Vision test scripts

tuto_vision/test/extract_regions.py

+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+from skimage.measure import label, regionprops
+import math
+
+filename = './mask.jpg'
+
+image = cv2.imread(filename)
+
+# Passage en niveau de gris
+if(filename != './mask.jpg'):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+###### extration des régions avec la lib skimage
+
+# Binarisation de l'image
+if(filename != './mask.jpg'):
+    ret, thresh = cv2.threshold(gray, 127, 255, 1)
+else:
+    thresh = image
+
+cv2.imshow("image seuillée",thresh)
+cv2.waitKey(0)
+
+# extraction des régions et des propriétés des régions
+label_img = label(thresh)
+regions = regionprops(label_img)
+print(f'{regions=}')
+cv2.waitKey(0)
+
+# affichage des régions et des boites englobantes
+fig, ax = plt.subplots()
+ax.imshow(thresh, cmap=plt.cm.gray)
+
+for props in regions:
+    y0, x0 = props.centroid
+    orientation = props.orientation
+    x1 = x0 + math.cos(orientation) * 0.5 * props.minor_axis_length
+    y1 = y0 - math.sin(orientation) * 0.5 * props.minor_axis_length
+    x2 = x0 - math.sin(orientation) * 0.5 * props.major_axis_length
+    y2 = y0 - math.cos(orientation) * 0.5 * props.major_axis_length
+
+    ax.plot((x0, x1), (y0, y1), '-r', linewidth=2.5)
+    ax.plot((x0, x2), (y0, y2), '-r', linewidth=2.5)
+    ax.plot(x0, y0, '.g', markersize=15)
+
+    minr, minc, maxr, maxc = props.bbox
+    bx = (minc, maxc, maxc, minc, minc)
+    by = (minr, minr, maxr, maxr, minr)
+    ax.plot(bx, by, '-b', linewidth=2.5)
+
+ax.axis((0, 600, 600, 0))
+plt.show()
+
+cv2.waitKey(0)
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tuto_vision/mask_tuner.py → tuto_vision/test/mask_tuner.py

@@ -37,15 +37,28 @@ cv2.setMouseCallback('Camera', souris)
 hsv_px = [0,0,0]
 
 # Creating morphological kernel
-kernel = np.ones((3, 3), np.uint8)
+erodeKernel = np.ones((3, 3), np.uint8)
+dilateKernel = np.ones((3, 3), np.uint8)
 
 while True:
     ret, frame=cap.read()
+    base_img = frame.copy()
     image=cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
-    mask=cv2.inRange(image, lo, hi)
-    mask=cv2.erode(mask, kernel, iterations=1)
-    mask=cv2.dilate(mask, kernel, iterations=1)
+
+    mask = image
+
+    # Seuillage par colorimétrie
+    mask=cv2.inRange(mask, lo, hi)
+    beforeErosion = mask
+
+    # Réduction du bruit
+    mask=cv2.erode(mask, erodeKernel, iterations=4)
+    mask=cv2.dilate(mask, dilateKernel, iterations=4)
+
+    # Application du masque
     image2=cv2.bitwise_and(frame, frame, mask= mask)
+
+
     cv2.putText(frame, "Couleur: {:d}".format(color), (10, 30), cv2.FONT_HERSHEY_DUPLEX, 1, color_info, 1, cv2.LINE_AA)
 
     # Affichage des composantes HSV sous la souris sur l'image
@@ -54,11 +67,29 @@ while True:
     cv2.putText(frame, "px HSV: "+pixel_hsv, (10, 260),
                font, 1, (255, 255, 255), 1, cv2.LINE_AA)
     
+    # Segmentation
+    ## Min enclosing circle method
+    elements=cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]
+    if len(elements) > 0:
+        c=max(elements, key=cv2.contourArea)
+        ((x, y), rayon)=cv2.minEnclosingCircle(c)
+        if rayon>30:
+            cv2.circle(frame, (int(x), int(y)), int(rayon), color_info, 2)
+            cv2.circle(frame, (int(x), int(y)), 5, color_info, 10)
+            cv2.line(frame, (int(x), int(y)), (int(x)+150, int(y)), color_info, 2)
+            cv2.putText(frame, "Objet !!!", (int(x)+10, int(y) -10), cv2.FONT_HERSHEY_DUPLEX, 1, color_info, 1, cv2.LINE_AA)
+
+
+
+
     cv2.imshow('Camera', frame)
-    cv2.imshow('image2', image2)
+    cv2.imshow('Before erosion', beforeErosion)
+    # cv2.imshow('image2', image2)
     cv2.imshow('Mask', mask)
 
     if cv2.waitKey(1)&0xFF==ord('q'):
         break
+    cv2.imwrite('mask.jpg', mask)
+    cv2.imwrite('rgb.jpg', base_img)
 cap.release()
 cv2.destroyAllWindows()
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