How can I increase the resolution of my input video for my real time Object Detection API? - tensorflow

Question

I am having trouble increasing the resolution in my input video for real time object detection. I have tried increasing the input resolution of the video read in, but the output gives me an error.
I am streaming the video using a webcam program
The only dimensions that work are 640 x 480. Anything bigger gives me an output error. The resolution will only stay 1920 x 1080 until before the frame window, then it switches back to 640 x 480 and gives me an error when the video saves.
import numpy as np
import tensorflow as tf
from object_detection.utils import visualization_utils as vis_util
import cv2 as cv
from time import time
import serial
refPoints = []
# draw ROI
def image_crop(event, x, y, flags, param):
global refPoints
if event == cv.EVENT_LBUTTONDOWN:
refPoints = [(x, y)]
elif event == cv.EVENT_LBUTTONUP:
refPoints.append((x, y))
# run inference on single image
def run_inference_for_single_image(image, graph, sess):
with graph.as_default():
boxes = tf.get_default_graph().get_tensor_by_name('detection_boxes:0')
scores = tf.get_default_graph().get_tensor_by_name('detection_scores:0')
classes = tf.get_default_graph().get_tensor_by_name('detection_classes:0')
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
rboxes, rscores, rclasses = sess.run([boxes, scores, classes], feed_dict={image_tensor: np.expand_dims(image, 0)})
return rboxes[0], rscores[0], rclasses[0]
def main():
# 0 - load model
PATH_TO_MODEL = 'C:\\frozen_inference_graph.pb'
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_MODEL, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
sess = tf.Session(graph=detection_graph)
# 1 - define video streamer & serial port
streamer = 0
out = cv.VideoWriter('output.avi', cv.VideoWriter_fourcc(*'XVID'), 20, (1920, 1080)) #previously 25.0, (640, 480)
port = 'COM4'
ser = serial.Serial(port, 9600)
# 2 - draw region of interest (ROI)
cap = cv.VideoCapture(cv.CAP_DSHOW)
clone = frame.copy()
cv.namedWindow('frame')
cv.resizeWindow('frame', (1920, 1080))
cv.setMouseCallback('frame', image_crop)
print(frame.shape)#
print('ROI selecting...')
while True:
cv.imshow('frame', frame)
if cv.waitKey(1) & 0xFF == ord('r'):
print('ROI selection reset.')
frame = clone.copy()
elif cv.waitKey(1) & 0xff == ord('c'):
print('ROI selected.')
cap.release()
break
if len(refPoints) == 2:
cv.rectangle(frame, refPoints[0], refPoints[1], (0, 255, 0), 2)
cv.imshow('frame', frame)
if cv.waitKey(0) & 0xFF == ord('q'):
cap.release()
cv.destroyAllWindows()
else:
print('only one ROI allowed.')
cap.release()
cv.destroyAllWindows()
return -1
# 3 - run inferences on ROI
t1 = 0
t2 = 0
t3 = 0
i = 0
cap = cv.VideoCapture(streamer)
cv.namedWindow('frame')
cv.resizeWindow('frame', (1920, 1080))
while(True):
# image reading & cropping
t0 = time()
ret, frame = cap.read()
if frame is None:
break
cropped = frame[refPoints[0][1]:refPoints[1][1], refPoints[0][0]:refPoints[1][0]]
dt = time()-t0
t1 += dt
# image inference
t0 = time()
image = cropped.copy()
boxes, scores, classes = run_inference_for_single_image(image, detection_graph, sess)
boxes = boxes[scores>0.95]
classes = classes[scores>0.95]
boxes1 = boxes[classes==1]
boxes2 = boxes[classes==2]
dt = time()-t0
t2 += dt
t0 = time()
cv.rectangle(frame, refPoints[0], refPoints[1], (0, 255, 0), 2)
if len(boxes1):
#vis_util.draw_bounding_boxes_on_image_array(image, boxes)
cv.rectangle(frame, refPoints[0], refPoints[1], (0, 0, 255), 4)
ser.write(b'1')
else:
ser.write(b'0')
#if len(boxes2):
# cv.rectangle(frame, refPoints[0], refPoints[1], (255, 0, 0), 4)
#print(frame.shape)
#print(frame.shape)#
cv.imshow('frame', frame)
out.write(frame)
dt = time()-t0
t3 += dt
i += 1
if cv.waitKey(1) & 0xFF == ord('q'):
break
print("image reading: average %f sec/frame"%(t1/i))
print("image processing: average %f sec/frame"%(t2/i))
print("image showing/saving: average %f sec/frame"%(t3/i))
ser.close()
sess.close()
cap.release()
out.release()
cv.destroyAllWindows()
if __name__ == '__main__':
main()