentials
 
-API_KEY = 'PLACEHOLDER'
-
-response = map_downloader.download_patch((13.623299, -15.192386),API_KEY)
+# 13.624999, -15.191250
+# 13.623299, -15.192386
+response = map_downloader.download_patch((13.624999, -15.191250), credentials.API_KEY)
+print("Got map")
 image_array = np.asarray(bytearray(response.content), dtype=np.uint8)
 map_image = cv2.imdecode(image_array, -1)
 max_size = 200
-resized_image = resize.resize(map_image,max_size)
+resized_image = resize.resize(map_image, max_size)
 height, width, _ = resized_image.shape
+cv2.imwrite("test.png", resized_image)
+cv2.imshow("Window", resized_image)
+cv2.waitKey(0)
+
 recoloured_image = recolour.find_silver(resized_image)
+cv2.imshow("Window", recoloured_image)
+cv2.waitKey(0)
+
 houses = contours.get_contour_nodes(recoloured_image)
-tap_locations = TapWork.greedy_brute(houses,5,(height,width))
-TapWork.draw_network(houses,tap_locations,resized_image)
+print(houses)
+tap_locations = TapWork.greedy_brute(houses, 5, (height, width))
+print(tap_locations)
+TapWork.draw_network(houses, tap_locations, resized_image)

figureTaps(hsv2).png

@@ -2,19 +2,26 @@
 import numpy
 
 def find_silver(image):
+    image = cv2.GaussianBlur(image,(5,5),0)
     hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
-    h,s,v = cv2.split(hsv)
-
     rgb = cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
 
-    mask = cv2.inRange(rgb, (150,160,160), (255, 255, 255))
-    image = cv2.bitwise_and(image, image, mask=mask)
-
-    for w in range(image.shape[0]):
-        for h in range(image.shape[1]):
-            # checks if not pure black
-            if(all(rgb != 0 for rgb in image[w][h])):
-                # makes pixel completely white
-                for colour in range(image.shape[2]):
-                    image[w][h][colour] = 255
-    return image
+    cv2.imshow("HSV", hsv)
+    cv2.waitKey();
+
+    mask2 = cv2.inRange(hsv, (10,0,120), (85, 70, 255))
+    # mask2 = cv2.inRange(hsv, (0,0,180), (255, 255, 255))
+
+
+    cv2.imshow("mask2", mask2)
+
+    newimage = cv2.bitwise_and(rgb, rgb, mask=mask2)
+    mask = cv2.inRange(rgb, (150,150,140), (255, 255, 255))
+
+
+
+    cv2.imshow("mask", mask)
+    cv2.waitKey();
+
+
+    return mask