def calculate_social_distancing(vid_path, net, output_dir, output_vid, ln1):
count = 0
vs = cv2.VideoCapture(vid_path)
# Get video height, width and fps
height = int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(vs.get(cv2.CAP_PROP_FRAME_WIDTH))
fps = int(vs.get(cv2.CAP_PROP_FPS))
# Set scale for birds eye view
# Bird's eye view will only show ROI
scale_w, scale_h = utills.get_scale(width, height)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
output_movie = cv2.VideoWriter("./output_vid/distancing.avi", fourcc, fps, (width, height))
bird_movie = cv2.VideoWriter("./output_vid/bird_eye_view.avi", fourcc, fps, (int(width * scale_w), int(height * scale_h)))
points = []
global image
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('here')
break
(H, W) = frame.shape[:2]
# first frame will be used to draw ROI and horizontal and vertical 180 cm distance(unit length in both directions)
if count == 0:
while True:
image = frame
cv2.imshow("image", image)
cv2.waitKey(1)
if len(mouse_pts) == 8:
cv2.destroyWindow("image")
break
points = mouse_pts
# Using first 4 points or coordinates for perspective transformation. The region marked by these 4 points are
# considered ROI. This polygon shaped ROI is then warped into a rectangle which becomes the bird eye view.
# This bird eye view then has the property property that points are distributed uniformally horizontally and
# vertically(scale for horizontal and vertical direction will be different). So for bird eye view points are
# equally distributed, which was not case for normal view.
src = np.float32(np.array(points[:4]))
dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])
prespective_transform = cv2.getPerspectiveTransform(src, dst)
# using next 3 points for horizontal and vertical unit length(in this case 180 cm)
pts = np.float32(np.array([points[4:7]]))
warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]
# since bird eye view has property that all points are equidistant in horizontal and vertical direction.
# distance_w and distance_h will give us 180 cm distance in both horizontal and vertical directions
# (how many pixels will be there in 180cm length in horizontal and vertical direction of birds eye view),
# which we can use to calculate distance between two humans in transformed view or bird eye view
distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0]) ** 2 + (warped_pt[0][1] - warped_pt[1][1]) ** 2)
distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0]) ** 2 + (warped_pt[0][1] - warped_pt[2][1]) ** 2)
pnts = np.array(points[:4], np.int32)
cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)
####################################################################################
# YOLO v3
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln1)
end = time.time()
boxes = []
confidences = []
classIDs = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# detecting humans in frame
if classID == 0:
if confidence > confid:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)
font = cv2.FONT_HERSHEY_PLAIN
boxes1 = []
for i in range(len(boxes)):
if i in idxs:
boxes1.append(boxes[i])
x,y,w,h = boxes[i]
if len(boxes1) == 0:
count = count + 1
continue
# Here we will be using bottom center point of bounding box for all boxes and will transform all those
# bottom center points to bird eye view
person_points = utills.get_transformed_points(boxes1, prespective_transform)
# Here we will calculate distance between transformed points(humans)
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points, distance_w, distance_h)
risk_count = utills.get_count(distances_mat)
frame1 = np.copy(frame)
# Draw bird eye view and frame with bouding boxes around humans according to risk factor
bird_image = plot.bird_eye_view(frame, distances_mat, person_points, scale_w, scale_h, risk_count)
img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)
# Show/write image and videos
if count != 0:
output_movie.write(img)
bird_movie.write(bird_image)
cv2.imshow('Bird Eye View', bird_image)
cv2.imwrite(output_dir+"frame%d.jpg" % count, img)
cv2.imwrite(output_dir+"bird_eye_view/frame%d.jpg" % count, bird_image)
count = count + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
vs.release()
cv2.destroyAllWindows()
def calculate_social_distancing(vid_path, net, ln1):
count = 0
vs = cv2.VideoCapture(vid_path)
points = []
global image
while True:
(grabbed, frame) = vs.read()
frame = imutils.resize(frame, width=500) # if changing width, must also change offset of face
if not grabbed:
print('here')
break
(H, W) = frame.shape[:2]
# first frame will be used to draw ROI and horizontal and vertical 180 cm distance(unit length in both directions)
if count == 0:
while True:
image = frame
cv2.imshow("image", image)
cv2.waitKey(1)
if len(mouse_pts) == 8:
cv2.destroyWindow("image")
start1=time.time()
break
points = mouse_pts
if count % 50 == 0:
(locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
# Using first 4 points or coordinates for perspective transformation. The region marked by these 4 points are
# considered ROI. This polygon shaped ROI is then warped into a rectangle which becomes the bird eye view.
# This bird eye view then has the property property that points are distributed uniformally horizontally and
# vertically(scale for horizontal and vertical direction will be different). So for bird eye view points are
# equally distributed, which was not case for normal view.
src = np.float32(np.array(points[:4]))
dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])
prespective_transform = cv2.getPerspectiveTransform(src, dst)
# using next 3 points for horizontal and vertical unit length(in this case 180 cm)
pts = np.float32(np.array([points[4:7]]))
warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]
# since bird eye view has property that all points are equidistant in horizontal and vertical direction.
# distance_w and distance_h will give us 180 cm distance in both horizontal and vertical directions
# (how many pixels will be there in 180cm length in horizontal and vertical direction of birds eye view),
# which we can use to calculate distance between two humans in transformed view or bird eye view
distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0]) ** 2 + (warped_pt[0][1] - warped_pt[1][1]) ** 2)
distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0]) ** 2 + (warped_pt[0][1] - warped_pt[2][1]) ** 2)
pnts = np.array(points[:4], np.int32)
cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)
####################################################################################
# YOLO v3
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
layerOutputs = net.forward(ln1)
boxes = []
confidences = []
classIDs = []
for (box, pred) in zip(locs, preds):
# unpack the bounding box and predictions
(startX, startY, endX, endY) = box
(mask, withoutMask) = pred
# determine the class label and color we'll use to draw the bounding box and text
if mask > withoutMask:
if mask > 0.90:
label = "Mask"
color = (0, 255, 0) # green
img1 = cv2.imread("emoji_happy.png", -1)
else:
label = "Mask not on Properly"
color = (0, 220, 220) # yellow
img1 = cv2.imread("emoji_middle.png", -1)
else:
label = "No Mask"
color = (0, 0, 255) # red
img1 = cv2.imread("emoji_worried.png", -1)
img1 = cv2.resize(img1, (int(img1.shape[1] * 0.25), int(img1.shape[0] * 0.25)))
label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
x_offset = y_offset = 50
y1, y2 = y_offset, y_offset + img1.shape[0]
x1, x2 = x_offset, x_offset + img1.shape[1]
alpha_s = img1[:, :, 3] / 255.0
alpha_l = 1.0 - alpha_s
for c in range(0, 3):
frame[y1:y2, x1:x2, c] = (alpha_s * img1[:, :, c] +
alpha_l * frame[y1:y2, x1:x2, c])
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# detecting humans in frame
if classID == 0:
if confidence > confid:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
cv2.putText(frame, label, (startX, startY - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes.append([x, y-10, int(width), int(height)+25])
confidences.append(float(confidence))
classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)
boxes1 = []
for i in range(len(boxes)):
if i in idxs:
boxes1.append(boxes[i])
x, y, w, h = boxes[i]
if len(boxes1) == 0:
count = count + 1
continue
# Here we will be using bottom center point of bounding box for all boxes and will transform all those
# bottom center points to bird eye view
person_points = utills.get_transformed_points(boxes1, prespective_transform)
# Here we will calculate distance between transformed points(humans)
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points, distance_w, distance_h)
risk_count = utills.get_count(distances_mat)
frame1 = np.copy(frame)
# Draw bird eye view and frame with bouding boxes around humans according to risk factor
img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)
# Show/write image and videos
cv2.imshow('Camera View', img)
count += 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
print(time.time()-start1)
vs.release()
cv2.destroyAllWindows()
def calculate_social_distancing(vid_path, net, output_dir, output_vid, ln1):
count = 0
vs = cv2.VideoCapture(vid_path)
# Get video height, width and fps
height = int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(vs.get(cv2.CAP_PROP_FRAME_WIDTH))
fps = int(vs.get(cv2.CAP_PROP_FPS))
# Set scale for birds eye view
scale_w, scale_h = utills.get_scale(width, height)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
# Initialize writer objects
output_movie = cv2.VideoWriter("Output.avi", fourcc, fps, (width, height))
output_movie2 = cv2.VideoWriter("Output2.avi", fourcc, fps, (1920, 1080))
bird_movie = cv2.VideoWriter("./output_vid/bird_eye_view.avi", fourcc, fps,
(int(width * scale_w), int(height * scale_h)))
points = []
global image
while True:
# Read frames
(grabbed, frame) = vs.read()
if not grabbed:
print('here')
break
(H, W) = frame.shape[:2]
if count == 0:
while True:
image = frame
cv2.imshow("image", image)
cv2.waitKey(1)
if len(mouse_pts) == 8:
cv2.destroyWindow("image")
break
points = mouse_pts
src = np.float32(np.array(points[:4]))
dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])
# Transform perspective using opencv method
prespective_transform = cv2.getPerspectiveTransform(src, dst)
# using next 3 points for horizontal and vertical unit length(in this case 6 Feets ~= 180 cm)
pts = np.float32(np.array([points[4:7]]))
warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]
# Calculate distance scale using marked points by user
distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0])**2 +
(warped_pt[0][1] - warped_pt[1][1])**2)
distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0])**2 +
(warped_pt[0][1] - warped_pt[2][1])**2)
pnts = np.array(points[:4], np.int32)
cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)
# Using YOLO v3 model using dnn method
blob = cv2.dnn.blobFromImage(frame,
1 / 255.0, (416, 416),
swapRB=True,
crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln1)
end = time.time()
boxes = []
confidences = []
classIDs = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
# detecting humans in frame
if classID == 0:
if confidence > confid:
# Finding bounding boxes dimensions
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# Applying Non Maximum Suppression to remove multiple bounding boxes around same object
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)
font = cv2.FONT_HERSHEY_PLAIN
boxes1 = []
for i in range(len(boxes)):
if i in idxs:
boxes1.append(boxes[i])
x, y, w, h = boxes[i]
if len(boxes1) == 0:
count = count + 1
continue
# Get transformed points using perspective transform
person_points = utills.get_transformed_points(boxes1,
prespective_transform)
# Get distances between the points
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points,
distance_w, distance_h)
# Get the risk counts
risk_count = utills.get_count(distances_mat)
frame1 = np.copy(frame)
bird_image = plot.bird_eye_view(frame, distances_mat, person_points,
scale_w, scale_h, risk_count)
img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)
if count != 0:
bird_movie.write(bird_image)
cv2.imshow('Social Distancing Detect', img)
output_movie.write(img)
output_movie2.write(img)
cv2.imwrite(output_dir + "frame%d.jpg" % count, img)
cv2.imwrite(output_dir + "bird_eye_view/frame%d.jpg" % count,
bird_image)
count = count + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
output_movie.write(img)
vs.release()
cv2.destroyAllWindows()
boxes1.append(boxes[i])
x, y, w, h = boxes[i]
if len(boxes1) == 0:
count = count + 1
continue
# Here we will be using bottom center point of bounding box for all boxes and will transform all those
# bottom center points to bird eye view
person_points = utills.get_transformed_points(boxes1,
perspective_transform)
# Here we will calculate distance between transformed points(humans)
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points,
distance_w, distance_h)
risk_count = utills.get_count(distances_mat)
frame1 = np.copy(frame)
# Draw bird eye view and frame with bouding boxes around humans according to risk factor
bird_image = draw.bird_eye_view(frame, distances_mat, person_points,
scale_w, scale_h, risk_count)
img = draw.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)
# closing part
count = count + 1
if args["display"] > 0:
# show the output frame
cv2.imshow("birdImg", bird_image)
cv2.imshow("frame", img)
key = cv2.waitKey(1) & 0xFF
def calculate_social_distancing(vid_path, net, output_dir, output_vid, ln1):
count = 0
vs = cv2.VideoCapture(vid_path)
height = int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT))
width = int(vs.get(cv2.CAP_PROP_FRAME_WIDTH))
fps = int(vs.get(cv2.CAP_PROP_FPS))
scale_w, scale_h = utills.get_scale(width, height)
fourcc = cv2.VideoWriter_fourcc(*"XVID")
output_movie = cv2.VideoWriter("./output_vid/distancing.avi", fourcc, fps,
(width, height))
bird_movie = cv2.VideoWriter("./output_vid/bird_eye_view.avi", fourcc, fps,
(int(width * scale_w), int(height * scale_h)))
points = []
global image
while True:
(grabbed, frame) = vs.read()
if not grabbed:
print('here')
break
(H, W) = frame.shape[:2]
if count == 0:
while True:
image = frame
cv2.imshow("image", image)
cv2.waitKey(1)
if len(mouse_pts) == 8:
cv2.destroyWindow("image")
break
points = mouse_pts
src = np.float32(np.array(points[:4]))
dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])
prespective_transform = cv2.getPerspectiveTransform(src, dst)
pts = np.float32(np.array([points[4:7]]))
warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]
distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0])**2 +
(warped_pt[0][1] - warped_pt[1][1])**2)
distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0])**2 +
(warped_pt[0][1] - warped_pt[2][1])**2)
pnts = np.array(points[:4], np.int32)
cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)
blob = cv2.dnn.blobFromImage(frame,
1 / 255.0, (416, 416),
swapRB=True,
crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln1)
end = time.time()
boxes = []
confidences = []
classIDs = []
for output in layerOutputs:
for detection in output:
scores = detection[5:]
classID = np.argmax(scores)
confidence = scores[classID]
if classID == 0:
if confidence > confid:
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)
font = cv2.FONT_HERSHEY_PLAIN
boxes1 = []
for i in range(len(boxes)):
if i in idxs:
boxes1.append(boxes[i])
x, y, w, h = boxes[i]
if len(boxes1) == 0:
count = count + 1
continue
person_points = utills.get_transformed_points(boxes1,
prespective_transform)
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points,
distance_w, distance_h)
risk_count = utills.get_count(distances_mat)
frame1 = np.copy(frame)
bird_image = plot.bird_eye_view(frame, distances_mat, person_points,
scale_w, scale_h, risk_count)
img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)
if count != 0:
output_movie.write(img)
bird_movie.write(bird_image)
cv2.imshow('Bird Eye View', bird_image)
cv2.imwrite(output_dir + "frame%d.jpg" % count, img)
cv2.imwrite(output_dir + "bird_eye_view/frame%d.jpg" % count,
bird_image)
count = count + 1
if cv2.waitKey(1) & 0xFF == ord('q'):
break
vs.release()
cv2.destroyAllWindows()