Debug mode

qr_code_extractor.py

@@ -1,5 +1,7 @@
 import cv2
-
+import numpy as np
+from skimage.measure import label, regionprops
+import matplotlib.pyplot as plt
 
 def get_qr_data(file, debug_show_image=False):
     position_marker_coordinates = None
@@ -8,10 +10,183 @@ def get_qr_data(file, debug_show_image=False):
     payload, bounding_box, rectified_image = cv2.QRCodeDetector().detectAndDecode(input_image)
 
     if len(payload) > 0:
-        position_marker_coordinates = get_position_marker_coordinates(bounding_box)
+        position_marker_coordinates_linear = get_position_marker_coordinates(bounding_box)
+
+        # get the corner pixel of the qr image
+        x_min = int(np.floor(np.min(bounding_box[0][:, 1]))) - 10
+        x_max = int(np.ceil(np.max(bounding_box[0][:, 1])))  + 10
+        y_min = int(np.floor(np.min(bounding_box[0][:, 0]))) - 10
+        y_max = int(np.ceil(np.max(bounding_box[0][:, 0])))  + 10
+
+        qr_image = input_image[x_min: x_max, y_min : y_max]
+
+        # converting the image to grey image for better contrast
+        img_gray = cv2.cvtColor(qr_image, cv2.COLOR_BGR2GRAY)
+        ret, img_gray = cv2.threshold(img_gray,127,255,cv2.THRESH_BINARY)
+
+
+        # get all the clusters in the original black and white qr-code image
+        label_img = label(img_gray)
+        regions = regionprops(label_img)
+
+        # get all the clusters in the inverted black and white qr-code image
+        img_gray_invert = cv2.bitwise_not(img_gray)
+        label_img_invert = label(img_gray_invert)
+        regions_invert = regionprops(label_img_invert)
+
+
+
+        # get the diagonal from the center bobble to the other corner without bobble
+        diagonal_x1 = [int(bounding_box[0][0][0])-y_min, int(bounding_box[0][2][0])-y_min]
+        diagonal_y1 = [int(bounding_box[0][0][1])-x_min, int(bounding_box[0][2][1])-x_min]
+
+        diagonal_m1 = ((int(bounding_box[0][2][1])-x_min) - (int(bounding_box[0][0][1])-x_min)) /\
+            ((int(bounding_box[0][2][0])-y_min) - (int(bounding_box[0][0][0])-y_min))
+
+        diagonal_b1 = (int(bounding_box[0][2][1])-x_min) - diagonal_m1 * (int(bounding_box[0][2][0])-y_min)
+
+        dia_x1 = []
+        dia_y1 = []
+        for i in range(y_max - y_min):
+            dia_x1.append(i)
+            dia_y1.append(diagonal_m1*i+diagonal_b1)
+
+
+        # get the diagonal from the left down bobble to the other corner with right up bobble
+        diagonal_x3 = [int(bounding_box[0][1][0])-y_min, int(bounding_box[0][3][0])-y_min]
+        diagonal_y3 = [int(bounding_box[0][1][1])-x_min, int(bounding_box[0][3][1])-x_min]
+
+        diagonal_m3 = ((int(bounding_box[0][3][1])-x_min) - (int(bounding_box[0][1][1])-x_min)) /\
+            ((int(bounding_box[0][3][0])-y_min) - (int(bounding_box[0][1][0])-y_min))
+
+        diagonal_b3 = (int(bounding_box[0][3][1])-x_min) - diagonal_m3 * (int(bounding_box[0][3][0])-y_min)
+
+        dia_x3 = []
+        dia_y3 = []
+        for i in range(y_max - y_min):
+            dia_x3.append(i)
+            dia_y3.append(diagonal_m3*i+diagonal_b3)
+
+
+        # put all the cluster IDs along the first diagonal
+        signal1 = []
+        for i in range(len(dia_x1)):
+            if dia_y1[i] >= 0 and dia_y1[i] < x_max - x_min:
+                signal1.append(label_img_invert[int(dia_y1[i])][int(dia_x1[i])])
+
+        # put all the cluster IDs along the second diagonal
+        signal3 = []
+        for i in range(len(dia_x3)):
+            if dia_y3[i] >= 0 and dia_y3[i] < x_max - x_min:
+                signal3.append(label_img_invert[int(dia_y3[i])][int(dia_x3[i])])
+
+
+        # find the center bobble. that is the cluster after the 2nd cluster ID change
+        found1 = 0
+        index1 = 0
+        index1_b4 = -1
+        for i in range(len(signal1)):
+            if signal1[i] != 0 and index1_b4 != signal1[i]:
+                found1 += 1
+
+            if found1 == 2:
+                index1 = signal1[i]
+                break
+
+            index1_b4 = signal1[i]
+
+        x1, y1 = regions_invert[index1-1].centroid
+        area1 = regions_invert[index1-1].area
+
+        cv2.circle(input_image, [y_min + int(y1), x_min + int(x1)], 30, (0, 255, 255), -1)
+
+
+        # find the left down bobble. that is the cluster after the 2nd cluster ID change
+        found2 = 0
+        index2 = 0
+        index2_b4 = -1
+        for i in range(len(signal3)):
+            if signal3[i] != 0 and index2_b4 != signal3[i]:
+                found2 += 1
+
+            if found2 == 2:
+                index2 = signal3[i]
+                break
+
+            index2_b4 = signal3[i]
+
+        x2, y2 = regions_invert[index2-1].centroid
+        area2 = regions_invert[index2-1].area
+
+        cv2.circle(input_image, [y_min + int(y2), x_min + int(x2)], 30, (255, 255, 0), -1)
+
+
+        # find the righ up bobble. that is the cluster after the 2nd cluster ID change
+        found3 = 0
+        index3 = 0
+        index3_b4 = -1
+        for i in range(len(signal3)):
+            if signal3[len(signal3) - i - 1] != 0 and index3_b4 != signal3[len(signal3) - i - 1]:
+                found3 += 1
+
+            if found3 == 2:
+                index3 = signal3[len(signal3) - i - 1]
+                break
+
+            index3_b4 = signal3[len(signal3) - i - 1]
+
+        x3, y3 = regions_invert[index3-1].centroid
+        area3 = regions_invert[index3-1].area
+
+        cv2.circle(input_image, [y_min + int(y3), x_min + int(x3)], 30, (255, 0, 255), -1)
+
+        position_marker_coordinates_area = y_min + y1, x_min + x1
+        #maybe there needs to be a selection between the linear and the area cluster method
+        position_marker_coordinates = position_marker_coordinates_area
+
 
         if debug_show_image:
-            cv2.circle(input_image, position_marker_coordinates, 10, (0, 255, 0), -1)
+
+            plt.imshow(label_img_invert)
+            plt.plot(diagonal_x1, diagonal_y1, "o-")
+            plt.plot(diagonal_x3, diagonal_y3, "o-")
+            plt.plot(y1, x1, "*")
+            plt.plot(y2, x2, "*")
+            plt.plot(y3, x3, "*")
+            plt.show()
+
+            plt.imshow(label_img)
+            plt.plot(diagonal_x1, diagonal_y1, "o-")
+            plt.plot(diagonal_x3, diagonal_y3, "o-")
+            plt.plot(y1, x1, "*")
+            plt.plot(y2, x2, "*")
+            plt.plot(y3, x3, "*")
+
+            # finding the smallest bobble, the 4th bobble
+            for i in range(len(regions_invert)):
+                if regions_invert[i].area > (area1 + area2 + area3) / 3 / 13 and \
+                        regions_invert[i].area < (area1 + area2 + area3) / 3 / 6:
+
+                    xm, ym = regions_invert[i].centroid
+
+                    plt.plot(ym, xm, "o")
+
+
+            plt.title("cluster id on qr-code, diagonals and position markers")
+            plt.show()
+
+            plt.plot(signal1, label="diagonal 1")
+            plt.plot(signal3, label="diagonal 2")
+            plt.xlabel("x position via diagonal")
+            plt.ylabel("cluster id for pixel on diagonal")
+            plt.grid()
+            plt.legend()
+            plt.show()
+
+
+            circle_x = int(position_marker_coordinates[0])
+            circle_y = int(position_marker_coordinates[1])
+            cv2.circle(input_image,[circle_x, circle_y] , 10, (0, 0, 255), -1)
 
             base = input_image.shape[0]
             if base < input_image.shape[1]:

requirements.txt

@@ -3,3 +3,6 @@ utm
 opencv-python
 pyproj
 pyzbar
+scikit-image
+numpy
+matplotlib