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import cv2
import numpy as np
import math
class ContourAnalyzer:
def __init__(self, min_area_threshold=5):
self.min_area_threshold = min_area_threshold
def find_thickest_contour(self, contours, binary_image):
max_width = 0
thickest_section = None
thickest_points = None
distance_transforms = []
for contour in contours:
if cv2.contourArea(contour) > self.min_area_threshold:
mask = np.zeros_like(binary_image)
cv2.drawContours(mask, [contour], 0, 255, thickness=cv2.FILLED)
distance_transform = cv2.distanceTransform(mask, cv2.DIST_L2, cv2.DIST_MASK_PRECISE)
distance_transforms.append(distance_transform)
_, _, _, max_loc = cv2.minMaxLoc(distance_transform)
width = 2 * distance_transform[max_loc[1], max_loc[0]]
if width > max_width:
max_width = width
thickest_section = contour
thickest_points = max_loc
return max_width, thickest_section, thickest_points, distance_transforms
def find_contours(self, binary_image):
contours, _ = cv2.findContours(binary_image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# for debugging
print("Number of contours:", len(contours))
max_width, thickest_section, thickest_points, distance_transforms = self.find_thickest_contour(contours, binary_image)
return max_width, thickest_section, thickest_points, distance_transforms
@staticmethod
def calculate_width(y, x, pixel_width, calibration_factor, distance):
# angle = math.atan2(y, x)
angle=1
width = angle * pixel_width * distance * calibration_factor
return width
def draw_circle_on_image(self, image, center, radius, color=(57, 255, 20), thickness=-1):
cv2.circle(image, center, radius, color, thickness)
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