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predictdepth.py
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predictdepth.py
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import sys
sys.path.insert(0, './FCRN_DepthPrediction_master')
import os
import numpy as np
import tensorflow.compat.v1 as tf
from matplotlib import pyplot as plt
from PIL import Image
import cv2
from FCRN_DepthPrediction_master.tensorflow import models
tf.disable_v2_behavior()
# Take an image in OpenCV to estimate depth
def take_image():
cam = cv2.VideoCapture(0)
cv2.namedWindow("test")
ret, frame = cam.read()
if not ret:
print("failed to grab frame")
cv2.imshow("test", frame)
img_name = "img.jpg"
cv2.imwrite(img_name, frame)
print("{} written!".format(img_name))
cam.release()
cv2.destroyAllWindows()
def predict():
# Take Image to Calibrate Scene
take_image()
# Default input size
height = 228
width = 304
channels = 3
batch_size = 1
# Read image
img = Image.open("img.jpg")
img = img.resize([width, height], Image.ANTIALIAS)
img = np.array(img).astype('float32')
img = np.expand_dims(np.asarray(img), axis=0)
# Create a placeholder for the input image
input_node = tf.placeholder(tf.float32, shape=(None, height, width, channels))
# Construct the network
net = models.ResNet50UpProj({'data': input_node}, batch_size, 1, False)
with tf.Session() as sess:
# Load the converted parameters
print('Loading the model')
# Use to load from ckpt file
saver = tf.train.Saver()
saver.restore(sess, "NYU_FCRN.ckpt")
# Use to load from npy file
#net.load(model_data_path, sess)
# Evalute the network for the given image
pred = sess.run(net.get_output(), feed_dict={input_node: img})
# Plot result
fig = plt.figure()
ii = plt.imshow(pred[0,:,:,0], interpolation='nearest')
fig.colorbar(ii)
plt.show()
# Save Output Array
print(f"Prediction: {pred}")
np.save('pred.npy', pred)
return pred
def main():
# Predict the image
predict()
os._exit(0)
if __name__ == '__main__':
main()