def taskDistance():
# if 'access' not in session:
# return redirect('/')
latitude = request.args.get('lat')
longitude = request.args.get('lng')
return jsonify(getDistance(gmaps, start_coords, latitude, longitude)), 200
def findClosestCont(conts, point):
minDist = 10000
minCnt = None
for cnt in conts:
for p in cnt:
# import pdb; pdb.set_trace()
dist = helpers.getDistance(point, p)
if dist < minDist:
minDist = dist
minCnt = cnt
return minCnt
def findClosestCont(conts, point):
minDist = 10000
minCnt = None
for cnt in conts:
for p in cnt:
# import pdb; pdb.set_trace()
dist = helpers.getDistance(point, p)
if dist < minDist:
minDist = dist
minCnt = cnt
return minCnt
def test_ytick_coordinates(self):
"""
Setup:
- Input Test Image (Already done)
- Corners
Assertion:
- Check if the calculated 1st tick point is approximately correct
Things to Find:
- Corner Points
- Actual Coordinates of the 1st tick point
"""
self.plot.corners = self.corners
self.plot.findTicks()
ticks = self.plot.y_ticks
assert helper.getDistance(ticks[1], self.first_ytick) < 5
def getImageDistances(img, name):
print('')
# find center
centerX = img.shape[0] // 2
centerY = img.shape[1] // 2
cloneImg = img.copy()
# divide circle on the 12 sections
for i, angle in enumerate(np.arange(0, 2 * math.pi, math.pi / 6)):
distances = []
# rotate image to current angle
cloneImg = imutils.rotate(img, angle=(i - 3) * 30)
# draw center
cv.circle(cloneImg, (centerX, centerY), 2, (255, 0, 0), 2)
distances = []
# use 2 grad as one step
for a in np.arange(0, math.pi / 6, math.pi / 90):
# was red line or not
innerPoint = None
wasEnd = True
# get line equalation
endX = centerX + int(math.cos(a) * cloneImg.shape[0])
endY = centerY + int(math.sin(a) * cloneImg.shape[1])
eq = helpers.getEq((centerX, centerY), (endX, endY))
endX = min(endX, cloneImg.shape[0] - 1)
endX = max(endX, 0)
# get range
maxX = max(centerX, endX)
minX = min(centerX, endX)
for x in range(minX, maxX):
y = int(eq(x))
if y >= cloneImg.shape[1]:
continue
# get cell value
cell = cloneImg[y, x]
# it's black point
if helpers.isBlack(cell) and not innerPoint:
innerPoint = [x, y]
elif not helpers.isBlack(cell) and innerPoint:
# it's out point
outPoint = [x, y]
# get distance
d = helpers.getDistance(innerPoint, outPoint)
# too tiny
if d < 3:
continue
# add distance
distances.append(d)
# draw line
cv.line(cloneImg, (innerPoint[0], innerPoint[1]),
(outPoint[0], outPoint[1]), colors[i], 1)
break
else:
wasEnd = False
if not wasEnd and innerPoint:
outPoint = [
cloneImg.shape[0] - 1,
int(eq(cloneImg.shape[0] - 1))
]
# get distance
d = helpers.getDistance(innerPoint, outPoint)
# too tiny
if d < 3:
continue
# add distance
distances.append(d)
# draw line
cv.line(cloneImg, (innerPoint[0], innerPoint[1]),
(outPoint[0], outPoint[1]), colors[i], 1)
cv.imshow(str(f"{name}_{i}"), cloneImg)
print(f"Distance for {name} section {i+1} = {np.average(distances)}")
print('')
def main():
photos = helpers.getImages('./in')
for name, img in photos.items():
# find center
centerX = img.shape[0] // 2
centerY = img.shape[1] // 2
cloneImg = img.copy()
outContours = []
innerCountours = []
cv.circle(cloneImg, (centerX, centerY), 2, (255, 0, 0), 2)
for i, angle in enumerate(np.arange(0, 2 * math.pi, math.pi / 6)):
eY = centerY + int(math.sin(angle) * img.shape[1] // 2)
eX = centerX + int(math.cos(angle) * img.shape[0] // 2)
#cv.line(img, (centerX, centerY), (x, y), (0, 255, 255), 1)
distances = []
for a in np.arange(angle, angle + math.pi / 6, math.pi / 180):
innerPoint = None
endX = centerX + int(math.cos(a) * img.shape[0])
endY = centerY + int(math.sin(a) * img.shape[1])
eq = helpers.getEq((centerX, centerY), (endX, endY))
endX = min(endX, img.shape[0] - 1)
endX = max(endX, 0)
maxX = max(centerX, endX)
minX = min(centerX, endX)
for x in range(minX, maxX):
y = int(eq(x))
if y < 0 or y >= img.shape[1]:
continue
cell = img[y, x]
if cell[0] == 0 and cell[1] == 0 and cell[2] == 255:
if not innerPoint:
innerPoint = [x, y]
else:
outPoint = [x, y]
d = helpers.getDistance(innerPoint, outPoint)
distances.append(d)
cv.line(cloneImg, (innerPoint[0], innerPoint[1]),
(x, y), colors[i], 1)
break
avgDistance = np.average(distances)
print(f"Distance for {name} section {i+1} = {avgDistance}")
#cv.line(img, (centerX, centerY), (eX, eY), (0, 255, 255), 1)
cv.imshow(str(name), cloneImg)
cv.waitKey(0)
cv.destroyAllWindows()