Suggest

approximate_circle.py

@@ -0,0 +1,50 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("images/CIMG4555.JPG")
+
+gau = cv2.GaussianBlur(img, (21, 21), 0)
+
+gray = cv2.cvtColor(gau, cv2.COLOR_BGR2GRAY)
+
+ret, th = cv2.threshold(gray, 230, 255, cv2.THRESH_BINARY)
+
+src = np.array(th, dtype="float32")
+
+cv2.imwrite("images/CIMG2478.JPG", src)
+
+if len(src.shape) == 3:
+    height, width, channels = src.shape[:3]
+else:
+    height, width = src.shape[:2]
+
+    channels = 1
+
+    print("dtype(src) " + str(src.dtype))
+
+
+image, contours, hierarchy = cv2.findContours(
+    th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+cnt = contours[0]
+
+print("dtype(cnt): " + str(cnt.dtype))
+
+cnt_ = np.array(cnt, dtype="float32")
+
+print("dtype(cnt_): " + str(cnt_.dtype))
+
+(x, y), radius = cv2.minEnclosingCircle(cnt)
+center = (int(x), int(y))
+radius = int(radius)
+
+
+img = cv2.circle(img, center, 200, (0, 0, 255), 2)
+
+cv2.imwrite("images/CIMG4555_1.JPG", img)
+
+print(center)
+print(radius)
+
+
+print("OK")

canny.py

@@ -0,0 +1,7 @@
+import cv2
+
+img = cv2.imread("images/point.JPG")
+gray = cv2.cvtColor(img, cv2.COLOR_RGBA2GRAY)
+dst = cv2.Canny(gray, 350, 600)
+
+cv2.imwrite("images/point.JPG", dst)

ellispe_fitting.py

@@ -0,0 +1,64 @@
+import cv2
+import numpy as np
+
+# 画像の読み込み
+img = cv2.imread("images/volto_copy.png")
+
+
+# 塗りつぶし
+height, width, channels = img.shape[:3]
+
+print("width: " + str(width))
+print("height: " + str(height))
+
+cv2.rectangle(img, (1, height), (width, 1), (0, 0, 0),
+              thickness=30, lineType=cv2.LINE_4)
+
+cv2.imwrite("images/CIMG4192.JPG", img)
+
+
+# ガウスのぼかし処理
+gau = cv2.GaussianBlur(img, (5, 5), 0)
+
+# グレースケール化
+gray = cv2.cvtColor(gau, cv2.COLOR_BGR2GRAY)
+
+# ２値化
+ret, th = cv2.threshold(gray, 0, 255, cv2.THRESH_OTSU)
+cv2.imwrite("images/volto_out2.png", th)
+
+#
+src = np.array(th, dtype="float32")
+
+
+if len(src.shape) == 3:
+    height, width, channels = src.shape[:3]
+else:
+    height, width = src.shape[:2]
+
+    channels = 1
+
+    print("dtype(src) " + str(src.dtype))
+
+# 輪郭抽出
+image, contours, hierarchy = cv2.findContours(
+    th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+cnt = contours[0]
+
+print("dtype(cnt): " + str(cnt.dtype))
+
+cnt_ = np.array(cnt, dtype="float32")
+
+print("dtype(cnt_): " + str(cnt_.dtype))
+
+# 楕円フィッティング
+ellipse = cv2.fitEllipse(cnt)
+img = cv2.ellipse(img, ellipse, (0, 255, 0), 2)
+
+# 焦点
+print(ellipse)
+
+cv2.imwrite("images/volto_out1.png", img)
+
+print("OK")

輪郭検出1.py → extract_contour1.py

@@ -3,15 +3,15 @@
 import numpy as np
 from matplotlib.patches import Polygon
 
-img = cv2.imread("C:\\Users\\tuzuk\\Desktop\\point.JPG")
+img = cv2.imread("images/point.JPG")
 
 gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
 
-ret,thresh1 = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
+ret, thresh1 = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
+
+contours = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
-contours =  cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
-
 def draw_contours(ax, img, contours):
     ax.imshow(img)
     ax.axis('off')
@@ -21,7 +21,8 @@ def draw_contours(ax, img, contours):
         ax.add_patch(Polygon(cnt, color='b', fill=None, lw=2))
         # 輪郭の点を描画する。
         ax.plot(cnt[:, 0], cnt[:, 1], 'ro', mew=0, ms=4)
-
+
+
 fig, ax = plt.subplots(figsize=(6, 6))
 draw_contours(ax, img, contours)
-plt.show()
+plt.show()

extract_contour2.py

@@ -0,0 +1,12 @@
+import cv2
+
+img = cv2.imread("images/point.JPG")
+
+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+contours = cv2.findContours(gray, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+
+cnt = contours[0]
+result = cv2.drawContours(img, [cnt], 0, (0, 255, 0), 3)
+
+cv2.imwrite("images/point_canny.JPG", result)

line_fitting.py

@@ -0,0 +1,63 @@
+import cv2
+import numpy as np
+
+img = cv2.imread("images/hand_gau_out1.JPG")
+
+# 塗りつぶし
+height, width, channels = img.shape[:3]
+
+print("width: " + str(width))
+print("height: " + str(height))
+
+cv2.rectangle(img, (1, height), (width, 1), (0, 0, 0),
+              thickness=100, lineType=cv2.LINE_4)
+
+cv2.imwrite("images/hand_gau_draw.JPG", img)
+
+
+# ガウスのぼかし処理
+gau = cv2.GaussianBlur(img, (99, 99), 0)
+
+# グレースケール化
+gray = cv2.cvtColor(gau, cv2.COLOR_BGR2GRAY)
+
+# ２値化
+ret, th = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY)
+cv2.imwrite("images/hand_gau_out2.JPG", th)
+
+src = np.array(th, dtype="float32")
+
+# cv2.imwrite("images/CIMG2478.JPG", src)
+
+if len(src.shape) == 3:
+    height, width, channels = src.shape[:3]
+else:
+    height, width = src.shape[:2]
+
+    channels = 1
+
+    print("dtype(src) " + str(src.dtype))
+
+
+image, contours, hierarchy = cv2.findContours(
+    th, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+cnt = contours[0]
+
+print("dtype(cnt): " + str(cnt.dtype))
+
+cnt_ = np.array(cnt, dtype="float32")
+
+print("dtype(cnt_): " + str(cnt_.dtype))
+
+rows, cols = img.shape[:2]
+[vx, vy, x, y] = cv2.fitLine(cnt, cv2.DIST_L2, 0, 0.01, 0.01)
+lefty = int((-x*vy/vx) + y)
+righty = int(((cols-x)*vy/vx)+y)
+img = cv2.line(img, (cols-1, righty), (0, lefty), (0, 255, 0), 2)
+
+
+print(lefty)
+print(righty)
+
+cv2.imwrite("images/hand_gau_out1.JPG", img)