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I am streaming lepton FLIR camera on jetson nano using python3 and OpenCV and I have problems that I can not resize live video. The original lepton resolution is (160 x 120) and I would like to resize it to (640 x 480). I tried to use different OpenCV commands but it does not work
need help
Herein the code
<pre># imports<br />
import cv2<br />
import numpy as np<br />
import time<br />
import argparse<br />
<br />
# own modules<br />
import utills, plot<br />
<br />
confid = 0.5<br />
thresh = 0.5<br />
mouse_pts = []<br />
<br />
<br />
# Function to get points for Region of Interest(ROI) and distance scale. It will take 8 points on first frame using mouse click <br />
# event.First four points will define ROI where we want to moniter social distancing. Also these points should form parallel <br />
# lines in real world if seen from above(birds eye view). Next 3 points will define 6 feet(unit length) distance in <br />
# horizontal and vertical direction and those should form parallel lines with ROI. Unit length we can take based on choice.<br />
# Points should pe in pre-defined order - bottom-left, bottom-right, top-right, top-left, point 5 and 6 should form <br />
# horizontal line and point 5 and 7 should form verticle line. Horizontal and vertical scale will be different. <br />
<br />
# Function will be called on mouse events <br />
<br />
def get_mouse_points(event, x, y, flags, param):<br />
<br />
global mouse_pts<br />
if event == cv2.EVENT_LBUTTONDOWN:<br />
if len(mouse_pts) < 4:<br />
cv2.circle(image, (x, y), 5, (0, 0, 255), 10)<br />
else:<br />
cv2.circle(image, (x, y), 5, (255, 0, 0), 10)<br />
<br />
if len(mouse_pts) >= 1 and len(mouse_pts) <= 3:<br />
cv2.line(image, (x, y), (mouse_pts[len(mouse_pts)-1][0], mouse_pts[len(mouse_pts)-1][1]), (70, 70, 70), 2)<br />
if len(mouse_pts) == 3:<br />
cv2.line(image, (x, y), (mouse_pts[0][0], mouse_pts[0][1]), (70, 70, 70), 2)<br />
<br />
if "mouse_pts" not in globals():<br />
mouse_pts = []<br />
mouse_pts.append((x, y))<br />
#print("Point detected")<br />
#print(mouse_pts)<br />
<br />
<br />
<br />
def calculate_social_distancing(vid_path, net, output_dir, output_vid, ln1):<br />
<br />
count = 0<br />
vs = cv2.VideoCapture(0)<br />
<br />
# Get video height, width and fps<br />
height = int(vs.get(cv2.CAP_PROP_FRAME_HEIGHT))<br />
width = int(vs.get(cv2.CAP_PROP_FRAME_WIDTH))<br />
fps = int(vs.get(cv2.CAP_PROP_FPS))<br />
<br />
# Set scale for birds eye view<br />
# Bird's eye view will only show ROI<br />
scale_w, scale_h = utills.get_scale(width, height)<br />
<br />
fourcc = cv2.VideoWriter_fourcc(*"XVID")<br />
output_movie = cv2.VideoWriter("./output_vid/distancing.avi", fourcc, fps, (width, height))<br />
bird_movie = cv2.VideoWriter("./output_vid/bird_eye_view.avi", fourcc, fps, (int(width * scale_w), int(height * scale_h)))<br />
<br />
points = []<br />
global image<br />
<br />
while True:<br />
<br />
(grabbed, frame) = vs.read()<br />
<br />
if not grabbed:<br />
print('here')<br />
break<br />
<br />
(H, W) = frame.shape[:2]<br />
<br />
# first frame will be used to draw ROI and horizontal and vertical 180 cm distance(unit length in both directions)<br />
if count == 0:<br />
while True:<br />
image = frame<br />
cv2.imshow("image", image)<br />
cv2.waitKey(1)<br />
if len(mouse_pts) == 8:<br />
cv2.destroyWindow("image")<br />
break<br />
<br />
points = mouse_pts <br />
<br />
# Using first 4 points or coordinates for perspective transformation. The region marked by these 4 points are <br />
# considered ROI. This polygon shaped ROI is then warped into a rectangle which becomes the bird eye view. <br />
# This bird eye view then has the property property that points are distributed uniformally horizontally and <br />
# vertically(scale for horizontal and vertical direction will be different). So for bird eye view points are <br />
# equally distributed, which was not case for normal view.<br />
src = np.float32(np.array(points[:4]))<br />
dst = np.float32([[0, H], [W, H], [W, 0], [0, 0]])<br />
prespective_transform = cv2.getPerspectiveTransform(src, dst)<br />
<br />
# using next 3 points for horizontal and vertical unit length(in this case 180 cm)<br />
pts = np.float32(np.array([points[4:7]]))<br />
warped_pt = cv2.perspectiveTransform(pts, prespective_transform)[0]<br />
<br />
# since bird eye view has property that all points are equidistant in horizontal and vertical direction.<br />
# distance_w and distance_h will give us 180 cm distance in both horizontal and vertical directions<br />
# (how many pixels will be there in 180cm length in horizontal and vertical direction of birds eye view),<br />
# which we can use to calculate distance between two humans in transformed view or bird eye view<br />
distance_w = np.sqrt((warped_pt[0][0] - warped_pt[1][0]) ** 2 + (warped_pt[0][1] - warped_pt[1][1]) ** 2)<br />
distance_h = np.sqrt((warped_pt[0][0] - warped_pt[2][0]) ** 2 + (warped_pt[0][1] - warped_pt[2][1]) ** 2)<br />
pnts = np.array(points[:4], np.int32)<br />
cv2.polylines(frame, [pnts], True, (70, 70, 70), thickness=2)<br />
<br />
####################################################################################<br />
<br />
# YOLO v4<br />
blob = cv2.dnn.blobFromImage(frame, 1 / 255.0, (128, 128), swapRB=True, crop=False)<br />
net.setInput(blob)<br />
start = time.time()<br />
layerOutputs = net.forward(ln1)<br />
end = time.time()<br />
boxes = []<br />
confidences = []<br />
classIDs = [] <br />
<br />
for output in layerOutputs:<br />
for detection in output:<br />
scores = detection[5:]<br />
classID = np.argmax(scores)<br />
confidence = scores[classID]<br />
# detecting humans in frame<br />
if classID == 1:<br />
<br />
if confidence > confid:<br />
<br />
box = detection[0:4] * np.array([W, H, W, H])<br />
(centerX, centerY, width, height) = box.astype("int")<br />
<br />
x = int(centerX - (width / 2))<br />
y = int(centerY - (height / 2))<br />
<br />
boxes.append([x, y, int(width), int(height)])<br />
confidences.append(float(confidence))<br />
classIDs.append(classID)<br />
<br />
idxs = cv2.dnn.NMSBoxes(boxes, confidences, confid, thresh)<br />
font = cv2.FONT_HERSHEY_PLAIN<br />
boxes1 = []<br />
for i in range(len(boxes)):<br />
if i in idxs:<br />
boxes1.append(boxes[i])<br />
x,y,w,h = boxes[i]<br />
<br />
if len(boxes1) == 0:<br />
count = count + 1<br />
continue<br />
<br />
# Here we will be using bottom center point of bounding box for all boxes and will transform all those<br />
# bottom center points to bird eye view<br />
person_points = utills.get_transformed_points(boxes1, prespective_transform)<br />
<br />
# Here we will calculate distance between transformed points(humans)<br />
distances_mat, bxs_mat = utills.get_distances(boxes1, person_points, distance_w, distance_h)<br />
risk_count = utills.get_count(distances_mat)<br />
<br />
frame1 = np.copy(frame)<br />
<br />
# Draw bird eye view and frame with bouding boxes around humans according to risk factor <br />
bird_image = plot.bird_eye_view(frame, distances_mat, person_points, scale_w, scale_h, risk_count)<br />
img = plot.social_distancing_view(frame1, bxs_mat, boxes1, risk_count)<br />
<br />
# Show/write image and videos<br />
if count != 0:<br />
output_movie.write(img)<br />
bird_movie.write(bird_image)<br />
<br />
cv2.imshow('Bird Eye View', bird_image)<br />
cv2.imshow('Social Distancing', img)<br />
cv2.imwrite(output_dir+"frame%d.jpg" % count, img)<br />
cv2.imwrite(output_dir+"bird_eye_view/frame%d.jpg" % count, bird_image)<br />
<br />
count = count + 1<br />
if cv2.waitKey(1) & 0xFF == ord('q'):<br />
break<br />
<br />
vs.release()<br />
cv2.destroyAllWindows() <br />
<br />
<br />
if __name__== "__main__":<br />
<br />
# Receives arguements specified by user<br />
parser = argparse.ArgumentParser()<br />
<br />
parser.add_argument('-v', '--video_path', action='store', dest='video_path', default='./data/example.mp4' ,<br />
help='Path for input video')<br />
<br />
parser.add_argument('-o', '--output_dir', action='store', dest='output_dir', default='./output/' ,<br />
help='Path for Output images')<br />
<br />
parser.add_argument('-O', '--output_vid', action='store', dest='output_vid', default='./output_vid/' ,<br />
help='Path for Output videos')<br />
<br />
parser.add_argument('-m', '--model', action='store', dest='model', default='./models/',<br />
help='Path for models directory')<br />
<br />
parser.add_argument('-u', '--uop', action='store', dest='uop', default='NO',<br />
help='Use open pose or not (YES/NO)')<br />
<br />
values = parser.parse_args()<br />
<br />
model_path = values.model<br />
if model_path[len(model_path) - 1] != '/':<br />
model_path = model_path + '/'<br />
<br />
output_dir = values.output_dir<br />
if output_dir[len(output_dir) - 1] != '/':<br />
output_dir = output_dir + '/'<br />
<br />
output_vid = values.output_vid<br />
if output_vid[len(output_vid) - 1] != '/':<br />
output_vid = output_vid + '/'<br />
<br />
<br />
# load Yolov4 weights<br />
<br />
weightsPath = model_path + "yolov4-tiny-custom_best.weights"<br />
configPath = model_path + "yolov4-tiny-custom.cfg"<br />
<br />
net_yl = cv2.dnn.readNetFromDarknet(configPath, weightsPath)<br />
ln = net_yl.getLayerNames()<br />
ln1 = [ln[i[0] - 1] for i in net_yl.getUnconnectedOutLayers()]<br />
<br />
# set mouse callback <br />
<br />
cv2.namedWindow("image")<br />
cv2.setMouseCallback("image", get_mouse_points)<br />
np.random.seed(42)<br />
<br />
calculate_social_distancing(values.video_path, net_yl, output_dir, output_vid, ln1)</pre>
What I have tried:
I want to resize the streaming video from LEPTON FLIR camera to (640 x 480)
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