def plot_boxes(i, frame_no, img, boxes, class_names, plot_labels = True, color = None):
queue_counter = 0
frame_no = frame_no
# Define a tensor used to set the colors of the bounding boxes
colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]])
# Define a function to set the colors of the bounding boxes
def get_color(c, x, max_val):
ratio = float(x) / max_val * 5
i = int(np.floor(ratio))
j = int(np.ceil(ratio))
ratio = ratio - i
r = (1 - ratio) * colors[i][c] + ratio * colors[j][c]
return int(r * 255)
# Get the width and height of the image
width = img.shape[1]
height = img.shape[0]
# Create a figure and plot the image
fig, a = plt.subplots(1,1)
a.imshow(img)
# cv2.imwrite('D:\HeadCount\YOLO-Object-Detection-master\YOLO-Object-Detection-master\Output\out' +str(frame_no)+'.jpeg', img)
# Plot the bounding boxes and corresponding labels on top of the image
for i in range(len(boxes)):
# Get the ith bounding box
box = boxes[i]
# Get the (x,y) pixel coordinates of the lower-left and lower-right corners
# of the bounding box relative to the size of the image.
x1 = int(np.around((box[0] - box[2]/2.0) * width))
y1 = int(np.around((box[1] - box[3]/2.0) * height))
x2 = int(np.around((box[0] + box[2]/2.0) * width))
y2 = int(np.around((box[1] + box[3]/2.0) * height))
# Set the default rgb value to red
rgb = (1, 0, 0)
# Use the same color to plot the bounding boxes of the same object class
if len(box) >= 7 and class_names:
cls_conf = box[5]
cls_id = box[6]
classes = len(class_names)
offset = cls_id * 123457 % classes
red = get_color(2, offset, classes) / 255
green = get_color(1, offset, classes) / 255
blue = get_color(0, offset, classes) / 255
# If a color is given then set rgb to the given color instead
if color is None:
rgb = (red, green, blue)
else:
rgb = color
# Calculate the width and height of the bounding box relative to the size of the image.
width_x = x2 - x1
width_y = y1 - y2
check_point = consider.check(0, 270, 530, 310, 530, 170,
0, 200, x1, y2)
# save_point = 0
if check_point == 1: #.....................
queue_counter += 1
# save_point = 1
# print(x1, y2) #left corner cordinate
# Set the postion and size of the bounding box. (x1, y2) is the pixel coordinate of the
# lower-left corner of the bounding box relative to the size of the image.
rect = patches.Rectangle((x1, y2),
width_x, width_y,
linewidth = 2,
edgecolor = rgb,
facecolor = 'none')
# Draw the bounding box on top of the image
a.add_patch(rect) #.........................
# save_point = 1
# print(x1, y2) #left corner cordinate
# Set the postion and size of the bounding box. (x1, y2) is the pixel coordinate of the
# lower-left corner of the bounding box relative to the size of the image.
# rect = patches.Rectangle((x1, y2), #.....................
# width_x, width_y,
# linewidth = 2,
# edgecolor = rgb,
# facecolor = 'none')
#
# # Draw the bounding box on top of the image
# a.add_patch(rect) #.............................
# If plot_labels = True then plot the corresponding label
# if plot_labels:
#
# # Create a string with the object class name and the corresponding object class probability
# conf_tx = class_names[cls_id] + ': {:.1f}'.format(cls_conf)
#
# # Define x and y offsets for the labels
# lxc = (img.shape[1] * 0.266) / 100
# lyc = (img.shape[0] * 1.180) / 100
##
# # Draw the labels on top of the image
# a.text(x1 + lxc, y1 - lyc, conf_tx, fontsize = 24, color = 'k',
# bbox = dict(facecolor = rgb, edgecolor = rgb, alpha = 0.8))
print('person standing in queue: ',queue_counter)
framename = 'output' + str(frame_no)
# dataentry.datawrite(framename, queue_counter)
plt.savefig('D:\Queue Managment v1\Output\out' +str(frame_no)+'.png')
im = cv2.imread('D:\Queue Managment v1\Output\out' +str(frame_no)+'.png')
imS = cv2.resize(im, (800,700))
cv2.imshow('output',imS)
# cv2.imwrite('OutputTest', im)
cv2.waitKey(2)
def plot_boxes(i,
frame_no,
img,
boxes,
class_names,
r,
plot_labels=True,
color=None):
queue_counter = 0
rect_lst = []
frame_no = frame_no
# Define a tensor used to set the colors of the bounding boxes
colors = torch.FloatTensor([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0],
[1, 1, 0], [1, 0, 0]])
# Define a function to set the colors of the bounding boxes
def get_color(c, x, max_val):
ratio = float(x) / max_val * 5
i = int(np.floor(ratio))
j = int(np.ceil(ratio))
ratio = ratio - i
r = (1 - ratio) * colors[i][c] + ratio * colors[j][c]
return int(r * 255)
# Get the width and height of the image
width = img.shape[1]
height = img.shape[0]
# cv2.imwrite('D:\HeadCount\YOLO-Object-Detection-master\YOLO-Object-Detection-master\Output\out' +str(frame_no)+'.jpeg', img)
# Plot the bounding boxes and corresponding labels on top of the image
for i in range(len(boxes)):
rects = []
# Get the ith bounding box
box = boxes[i]
c = box[6]
if class_names[c] == 'person':
# print('true: ',class_names[c])
# Get the (x,y) pixel coordinates of the lower-left and lower-right corners
# of the bounding box relative to the size of the image.
nx = int(np.around((box[0] - box[1] / 2.0) * width))
ny = int(np.around((box[2] - box[3] / 2.0) * width))
x1 = int(np.around((box[0] - box[2] / 2.0) * width))
y1 = int(np.around((box[1] - box[3] / 2.0) * height))
x2 = int(np.around((box[0] + box[2] / 2.0) * width))
y2 = int(np.around((box[1] + box[3] / 2.0) * height))
# Set the default rgb value to red
rgb = (1, 0, 0)
# Use the same color to plot the bounding boxes of the same object class
if len(box) >= 7 and class_names:
cls_conf = box[5]
cls_id = box[6]
classes = len(class_names)
offset = cls_id * 123457 % classes
red = get_color(2, offset, classes) / 255
green = get_color(1, offset, classes) / 255
blue = get_color(0, offset, classes) / 255
# If a color is given then set rgb to the given color instead
if color is None:
rgb = (red, green, blue)
else:
rgb = color
# Calculate the width and height of the bounding box relative to the size of the image.
width_x = x2 - x1
# width_y = y1 - y2
width_y = -200
x = int((x1 + x2) / 2)
y = int((y1 + y2) / 2)
check_point = consider.check(r[0][0], r[0][1], r[1][0], r[0][1],
r[1][0], r[1][1], r[0][0], r[1][1], x,
y) # 99%
# check_point = 1
shape = np.array([[[r[0][0], r[0][1]], [r[1][0], r[0][1]],
[r[1][0], r[1][1]], [r[0][0], r[1][1]]]],
np.int32)
# shape = np.array([[r]], np.int32)
# points = shape.reshape((-1, 1, 2))
img = cv2.polylines(img, [shape], True, (0, 200, 255), 5)
# img = cv2.line(img, r[0], r[1], (0,255,255), 2)
if check_point == 1: #.....................
# print(x1,y1," ",x2,y2)
queue_counter += 1
rect_lst.append([x, y])
img = cv2.line(img, (x, y1), (x, y2), (255, 0, 0), 2)
img = cv2.circle(img, (x, y), 5, (0, 0, 255), 1)
org = (x, y)
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 0.5
color = (0, 255, 0)
thickness = 2
txt = str(x) + ',' + str(y)
cv2.putText(img, txt, org, font, fontScale, color, thickness,
cv2.LINE_AA)
# cv2.imshow('cent',img)
# cv2.waitKey(0)
# img = cv2.rectangle(img, (x1, y1, width_x, width_y), (0,255,0), 1)
rect_lst = sorted(rect_lst)
print('list', rect_lst)
r = queueDetect.Detect()
r.key_values(rect_lst)
# print(p)
print('person standing in queue: ', queue_counter)
framename = 'output' + str(frame_no)
# dataentry.datawrite(framename, queue_counter)
# plt.savefig('D:\Queue Managment v1\Output\out' +str(frame_no)+'.png')
# im = cv2.imread('D:\Queue Managment v1\Output\out' +str(frame_no)+'.png')
# imS = cv2.resize(im, (800,700))
cv2.imwrite('Output/' + str(framename) + '.jpg', img)
cv2.imshow('draw', img)
# plt.show()
# cv2.imwrite('Output/Output{}'.format(framename), img)
cv2.waitKey(2)
def plot_boxes(i,
frame_no,
img,
boxes,
class_names,
plot_labels=True,
color=None):
queue_counter = 0
frame_no = frame_no
# Define a tensor used to set the colors of the bounding boxes
colors = torch.FloatTensor([[1, 0, 1], [0, 0, 1], [0, 1, 1], [0, 1, 0],
[1, 1, 0], [1, 0, 0]])
# Define a function to set the colors of the bounding boxes
def get_color(c, x, max_val):
ratio = float(x) / max_val * 5
i = int(np.floor(ratio))
j = int(np.ceil(ratio))
ratio = ratio - i
r = (1 - ratio) * colors[i][c] + ratio * colors[j][c]
return int(r * 255)
# Get the width and height of the image
width = img.shape[1]
height = img.shape[0]
# Create a figure and plot the image
fig, a = plt.subplots(1, 1)
a.imshow(img)
# cv2.imwrite('D:\HeadCount\YOLO-Object-Detection-master\YOLO-Object-Detection-master\Output\out' +str(frame_no)+'.jpeg', img)
# Plot the bounding boxes and corresponding labels on top of the image
for i in range(len(boxes)):
rects = []
# Get the ith bounding box
box = boxes[i]
c = box[6]
if class_names[c] == 'person':
# print('true: ',class_names[c])
# Get the (x,y) pixel coordinates of the lower-left and lower-right corners
# of the bounding box relative to the size of the image.
x1 = int(np.around((box[0] - box[2] / 2.0) * width))
y1 = int(np.around((box[1] - box[3] / 2.0) * height))
x2 = int(np.around((box[0] + box[2] / 2.0) * width))
y2 = int(np.around((box[1] + box[3] / 2.0) * height))
# Set the default rgb value to red
rgb = (1, 0, 0)
# Use the same color to plot the bounding boxes of the same object class
if len(box) >= 7 and class_names:
cls_conf = box[5]
cls_id = box[6]
classes = len(class_names)
offset = cls_id * 123457 % classes
red = get_color(2, offset, classes) / 255
green = get_color(1, offset, classes) / 255
blue = get_color(0, offset, classes) / 255
# If a color is given then set rgb to the given color instead
if color is None:
rgb = (red, green, blue)
else:
rgb = color
# Calculate the width and height of the bounding box relative to the size of the image.
width_x = x2 - x1
# width_y = y1 - y2
width_y = -200
# print(width_y)
#
# check_point = consider.check(0, 270, 530, 310, 530, 170,
# 0, 200, x1, y2)
check_point = consider.check(0, 150, 576, 208, 576, 300, 0, 215,
x1, y2) # 99%
# save_point = 0
if check_point == 1: #.....................
# print(x1,y1," ",x2,y2)
queue_counter += 1
rect = patches.Rectangle((x1, y2),
width_x,
width_y,
linewidth=2,
edgecolor=rgb,
facecolor='none')
rects.append(box)
track_no = tracker.track(x1, x2, y1, y2, frame_no)
# Draw the bounding box on top of the image
a.add_patch(rect) #.........................
# centroid = centroids_tracker.CentroidTracker()
# trackable_object = centroid.updates(rects)
# print(trackable_object)
label = str(track_no)
lxc = (img.shape[1] * 0.266) / 100
lyc = (img.shape[0] * 1.180) / 100
a.text(x1 + lxc,
y1 - lyc,
label,
fontsize=24,
color='k',
bbox=dict(facecolor=rgb, edgecolor=rgb, alpha=0.8))
print('person standing in queue: ', queue_counter)
framename = 'output' + str(frame_no)
dataentry.datawrite(framename, queue_counter)
plt.savefig('D:\Queue Managment v1\Output\out' + str(frame_no) + '.png')
im = cv2.imread('D:\Queue Managment v1\Output\out' + str(frame_no) +
'.png')
imS = cv2.resize(im, (800, 700))
cv2.imshow('output', imS)
# cv2.imwrite('OutputTest', im)
cv2.waitKey(2)