def draw_final(self, frame, paper_detection, hand_detection):
hand_masked = image_analysis.apply_hist_mask(frame,
hand_detection.hand_hist)
paper_hand = paper_detection.paper_copy()
self.plot_word_boxes(paper_hand, paper_detection.words)
contours = image_analysis.contours(hand_masked)
if contours is not None and len(contours) > 0:
max_contour = image_analysis.max_contour(contours)
hull = image_analysis.hull(max_contour)
centroid = image_analysis.centroid(max_contour)
defects = image_analysis.defects(max_contour)
if centroid is not None and defects is not None and len(
defects) > 0:
farthest_point = image_analysis.farthest_point(
defects, max_contour, centroid)
if farthest_point is not None:
self.plot_farthest_point(frame, farthest_point)
self.plot_hull(frame, hull)
# self.plot_contours(frame, contours)
# self.plot_defects(frame, defects, max_contour)
# self.plot_centroid(frame, (cx,cy))
self.plot_farthest_point(paper_hand, farthest_point)
point = self.original_point(farthest_point)
paper_detection.update_pointed_locations(point)
self.text = paper_detection.get_most_common_word()
self.plot_text(paper_hand, self.text)
frame_final = np.vstack([paper_hand, frame])
return frame_final
def draw_final(self, frame, paper_detection, hand_detection): hand_masked = image_analysis.apply_hist_mask(frame, hand_detection.hand_hist) paper_hand = paper_detection.paper_copy() self.plot_word_boxes(paper_hand, paper_detection.words) contours = image_analysis.contours(hand_masked) if contours is not None and len(contours) > 0: max_contour = image_analysis.max_contour(contours) hull = image_analysis.hull(max_contour) centroid = image_analysis.centroid(max_contour) defects = image_analysis.defects(max_contour) if centroid is not None and defects is not None and len(defects) > 0: farthest_point = image_analysis.farthest_point(defects, max_contour, centroid) if farthest_point is not None: self.plot_farthest_point(frame, farthest_point) self.plot_hull(frame, hull) # self.plot_contours(frame, contours) # self.plot_defects(frame, defects, max_contour) # self.plot_centroid(frame, (cx,cy)) self.plot_farthest_point(paper_hand, farthest_point) point = self.original_point(farthest_point) paper_detection.update_pointed_locations(point) self.text = paper_detection.get_most_common_word() self.plot_text(paper_hand, self.text) frame_final = np.vstack([paper_hand, frame]) return frame_final
def draw_final(self, frame, paper_detection, hand_detection):
hand_masked = image_analysis.apply_hist_mask(frame, hand_detection.hand_hist)
# paper_hand = paper_detection.paper_copy()
# self.plot_word_boxes(paper_hand, paper_detection.words)
contours = image_analysis.contours(hand_masked)
if contours is not None and len(contours) > 0:
max_contour = image_analysis.max_contour(contours)
hull = image_analysis.hull(max_contour)
centroid = image_analysis.centroid(max_contour)
defects = image_analysis.defects(max_contour)
if centroid is not None and defects is not None and len(defects) > 0:
farthest_point = image_analysis.farthest_point(defects, max_contour, centroid)
if farthest_point is not None:
self.plot_farthest_point(frame, farthest_point)
self.plot_hull(frame, hull)
point = self.original_point(farthest_point)
self.farthest_point = farthest_point
# print(farthest_point)
x, y = farthest_point
y -= 400
# a,b are the top-left coordinate of the rectangle and (c,d) be its width and height.
# to judge a point(x0,y0) is in the rectangle, just to check
# if a < x0 < a+c and b < y0 < b + d
for i in range(9):
#if farthest_point is in any of the rectangles
#then we add that to string
#self.hand_col_nw[i], self.hand_row_nw[i] a, b
#self.hand_col_se[i],self.hand_row_se[i] c, d
# # if i == 0:
# print(i)
# print(self.hand_col_nw[i], self.hand_row_nw[i])
# print(self.hand_col_se[i],self.hand_row_se[i])
# print(x, y)
# print("=================")
if self.hand_col_nw[i] < x and x < self.hand_col_se[i] and self.hand_row_nw[i] < y and y < self.hand_row_se[i]:
print self.pw, i
if len(self.pw) < 1 or self.pw[-1] != str(i):
self.pw += str(i)
frame_final = frame
return frame_final
def get_paper(self, frame):
paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist)
contours = image_analysis.contours(paper_masked)
max_contour = image_analysis.max_contour(contours)
paper = image_analysis.contour_interior(frame, max_contour)
return paper
def get_paper(self, frame): paper_masked = image_analysis.apply_hist_mask(frame, self.paper_hist) contours = image_analysis.contours(paper_masked) max_contour = image_analysis.max_contour(contours) paper = image_analysis.contour_interior(frame, max_contour) return paper
