def draw_box_on_image(num_hands_detect, score_thresh, scores, boxes, classes, im_width, im_height, image_np,Line_Position2,Orientation):
# Determined using a piece of paper of known length, code can be found in distance to camera
focalLength = 875
# The average width of a human hand (inches) http://www.theaveragebody.com/average_hand_size.php
# added an inch since thumb is not included
avg_width = 4.0
# To more easily differetiate distances and detected bboxes
global a,b
hand_cnt=0
color = None
color0 = (255,0,0)
color1 = (0,50,255)
for i in range(num_hands_detect):
if (scores[i] > score_thresh):
if classes[i] == 1:
id = 'hand'
if classes[i] == 2:
id ='gloved_hand'
avg_width = 3.0 # To compensate bbox size change
#b=1
if i == 0: color = color0
else: color = color1
(left, right, top, bottom) = (boxes[i][1] * im_width, boxes[i][3] * im_width,
boxes[i][0] * im_height, boxes[i][2] * im_height)
p1 = (int(left), int(top))
p2 = (int(right), int(bottom))
dist = distance_to_camera(avg_width, focalLength, int(right-left))
if dist:
hand_cnt=hand_cnt+1
cv2.rectangle(image_np, p1, p2, color , 3, 1)
cv2.putText(image_np, 'hand '+str(i)+': '+id, (int(left), int(top)-5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5 , color, 2)
cv2.putText(image_np, 'confidence: '+str("{0:.2f}".format(scores[i])),
(int(left),int(top)-20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0,255,0), 2)
cv2.putText(image_np, 'distance from camera: '+str("{0:.2f}".format(dist)+' inches'),
(int(im_width*0.65),int(im_height*0.9+30*i)),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 2)
a=alertcheck.drawboxtosafeline(image_np,p1,p2,Line_Position2,Orientation)
if hand_cnt==0 :
b=0
#print(" no hand")
else:
b=1
#print(" hand")
return a,b
def draw_box_on_image(results, im_width, im_height, image_np, Line_Position2, Orientation):
# Determined using a piece of paper of known length, code can be found in distance to camera
focalLength = 875
# The average width of a human hand (inches) http://www.theaveragebody.com/average_hand_size.php
# added an inch since thumb is not included
avg_width = 4.0
# To more easily differetiate distances and detected bboxes
global a,b
hand_cnt=0
color = None
color0 = (255,0,0)
color1 = (0,50,255)
for r in results:
# extract the bounding box and box and predicted class label
box = r.bounding_box.flatten().astype("int")
(startX, startY, endX, endY) = box
label = labels[r.label_id]
# draw the bounding box and label on the image
cv2.rectangle(image_np, (startX, startY), (endX, endY),
(0, 255, 0), 1)
y = startY - 15 if startY - 15 > 15 else startY + 15
text = "{}: {:.2f}%".format(label, r.score * 100)
cv2.putText(image_np, text, (startX, startY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0))
(left, right, top, bottom) = (box[0] , box[2] ,box[1] , box[3] )
p1 = (int(left), int(top))
p2 = (int(right), int(bottom))
dist = distance_to_camera(avg_width, focalLength, int(right - left))
if dist:
hand_cnt = hand_cnt + 1
a = alertcheck.drawboxtosafeline(image_np, p1, p2, Line_Position2, Orientation)
if hand_cnt==0:
b=0
else:
b=1
return a,b
def draw_box_on_image(num_cars_detect, score_thresh, scores, boxes, classes,
im_width, im_height, image_np):
global a, b
car_cnt = 0
color0 = (255, 0, 0)
color1 = (0, 50, 255)
centeriX = tuple()
centeriY = tuple()
for i in range(num_cars_detect):
if (scores[i] > score_thresh):
if classes[i] == 1:
id = 'Car'
if i == 0:
color = color0
(left, right, top,
bottom) = (boxes[i][1] * im_width, boxes[i][3] * im_width,
boxes[i][0] * im_height, boxes[i][2] * im_height)
p1 = (int(left), int(top))
p2 = (int(right), int(bottom))
cv2.rectangle(image_np, p1, p2, color, 3, 1)
cv2.putText(image_np, 'Car ' + str(i) + ': ' + id,
(int(left), int(top) - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.putText(image_np,
'confidence: ' + str("{0:.2f}".format(scores[i])),
(int(left), int(top) - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
(left, right, top,
bottom) = (boxes[i][1] * im_width, boxes[i][3] * im_width,
boxes[i][0] * im_height, boxes[i][2] * im_height)
centeriX = (left / 2, bottom / 2)
else:
color = color1
(left1, right1, top1,
bottom1) = (boxes[i][1] * im_width, boxes[i][3] * im_width,
boxes[i][0] * im_height, boxes[i][2] * im_height)
p3 = (int(left1), int(top1))
p4 = (int(right1), int(bottom1))
cv2.rectangle(image_np, p3, p4, color, 3, 1)
cv2.putText(image_np, 'Car ' + str(i) + ': ' + id,
(int(left1), int(top1) - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, color, 2)
cv2.putText(image_np,
'confidence: ' + str("{0:.2f}".format(scores[i])),
(int(left1), int(top1) - 20),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
centeriY = (left1 / 2, bottom1 / 2)
a = alertcheck.drawboxtosafeline(centeriX, centeriY)
if car_cnt == 0:
b = 0
else:
b = 1
return a, b
def draw_box_on_image(num_hands_detect, score_thresh, scores, boxes, classes,
im_width, im_height, image_np, Line_Position2,
Orientation):
# Determined using a piece of paper of known length, code can be found in distance to camera
focalLength = 875
# The average width of a human hand (inches) http://www.theaveragebody.com/average_hand_size.php
# added an inch since thumb is not included
avg_width = 4.0
# To more easily differetiate distances and detected bboxes
global a, b
hand_cnt = 0
color = None
color0 = (255, 0, 0)
color1 = (0, 50, 255)
for i in range(num_hands_detect):
if (scores[i] > score_thresh):
#no_of_times_hands_detected+=1
#b=b+1
#b=1
#print(b)
if classes[i] == 1:
id = 'HAND'
#b=1
if classes[i] == 2:
id = 'CLOSED HAND'
avg_width = 3.0 # To compensate bbox size change
#b=1
if i == 0: color = color0
else: color = color1
(left, right, top,
bottom) = (boxes[i][1] * im_width, boxes[i][3] * im_width,
boxes[i][0] * im_height, boxes[i][2] * im_height)
p1 = (int(left), int(top))
p2 = (int(right), int(bottom))
def is_close(p1, p2):
"""
# 1. Calculate Euclidean Distance of two points
:param:
p1, p2 = two points for calculating Euclidean Distance
:return:
dst = Euclidean Distance between two 2d points
"""
dst = math.sqrt(p1 + p2)
return dst
def convertBack(x, y, w, h):
"""
# 2. Converts center coordinates to rectangle coordinates
:param:
x, y = midpoint of bounding box
w, h = width, height of the bounding box
:return:
xmin, ymin, xmax, ymax
"""
xmin = int(round(x - (w / 2)))
xmax = int(round(x + (w / 2)))
ymin = int(round(y - (h / 2)))
ymax = int(round(y + (h / 2)))
return xmin, ymin, xmax, ymax
if len(detections) > 0:
centroid_dict = dict()
objectId = 0
for detection in detections:
name_tag = str(detection[0].decode())
if name_tag == 'person':
x, y, w, h = detection[2][0], \
detection[2][1], \
detection[2][2], \
detection[2][3]
xmin, ymin, xmax, ymax = convertBack(
float(x), float(y), float(w), float(h))
centroid_dict[objectId] = (int(x), int(y), xmin, ymin,
xmax, ymax)
objectId += 1
cv2.line(image_np, (tlblX, tlblY), (trbrX, trbrY),
color=(255, 0, 0),
thickness=2,
lineType=8,
shift=0)
dist = distance_to_camera(avg_width, focalLength,
int(right - left))
if dist:
hand_cnt = hand_cnt + 1
cv2.rectangle(image_np, p1, p2, color, 3, 1)
cv2.putText(image_np, 'hand ' + str(i) + ': ' + id,
(int(left), int(top) - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, color, 2)
cv2.putText(image_np,
'confidence: ' + str("{0:.2f}".format(scores[i])),
(int(left), int(top) - 20), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 255, 0), 2)
cv2.putText(
image_np, 'distance from camera: ' +
str("{0:.2f}".format(dist) + ' inches'),
(int(im_width * 0.65), int(im_height * 0.9 + 30 * i)),
cv2.FONT_HERSHEY_SIMPLEX, 0.3, color, 2)
a = alertcheck.drawboxtosafeline(image_np, p1, p2, Line_Position2,
Orientation)
if hand_cnt == 0:
b = 0
#print(" no hand")
else:
b = 1
#print(" hand")
return a, b