def extract_shape_contours(image, threshold=120, show=True):
img = copy.copy(image)
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
gray_flat = gray[np.isfinite(gray)]
black, white = np.min(gray_flat), np.max(gray_flat)
if show:
plt.figure('Gray Image and Histogram')
plt.subplot(1, 2, 1)
plt.imshow(img)
plt.title('Gray Image')
plt.subplot(1, 2, 2)
plt.hist(gray_flat,
bins=np.linspace(black, white, 20),
histtype='step')
plt.xlim(black, white)
plt.title('Gray Histogram')
plt.xlabel('Value')
plt.ylabel('Count')
plt.show()
gray_mask = np.zeros_like(gray)
gray_mask[gray < threshold] = 250
gray_mask[-20:-1, :] = 0
gray_mask[:, 0:30] = 0
gray_mask[:, -20:-1] = 0
edged, edg_img, cnts, hierarchy = ivt.extract_contours(
gray_mask,
min_thresh=60,
max_thresh=250,
blur=3,
dilate=1,
erode=1,
cnt_mode=cv2.RETR_EXTERNAL)
if show:
plt.figure("Extracted Shapes")
plt.subplot(1, 2, 1)
plt.imshow(gray_mask)
plt.subplot(1, 2, 2)
plt.imshow(edged)
plt.show()
cv2.imwrite("edged_img.jpg", edged)
cv2.imwrite("gray_mask.jpg", gray_mask)
return cnts, hierarchy
def find_spoon2(image, show=True):
img = copy.copy(image)
CAL_PARAM = {'thresh': [75, 100], 'radius': [25, 35]}
CROP_RADIUS = 90
PADDING = 30
circles, cimg = ivt.find_circles2(copy.copy(img),
2,
param=CAL_PARAM,
blur=1,
show=False)
print "CIRCLES: ", circles
empty_cup_centre, empty_cup_id = ivt.farthest_node(
np.array([180, 185]),
np.array([[circles[0][0][0], circles[0][0][1]],
[circles[0][1][0], circles[0][1][1]]]))
print "EMPTY_MUG_DISTANCE: ", np.array(empty_cup_centre)
empty_circles = np.array([[circles[0][empty_cup_id]]])
spoon_circles = np.array([[circles[0][1 - empty_cup_id]]])
sx, sy = int(spoon_circles[0][0][0]), int(spoon_circles[0][0][1])
print "SPOON_MUG_WORLD_CENTRE: ", sx, sy
#img_3b = ivt.black_out(copy.copy(crop_task_img_3), [180,-1,0,-1])
img_3b = copy.copy(img)
img_3b[sy - CROP_RADIUS + PADDING:sy + CROP_RADIUS - PADDING,
sx - CROP_RADIUS + PADDING:sx + CROP_RADIUS - PADDING] = [0, 0, 0]
print "EMPTY_CUP_WORLD: ", empty_circles
ex, ey, er = int(empty_circles[0][0][0]), int(
empty_circles[0][0][1]), int(empty_circles[0][0][-1] + 1)
r, g, b = cv2.split(img)
img_3a = copy.copy(img)
img_3a[ey - er:ey + er, ex - er:ex + er] = [r.mean(), g.mean(), b.mean()]
img_3a = img_3a[sy - CROP_RADIUS:sy + CROP_RADIUS,
sx - CROP_RADIUS:sx + CROP_RADIUS]
mug_centre = np.array([[sy, sx]])
cropped_mug_centre = np.array([[CROP_RADIUS, CROP_RADIUS]])
print mug_centre
print cropped_mug_centre
edged, edg_img, cnts, hierarchy = ivt.extract_contours(
copy.copy(img_3a),
min_thresh=65,
max_thresh=240,
blur=5,
dilate=3,
erode=2,
cnt_mode=cv2.RETR_TREE)
minsize = 0
mindistance = 190
box_minsize = 0
minperi = 40
show_img = copy.copy(img)
show_img = cv2.cvtColor(show_img, cv2.COLOR_RGB2GRAY)
show_img = cv2.cvtColor(show_img, cv2.COLOR_GRAY2RGB)
fnode = np.array([CROP_RADIUS, CROP_RADIUS])
for cnt in cnts:
peri = cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, 0.03 * peri, True)
current_outer_contour = []
for points in approx:
current_outer_contour.append(points[0])
distance = cdist(np.array(cropped_mug_centre), current_outer_contour)
print "CURRENT", np.shape(current_outer_contour)
if cv2.contourArea(cnt) < minsize:
print(
"Object at #{} REJECTED because CONTOUR not big enough: ".
format(cnt[0]), cv2.contourArea(cnt))
continue
if peri < minperi:
print(
"Object at #{} REJECTED because PERIMETER not long enough: ".
format(cnt[0]), peri)
continue
if distance[0][0] > mindistance:
print(
"Object at #{} REJECTED because not CLOSE ENOUGH: ".format(
cnt[0]), distance[0][0])
continue
#mindistance = distance[0][0]
box = ivt.extract_minBox(cnt)
box_area = cv2.contourArea(np.array([box]))
#box_area = abs((box[0][0]-box[2][0])*(box[1][1]-box[0][1]))
if box_area < box_minsize:
print(
"Object at #{} REJECTED because BOX not big enough: ".format(
cnt[0]), box_area)
continue
print("Object at #{} ACCEPTED: ".format(cnt[0]))
print " Contour Area: ", cv2.contourArea(cnt)
print " Perimeter: ", peri
print " Distance: ", distance[0][0]
print " Box Area: ", box_area
fnode_test, fnode_test_id = ivt.farthest_node(cropped_mug_centre[0],
current_outer_contour)
# Make edge more accurate
fnode_testa = current_outer_contour[fnode_test_id + 1]
fnode_testb = current_outer_contour[fnode_test_id - 1]
fnode_test_dist = cdist(np.array(cropped_mug_centre),
np.array([fnode_test]))
fnode_add = fnode_test
fnode_num = 1
if fnode_test_dist - cdist(np.array(cropped_mug_centre),
np.array([fnode_testa])) < 2:
print "HELLO"
print fnode_test_dist - cdist(np.array(cropped_mug_centre),
np.array([fnode_testa]))
fnode_add = fnode_add + fnode_testa
fnode_num = fnode_num + 1
if fnode_test_dist - cdist(np.array(cropped_mug_centre),
np.array([fnode_testb])) < 2:
print "HELLO2"
print fnode_test_dist - cdist(np.array(cropped_mug_centre),
np.array([fnode_testb]))
fnode_add = fnode_add + fnode_testb
fnode_num = fnode_num + 1
fnode_test_mean = fnode_add / fnode_num
print "FNODE MEAN: ", fnode_test_mean,
print "FNODE ORIG: ", fnode_test
fnode_dist = cdist(np.array(cropped_mug_centre),
np.array([fnode_test_mean]))
fnode_dist2 = cdist(np.array(cropped_mug_centre), np.array([fnode]))
print "FNODE_DISTANCES: ", fnode_dist, fnode_dist2,
if fnode_dist > fnode_dist2:
fnode = fnode_test_mean
print "FNODE: ", fnode
print "CIRCLE:", spoon_circles
spoon_mug = np.array([sx, sy])
#spoon_edge = fnode - (fnode-spoon_mug)*
spoon_edge = fnode
if cdist(np.array(cropped_mug_centre), np.array([spoon_edge])) < 60:
print cropped_mug_centre
print spoon_edge
print "VECTOR!!!!", cdist(np.array(cropped_mug_centre),
np.array([spoon_edge]))
fvect = spoon_edge - CROP_RADIUS
unit_fvect = fvect / scipy.linalg.norm(fvect)
spoon_edge = unit_fvect * 70 + 80
print "SPOON_EDGE: ", spoon_edge
spoon_mug = np.array([sx, sy])
fnode_world = np.array(
[sx - CROP_RADIUS + fnode[0], sy - CROP_RADIUS + fnode[1]])
spoon_edge_world = [
sx - CROP_RADIUS + int(spoon_edge[0]),
sy - CROP_RADIUS + int(spoon_edge[1])
]
cv2.circle(show_img, (int(spoon_mug[0]), int(spoon_mug[1])), 3,
(0, 255, 0), 5)
cv2.circle(show_img, (int(fnode_world[0]), int(fnode_world[1])), 3,
(0, 255, 255), 5)
cv2.circle(show_img, (int(spoon_edge_world[0]), int(spoon_edge_world[1])),
3, (255, 0, 255), 5)
if show:
plt.figure("Spoon and Cup", figsize=[9, 9])
plt.subplot(2, 2, 1)
plt.imshow(show_img)
cv2.imwrite("show_img.jpg", show_img)
plt.subplot(2, 2, 2)
plt.imshow(cimg)
plt.subplot(2, 2, 3)
plt.imshow(edged)
plt.subplot(2, 2, 4)
plt.imshow(img_3a)
plt.show()
#print "FNODE: ", fnode
return spoon_mug, spoon_edge_world, empty_cup_centre
def find_spoon(image, show=True):
img = copy.copy(image)
edged, edg_img, cnts, hierarchy = ivt.extract_contours(
copy.copy(img),
min_thresh=25,
max_thresh=200,
blur=3,
dilate=4,
erode=1,
cnt_mode=cv2.RETR_TREE)
CAL_PARAM = {'thresh': [75, 100], 'radius': [25, 35]}
minsize = 1
mindistance = 1000
box_minsize = 1
circles, cimg = ivt.find_circles2(copy.copy(img),
1,
param=CAL_PARAM,
blur=1,
show=False)
show_img = copy.copy(img)
show_img = cv2.cvtColor(show_img, cv2.COLOR_RGB2GRAY)
show_img = cv2.cvtColor(show_img, cv2.COLOR_GRAY2RGB)
for cnt in cnts:
current_outer_contour = []
for points in cnt:
current_outer_contour.append(points[0])
distance = cdist(np.array([[circles[0][0][0], circles[0][0][1]]]),
current_outer_contour)
if cv2.contourArea(cnt) < minsize:
print(
"Object at #{} REJECTED because CONTOUR not big enough: ".
format(cnt[0]), cv2.contourArea(cnt))
continue
if distance[0][0] > mindistance:
print(
"Object at #{} REJECTED because not CLOSE ENOUGH: ".format(
cnt[0]), distance[0][0])
continue
mindistance = distance[0][0]
box = ivt.extract_minBox(cnt)
box_area = abs((box[0][0] - box[2][0]) * (box[1][1] - box[0][1]))
if box_area < box_minsize:
print(
"Object at #{} REJECTED because BOX not big enough: ".format(
cnt[0]), box_area)
continue
fnode = ivt.farthest_node([circles[0][0][0], circles[0][0][1]],
current_outer_contour)
print "CIRCLE:", circles
cv2.circle(show_img, (int(circles[0][0][0]), int(circles[0][0][1])), 3,
(0, 255, 0), 5)
cv2.circle(show_img, (fnode[0][0], fnode[0][1]), 3, (0, 255, 255), 5)
if show:
plt.figure("Spoon and Cup")
plt.subplot(2, 2, 1)
plt.imshow(show_img)
cv2.imwrite("show_img.jpg", show_img)
plt.subplot(2, 2, 2)
plt.imshow(cimg)
plt.subplot(2, 2, 3)
plt.imshow(edged)
plt.show()
#print "FNODE: ", fnode
return circles[0], fnode