202 lines
6.9 KiB
Python
202 lines
6.9 KiB
Python
from __future__ import division
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from __future__ import print_function
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from collections import deque
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import cv2
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import numpy as np
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class FieldFinder(object):
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def __init__(self, hsv_lower, hsv_upper):
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self.hsv_lower = tuple(hsv_lower)
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self.hsv_upper = tuple(hsv_upper)
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def primary_mask(self, frame):
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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blurred = cv2.GaussianBlur(hsv, (25, 25), 20)
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thr = cv2.inRange(blurred, tuple(self.hsv_lower), tuple(self.hsv_upper))
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thr = cv2.erode(thr, None, iterations=6)
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thr = cv2.dilate(thr, None, iterations=10)
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return thr
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def find(self, frame):
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thr = self.primary_mask(frame)
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cnts, _ = cv2.findContours(thr.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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if not cnts:
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return None
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field = max(cnts, key=cv2.contourArea)
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field = cv2.convexHull(field)
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return field
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def draw(self, frame, field):
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if field is not None:
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frame = frame.copy()
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cv2.drawContours(frame, (field,), -1, (0, 0, 255), 2)
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return frame
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def mask_it(self, frame, field, inverse=False):
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if field is not None:
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mask = np.zeros(frame.shape[:2], dtype=np.uint8)
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cv2.drawContours(mask, (field,), -1, 255, -1)
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if inverse:
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mask = cv2.bitwise_not(mask)
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frame = cv2.bitwise_and(frame, frame, mask=mask)
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return frame
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class GoalFinder(object):
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def __init__(self, hsv_lower, hsv_upper):
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self.hsv_lower = tuple(hsv_lower)
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self.hsv_upper = tuple(hsv_upper)
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def primary_mask(self, frame):
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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thr = cv2.inRange(hsv, self.hsv_lower, self.hsv_upper)
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thr = cv2.erode(thr, None, iterations=2)
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thr = cv2.dilate(thr, None, iterations=2)
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return thr
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def goal_similarity(self, contour):
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hull = cv2.convexHull(contour).squeeze()
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len_h = cv2.arcLength(hull, True)
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# Supporting points of goal contour should lie close to its
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# enclosing convex hull
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distances = np.array([[np.sqrt(np.sum(point**2)) for point in node]
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for node in contour - hull])
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min_dist = np.array([d.min() for d in distances])
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shape_sim = min_dist.sum() / len_h
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# Wild assumption that the area of the goal is rather small
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# compared to its enclosing convex hull
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area_c = cv2.contourArea(contour)
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area_h = cv2.contourArea(hull)
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area_sim = area_c / area_h
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# Final similarity score is just the sum of both
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final_score = shape_sim + area_sim
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print('Goal candidate:', shape_sim, area_sim, final_score)
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return final_score
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def find(self, frame):
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thr = self.primary_mask(frame)
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cnts, _ = cv2.findContours(thr, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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cnts.sort(key=cv2.contourArea, reverse=True)
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top_x = 6
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cnts = cnts[:top_x]
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epsilon = [0.01 * cv2.arcLength(cnt, True) for cnt in cnts]
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# Approximate resulting contours with simpler lines
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cnts = [cv2.approxPolyDP(cnt, eps, True)
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for cnt, eps in zip(cnts, epsilon)]
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# Goal needs normally 8 points for perfect approximation
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# But with 6 can also be approximated
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good_cnts = [cnt for cnt in cnts if 6 <= cnt.shape[0] <= 9
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and not cv2.isContourConvex(cnt)]
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if not good_cnts:
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return None
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similarities = [self.goal_similarity(cnt) for cnt in good_cnts]
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best = min(similarities)
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print('Final goal score:', best)
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print()
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if best > 0.45:
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return None
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# Find the contour with the shape closest to that of the goal
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goal = good_cnts[similarities.index(best)]
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return goal
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def left_right_post(self, contour):
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return contour[:,0].min(), contour[:,0].max()
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def goal_center(self, contour):
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l, r = self.left_right_post(contour)
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print('Left goal post:', l,
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'Right goal post:', r)
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return (l + r) / 2
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def draw(self, frame, goal):
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if goal is not None:
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frame = frame.copy()
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cv2.drawContours(frame, (goal,), -1, (0, 255, 0), 2)
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return frame
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class BallFinder(object):
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def __init__(self, hsv_lower, hsv_upper, min_radius):
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self.hsv_lower = tuple(hsv_lower)
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self.hsv_upper = tuple(hsv_upper)
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self.min_radius = min_radius
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self.history = deque(maxlen=64)
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def find(self, frame):
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hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
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# construct a mask for the color, then perform a series of
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# dilations and erosions to remove any small blobs left in the mask ?
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mask = cv2.inRange(hsv, self.hsv_lower, self.hsv_upper)
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# mask = cv2.erode(mask, None, iterations=2)
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# mask = cv2.dilate(mask, None, iterations=2)
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cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)[-2]
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if len(cnts) == 0:
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print('No red contours')
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self.history.appendleft(None)
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return None
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# find the largest contour in the mask, then use it to compute
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# the minimum enclosing circle and centroid
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c = max(cnts, key=cv2.contourArea)
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(x, y), radius = cv2.minEnclosingCircle(c)
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min_radius_abs = self.min_radius * frame.shape[0]
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if radius < min_radius_abs:
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print('Radius:', radius, 'Min radius:', min_radius_abs)
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self.history.appendleft(None)
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return None
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M = cv2.moments(c)
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try:
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center = int(M["m10"] / M["m00"]), int(M["m01"] / M["m00"])
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except ZeroDivisionError:
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# It's weird but happened yeah
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self.history.append(None)
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return None
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self.history.appendleft((center, int(radius)))
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print('Ball:', center, radius)
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return center, int(radius)
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def draw(self, frame, ball):
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if ball is not None:
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frame = frame.copy()
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center, radius = ball
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cv2.circle(frame, center, radius, (255, 255, 0), 1)
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return frame
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# cv2.circle(frame, center, 5, (0, 255, 0), -1)
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# loop over the set of tracked points
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# for i in range(1, len(self.history)):
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# if either of the tracked points are None, ignore them
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# if self.history[i - 1] is None or self.history[i] is None:
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# continue
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# otherwise, compute the thickness of the line and
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# draw the connecting lines
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# center_now = self.history[i - 1][0]
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# center_prev = self.history[i][0]
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# thickness = int((64 / (i + 1))**0.5 * 2.5)
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# cv2.line(frame, center_now, center_prev, (0, 255, 0), thickness)
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