<filename>test_box.py
import numpy as np
import cv2

def join_box_slow(boxes,min_dist = 10):
	
	if len(boxes) == 0:
		return []

	x1 = boxes[:, 0]
	y1 = boxes[:, 1]
	x2 = boxes[:, 2]
	y2 = boxes[:, 3]

	area = (x2 - x1 + 1) * (y2 - y1 + 1)
	idxs = np.argsort(area)

	result = []

	while len(idxs) > 0:
		i = idxs[-1]

		union = []
		union.append(i)
		for pos in range(0, len(idxs) - 1):
			j = idxs[pos]
			
			#intersezione
			xi1 = max(x1[i] - min_dist, x1[j] - min_dist)
			yi1 = max(y1[i] - min_dist, y1[j] - min_dist)
			xi2 = min(x2[i] + min_dist, x2[j] + min_dist)
			yi2 = min(y2[i] + min_dist, y2[j] + min_dist)

			w = max(0, xi2 - xi1 + 1)
			h = max(0, yi2 - yi1 + 1)

			overlap = float(w * h)

			if overlap > 0:
				union.append(j)

		
		xu1 = min(x1[union])
		yu1 = min(y1[union])
		xu2 = max(x2[union])
		yu2 = max(y2[union])

		result.append((xu1,yu1,xu2,yu2))
		
		idxs = np.delete(idxs, union)

	
	return result


def non_max_suppression(boxes, probs=None, overlapThresh=0.3):
	# if there are no boxes, return an empty list
	if len(boxes) == 0:
		return []

	# if the bounding boxes are integers, convert them to floats -- this
	# is important since we'll be doing a bunch of divisions
	if boxes.dtype.kind == "i":
		boxes = boxes.astype("float")

	# initialize the list of picked indexes
	pick = []

	# grab the coordinates of the bounding boxes
	x1 = boxes[:, 0]
	y1 = boxes[:, 1]
	x2 = boxes[:, 2]
	y2 = boxes[:, 3]

	# compute the area of the bounding boxes and grab the indexes to sort
	# (in the case that no probabilities are provided, simply sort on the
	# bottom-left y-coordinate)
	area = (x2 - x1 + 1) * (y2 - y1 + 1)
	idxs = y2

	# if probabilities are provided, sort on them instead
	if probs is not None:
		idxs = probs

	# sort the indexes
	idxs = np.argsort(idxs)

	# keep looping while some indexes still remain in the indexes list
	while len(idxs) > 0:
		# grab the last index in the indexes list and add the index value
		# to the list of picked indexes
		last = len(idxs) - 1
		i = idxs[last]
		pick.append(i)

		# find the largest (x, y) coordinates for the start of the bounding
		# box and the smallest (x, y) coordinates for the end of the bounding
		# box
		xx1 = np.maximum(x1[i], x1[idxs[:last]])
		yy1 = np.maximum(y1[i], y1[idxs[:last]])
		xx2 = np.minimum(x2[i], x2[idxs[:last]])
		yy2 = np.minimum(y2[i], y2[idxs[:last]])

		# compute the width and height of the bounding box
		w = np.maximum(0, xx2 - xx1 + 1)
		h = np.maximum(0, yy2 - yy1 + 1)

		# compute the ratio of overlap
		overlap = (w * h) / area[idxs[:last]]

		# delete all indexes from the index list that have overlap greater
		# than the provided overlap threshold
		idxs = np.delete(idxs, np.concatenate(([last],
			np.where(overlap > overlapThresh)[0])))

	# return only the bounding boxes that were picked
	return boxes[pick].astype("int")




boxes = np.array([
	(12, 84, 140, 212),
	(151, 223, 191, 273)
	])


orig = np.zeros((500,500,3), np.uint8)
orig.fill(255)

for (startX, startY, endX, endY) in boxes:
		cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)


pick = join_box_slow(boxes)
print("[x] after applying non-maximum, %d bounding boxes" % (len(pick)))

for (startX, startY, endX, endY) in pick:
	cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)

cv2.imshow('test image', orig)

cv2.waitKey(0)
cv2.destroyAllWindows()
<filename>test_box.py
import numpy as np
import cv2

def join_box_slow(boxes,min_dist = 10):
	
	if len(boxes) == 0:
		return []

	x1 = boxes[:, 0]
	y1 = boxes[:, 1]
	x2 = boxes[:, 2]
	y2 = boxes[:, 3]

	area = (x2 - x1 + 1) * (y2 - y1 + 1)
	idxs = np.argsort(area)

	result = []

	while len(idxs) > 0:
		i = idxs[-1]

		union = []
		union.append(i)
		for pos in range(0, len(idxs) - 1):
			j = idxs[pos]
			
			#intersezione
			xi1 = max(x1[i] - min_dist, x1[j] - min_dist)
			yi1 = max(y1[i] - min_dist, y1[j] - min_dist)
			xi2 = min(x2[i] + min_dist, x2[j] + min_dist)
			yi2 = min(y2[i] + min_dist, y2[j] + min_dist)

			w = max(0, xi2 - xi1 + 1)
			h = max(0, yi2 - yi1 + 1)

			overlap = float(w * h)

			if overlap > 0:
				union.append(j)

		
		xu1 = min(x1[union])
		yu1 = min(y1[union])
		xu2 = max(x2[union])
		yu2 = max(y2[union])

		result.append((xu1,yu1,xu2,yu2))
		
		idxs = np.delete(idxs, union)

	
	return result


def non_max_suppression(boxes, probs=None, overlapThresh=0.3):
	# if there are no boxes, return an empty list
	if len(boxes) == 0:
		return []

	# if the bounding boxes are integers, convert them to floats -- this
	# is important since we'll be doing a bunch of divisions
	if boxes.dtype.kind == "i":
		boxes = boxes.astype("float")

	# initialize the list of picked indexes
	pick = []

	# grab the coordinates of the bounding boxes
	x1 = boxes[:, 0]
	y1 = boxes[:, 1]
	x2 = boxes[:, 2]
	y2 = boxes[:, 3]

	# compute the area of the bounding boxes and grab the indexes to sort
	# (in the case that no probabilities are provided, simply sort on the
	# bottom-left y-coordinate)
	area = (x2 - x1 + 1) * (y2 - y1 + 1)
	idxs = y2

	# if probabilities are provided, sort on them instead
	if probs is not None:
		idxs = probs

	# sort the indexes
	idxs = np.argsort(idxs)

	# keep looping while some indexes still remain in the indexes list
	while len(idxs) > 0:
		# grab the last index in the indexes list and add the index value
		# to the list of picked indexes
		last = len(idxs) - 1
		i = idxs[last]
		pick.append(i)

		# find the largest (x, y) coordinates for the start of the bounding
		# box and the smallest (x, y) coordinates for the end of the bounding
		# box
		xx1 = np.maximum(x1[i], x1[idxs[:last]])
		yy1 = np.maximum(y1[i], y1[idxs[:last]])
		xx2 = np.minimum(x2[i], x2[idxs[:last]])
		yy2 = np.minimum(y2[i], y2[idxs[:last]])

		# compute the width and height of the bounding box
		w = np.maximum(0, xx2 - xx1 + 1)
		h = np.maximum(0, yy2 - yy1 + 1)

		# compute the ratio of overlap
		overlap = (w * h) / area[idxs[:last]]

		# delete all indexes from the index list that have overlap greater
		# than the provided overlap threshold
		idxs = np.delete(idxs, np.concatenate(([last],
			np.where(overlap > overlapThresh)[0])))

	# return only the bounding boxes that were picked
	return boxes[pick].astype("int")




boxes = np.array([
	(12, 84, 140, 212),
	(151, 223, 191, 273)
	])


orig = np.zeros((500,500,3), np.uint8)
orig.fill(255)

for (startX, startY, endX, endY) in boxes:
		cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 0, 255), 2)


pick = join_box_slow(boxes)
print("[x] after applying non-maximum, %d bounding boxes" % (len(pick)))

for (startX, startY, endX, endY) in pick:
	cv2.rectangle(orig, (startX, startY), (endX, endY), (0, 255, 0), 2)

cv2.imshow('test image', orig)

cv2.waitKey(0)
cv2.destroyAllWindows()
#intersezione # if there are no boxes, return an empty list # if the bounding boxes are integers, convert them to floats -- this # is important since we'll be doing a bunch of divisions # initialize the list of picked indexes # grab the coordinates of the bounding boxes # compute the area of the bounding boxes and grab the indexes to sort # (in the case that no probabilities are provided, simply sort on the # bottom-left y-coordinate) # if probabilities are provided, sort on them instead # sort the indexes # keep looping while some indexes still remain in the indexes list # grab the last index in the indexes list and add the index value # to the list of picked indexes # find the largest (x, y) coordinates for the start of the bounding # box and the smallest (x, y) coordinates for the end of the bounding # box # compute the width and height of the bounding box # compute the ratio of overlap # delete all indexes from the index list that have overlap greater # than the provided overlap threshold # return only the bounding boxes that were picked