dots lines result

Edge/canny_Edge_Detection.py

@@ -13,8 +13,8 @@
 from matplotlib import pyplot as plt
 
 
-img = cv.imread('data/bp_src3/5.jpg', 0)
-edges = cv.Canny(img, 720, 576, edges=200, apertureSize=7)
+img = cv.imread('/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/color/lb_color.png', 0)
+edges = cv.Canny(img, 1001, 1001, edges=200, apertureSize=7)
 edges = Image.fromarray(edges)
 edges = edges.convert('RGBA')
 data = np.array(edges)   # "data" is a height x width x 4 numpy array
@@ -25,6 +25,6 @@
 # data[..., :-1][white_areas.T] = (0, 136, 126)     # Transpose back needed
 
 im2 = Image.fromarray(data)
-im2.save("data/temp/edge_ct.png")
+im2.save("/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/edge/edge_lb.png")
 im2.show()
 

Transparent/batch_transparent_color.py

@@ -21,7 +21,7 @@
 
 
 if __name__ == '__main__':
-    for filename in glob.glob('/home/luolu/PycharmProjects/color_detection/data/result_qaak/caolv_mask/*.png'):
+    for filename in glob.glob('/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/edge/*.png'):
         img = cv.imread(filename)
         # print(filename)
         base_name = os.path.basename(filename)
@@ -42,4 +42,4 @@
                     print(item)
 
         img.putdata(newData)
-        img.save("/home/luolu/PycharmProjects/color_detection/data/result_qaak/transparent/" + base_name)
+        img.save("/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/trans/" + base_name)

Transparent/color_img_transparent.py

@@ -8,7 +8,7 @@
 
 from PIL import Image
 
-img = Image.open('/home/luolu/PycharmProjects/ParticleDetection/data/image/line_thresh_pills_02.png')
+img = Image.open('/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/color/fen_color.png')
 img = img.convert("RGBA")
 datas = img.getdata()
 
@@ -25,4 +25,4 @@
 
 
 img.putdata(newData)
-img.save("/home/luolu/PycharmProjects/ParticleDetection/data/image/trans_line_thresh_pills_02.png", "PNG")
+img.save("/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/trans/fen_color.png", "PNG")

batch_fill_hole.py

@@ -0,0 +1,41 @@
+# -*- coding: utf-8 -*-
+# @Time : 2020/4/14 上午10:07
+# @Author : LuoLu
+# @FileName: batch_fill_hole.py
+# @Software: PyCharm
+# @Github ：https://github.com/luolugithub
+# @E-mail ：[email protected]
+import glob
+import os
+
+import cv2 as cv
+import numpy as np
+
+root_path = '/home/luolu/PycharmProjects/ParticleDetection/'
+
+if __name__ == '__main__':
+    base_name = ''
+    counter = 0
+    for filename in glob.glob('data/yashi_qscan/mask/*.png'):
+        img = cv.imread(filename, 0)
+        # height, width, channels = img.shape
+        print(filename)
+        base_name = os.path.basename(filename)
+        save_name = base_name.split('_')[0]
+
+        # fill hole
+        # read image, ensure binary
+        img[img != 0] = 255
+
+        # flood fill background to find inner holes
+        holes = img.copy()
+        cv.floodFill(holes, None, (0, 0), 255)
+
+        # invert holes mask, bitwise or with img fill in holes
+        holes = cv.bitwise_not(holes)
+        filled_holes = cv.bitwise_or(img, holes)
+
+        cv.imwrite(root_path + "data/yashi_qscan/" + save_name + '.png', filled_holes)
+        counter = counter + 1
+
+    print('counter: ', counter)

batch_remove_small.py

@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+# @Time : 2020/4/14 下午4:13
+# @Author : LuoLu
+# @FileName: batch_remove_small.py
+# @Software: PyCharm
+# @Github ：https://github.com/luolugithub
+# @E-mail ：[email protected]
+
+import glob
+import os
+
+import cv2 as cv
+import numpy as np
+
+root_path = '/home/luolu/PycharmProjects/ParticleDetection/'
+
+if __name__ == '__main__':
+    base_name = ''
+    counter = 0
+    for filename in glob.glob('data/yashi_qscan/mask/*.png'):
+        img = cv.imread(filename, 0)
+        # height, width, channels = img.shape
+        print(filename)
+        base_name = os.path.basename(filename)
+        save_name = base_name.split('_')[0]
+
+        # fill hole
+        # read image, ensure binary
+        img[img != 0] = 255
+
+        # flood fill background to find inner holes
+        holes = img.copy()
+        cv.floodFill(holes, None, (0, 0), 255)
+
+        # invert holes mask, bitwise or with img fill in holes
+        holes = cv.bitwise_not(holes)
+        filled_holes = cv.bitwise_or(img, holes)
+
+        # remove_small_objects
+
+        # find all your connected components (white blobs in your image)
+        nb_components, output, stats, centroids = cv.connectedComponentsWithStats(filled_holes, connectivity=8)
+        # connectedComponentswithStats yields every seperated component with information on each of them,
+        # such as size the following part is just taking out the background which is also considered a component,
+        # but most of the time we don't want that.
+        sizes = stats[1:, -1]
+        nb_components = nb_components - 1
+
+        # minimum size of particles we want to keep (number of pixels)
+        # here, it's a fixed value, but you can set it as you want, eg the mean of the sizes or whatever
+        min_size = 250
+
+        # your answer image
+        cleaned = np.zeros((output.shape))
+        # for every component in the image, you keep it only if it's above min_size
+        for i in range(0, nb_components):
+            if sizes[i] >= min_size:
+                cleaned[output == i + 1] = 255
+
+        cv.imwrite(root_path + "data/yashi_qscan/cleaned/" + save_name + '.png', cleaned)
+        counter = counter + 1
+
+    print('counter: ', counter)

binary_image.py

@@ -13,20 +13,20 @@
 from PIL.Image import Image
 
 
-path = "/home/luolu/PycharmProjects/ParticleDetection/data/image/particle_ct.png"
+path = "/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/color/fen_color.png"
 original = cv.imread(path)
 height, width, channels = original.shape
 src = cv.GaussianBlur(original, (1, 1), 0)
 gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
 # cv.THRESH_BINARY | cv.THRESH_OTSU        "ct.png"
-ret, binary_ = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TOZERO)
+ret, binary_ = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
 
 # 使用开运算去掉外部的噪声
 # kernel = cv.getStructuringElement(cv.MORPH_RECT, (11, 11))
 # binary = cv.morphologyEx(binary_, cv.MORPH_OPEN, kernel)
 cv.imshow('binary', binary_)
 
-cv.imwrite("/home/luolu/PycharmProjects/ParticleDetection/data/image/binary_ct.png", binary_)
+# cv.imwrite("/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/binary_test.png", binary_)
 
 cv.waitKey(0)
 cv.destroyAllWindows()

blob_detector.py

@@ -55,7 +55,7 @@
 # Show blobs
 cv2.imshow("Keypoints", im_with_keypoints)
 cv2.waitKey(0)
-
+cv2.destroyAllWindows()
 
 # detector = cv2.SimpleBlobDetector()
 #

conncted_components.py

@@ -0,0 +1,60 @@
+# -*- coding: utf-8 -*-
+# @Time : 2020/4/8 上午10:52
+# @Author : LuoLu
+# @FileName: conncted_components.py
+# @Software: PyCharm
+# @Github ：https://github.com/luolugithub
+# @E-mail ：[email protected]
+
+import cv2
+import numpy as np
+from PIL import Image
+from collections import Counter
+
+img = cv2.imread('/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/edge/edge_cl.png', 0)
+img = cv2.GaussianBlur(img, (1, 1), 0)
+img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary
+num_labels, labels_im = cv2.connectedComponents(img)
+print("num_labels:", num_labels - 1)
+# Image._show(Image.fromarray(labels_im == 2), title="labels_im.jpg")
+print("len labels_im:", (labels_im == 2).shape)
+print("type labels_im:", type(labels_im))
+# print("len 1:", np.where((labels_im == 2).sum()))
+# count = [0] * (num_labels)
+# print("count len:", range(num_labels))
+# for num in range(1, num_labels, 1):
+#     for i in range((labels_im == num).shape[0]):
+#         for j in range((labels_im == num).shape[1]):
+#             if (labels_im == num)[i][j] == 1:
+#                 count[num] = count[num] + 1
+#
+# print("count len:", len(count))
+# for iterm in range(1, len(count), 1):
+#     print("iterm" + str(iterm) + ":", count[iterm])
+
+
+
+
+
+def imshow_components(labels):
+    # Map component labels to hue val
+    label_hue = np.uint8(179 * labels / np.max(labels))
+    # Image._show(Image.fromarray(label_hue), title="label_hue.jpg")
+
+    blank_ch = 255 * np.ones_like(label_hue)
+    labeled_img = cv2.merge([label_hue, blank_ch, blank_ch])
+
+    # cvt to BGR for display
+    labeled_img = cv2.cvtColor(labeled_img, cv2.COLOR_HSV2BGR)
+    # Image._show(Image.fromarray(blank_ch), title="blank_ch.jpg")
+
+    # set bg label to black
+    labeled_img[label_hue == 0] = 40
+
+    cv2.namedWindow("labeled", flags=2)
+    cv2.imshow("labeled", labeled_img)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+imshow_components(labels_im)

count/count_mask.py

@@ -0,0 +1,92 @@
+# -*- coding: utf-8 -*-
+# @Time : 2020/3/23 下午4:30
+# @Author : LuoLu
+# @FileName: count_mask.py
+# @Software: PyCharm
+# @Github ：https://github.com/luolugithub
+# @E-mail ：[email protected]
+import cv2
+import matplotlib
+import numpy as np
+import cv2 as cv
+import matplotlib.pyplot as plt
+from PIL.Image import Image
+
+matplotlib.use('TkAgg')
+cv.namedWindow("original", flags=2)
+cv.namedWindow("binary", flags=2)
+cv.namedWindow("colored labels", flags=2)
+
+path = "/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/edge/edge_cl.png"
+# img_src = "/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/binary_test.png"
+original = cv.imread(path)
+# src_image = cv.imread(img_src)
+height, width, channels = original.shape
+src = cv.GaussianBlur(original, (3, 3), 0)
+gray = cv.cvtColor(src, cv.COLOR_BGR2GRAY)
+ret, binary_ = cv.threshold(gray, 0, 255, cv.THRESH_BINARY | cv.THRESH_TRIANGLE)
+
+# 使用开运算去掉外部的噪声
+kernel = cv.getStructuringElement(cv.MORPH_RECT, (3, 3))
+binary = cv.morphologyEx(binary_, cv.MORPH_OPEN, kernel)
+# gray_correct = np.array(255 * (gray / 255) ** 1.2, dtype='uint8')
+# thresh = cv.adaptiveThreshold(gray_correct, 50, cv.ADAPTIVE_THRESH_GAUSSIAN_C, cv.THRESH_BINARY, 255, 50)
+# binary = cv.bitwise_not(thresh)
+cv.imshow("original", original)
+cv.imshow("binary", binary_)
+# cv.imshow("binary digit", binary_)
+
+num_labels, labels, stats, centers = cv.connectedComponentsWithStats(binary, connectivity=4, ltype=cv.CV_32S)
+colors = []
+for i in range(num_labels):
+    b = np.random.randint(0, 256)
+    g = np.random.randint(0, 256)
+    r = np.random.randint(0, 256)
+    colors.append((b, g, r))
+
+# add continuous pixel count
+img = cv2.imread(path, 0)
+img = cv2.GaussianBlur(img, (1, 1), 0)
+img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)[1]  # ensure binary
+num_labels_original, labels_im = cv2.connectedComponents(img)
+count = [0] * (num_labels_original)
+# print("count len:", range(1, num_labels_original, 1))
+# for num in range(num_labels_original):
+#     for i in range((labels_im == num).shape[0]):
+#         for j in range((labels_im == num).shape[1]):
+#             if (labels_im == num)[i][j] == 1:
+#                 count[num] = count[num] + 1
+#
+# dots = 0
+# line = 0
+# for iterm in range(1, len(count), 1):
+#     # print("iterm" + str(iterm) + ":", count[iterm])
+#     if count[iterm] > 10:
+#         line = line + 1
+#     else:
+#         dots = dots + 1
+
+colors[0] = (0, 0, 0)
+image = np.copy(src)
+for t in range(1, num_labels, 1):
+    x, y, w, h, area = stats[t]
+    cx, cy = centers[t]
+    # 标出中心位置
+    # cv.circle(src_image, (np.int32(cx), np.int32(cy)), 1, (255, 0, 0), 0, 1, 0)
+    # 画出外接矩形
+    cv.rectangle(image, (x, y), (x + w, y + h), colors[t], 1, 8, 0)
+    # cv.putText(src_image, str(t).__add__(":L=").__add__(str(count[t])), (x, y), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 255), 1)
+    cv.putText(image, str(t), (x, y), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 255), 1)
+    print("label index %d, area of the label : %d" % (t, area))
+
+    cv.imshow("colored labels", image)
+    # cv.imwrite("labels.png", image)
+    print("total number : ", num_labels - 1)
+    cv.putText(image, "contact Sum:" + str(num_labels - 1), (30, 960), cv.FONT_HERSHEY_SIMPLEX, .8, (0, 0, 255), 3)
+    # cv.putText(src_image, "dots:" + str(dots), (30, 300), cv.FONT_HERSHEY_SIMPLEX, .8, (0, 0, 255), 2)
+    # cv.putText(src_image, "lines:" + str(line), (30, 330), cv.FONT_HERSHEY_SIMPLEX, .8, (0, 0, 255), 2)
+
+# input = cv.imread("granule.png")
+# connected_components_stats_demo(input)
+cv.waitKey(0)
+cv.destroyAllWindows()

count/length_contact.py

@@ -0,0 +1,43 @@
+# -*- coding: utf-8 -*-
+# @Time : 2020/4/15 上午10:38
+# @Author : LuoLu
+# @FileName: length_contact.py
+# @Software: PyCharm
+# @Github ：https://github.com/luolugithub
+# @E-mail ：[email protected]
+
+import numpy as np
+import cv2 as cv
+
+img = cv.imread('/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/contact_test.png')
+gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+ret, thresh = cv.threshold(gray, 0, 255, 0)
+contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+num_labels, labels_im = cv.connectedComponents(thresh)
+print("num_labels:", num_labels - 1)
+print("len contours :", len(contours))
+count0 = 0
+
+dots = 0
+line = 0
+
+for iterm in range(len(contours)):
+    len_contact = cv.arcLength(contours[iterm], False)
+    print("iterm:" + str(iterm) + "," + "perimeter = " + str(len_contact))
+    if len_contact != 0:
+        if len_contact > 30:
+            line = line + 1
+        else:
+            dots = dots + 1
+# print("count0 contours :", count0)
+# print("jian :", len(contours) - count0)
+print("dots :", dots)
+print("line :", line)
+print(type(img))
+img = np.copy(img)
+cv.putText(img, "dots Sum:" + str(dots), (30, 960), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 255), 1)
+cv.putText(img, "line Sum:" + str(line), (30, 990), cv.FONT_HERSHEY_SIMPLEX, .5, (0, 0, 255), 1)
+cv.imwrite("/home/luolu/PycharmProjects/ParticleDetection/data/yashi_qscan/perimeter/result_contact.png", img)
+cv.imshow("img", img)
+cv.waitKey()
+cv.destroyAllWindows()