initial version - not on superpixels, just full frame

converter/sp-inceptionV1OnFire-validation.py

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+################################################################################
+
+# Example : perform validation of SP-InceptionV1-OnFire models in TFLearn, PB and TFLite formats
+# (do this for whole image, not superpixels, just for proof of consistency)
+
+# Copyright (c) 2019 - Toby Breckon, Durham University, UK
+
+# License : https://github.com/tobybreckon/fire-detection-cnn/blob/master/LICENSE
+
+################################################################################
+
+import cv2
+import os
+import sys
+import math
+
+################################################################################
+
+import tflearn
+from tflearn.layers.core import *
+from tflearn.layers.conv import *
+from tflearn.layers.normalization import *
+from tflearn.layers.estimator import regression
+
+################################################################################
+
+VALIDATE_TO_PRECISION_N = 5
+
+################################################################################
+
+sys.path.append('..')
+from inceptionV1OnFire import construct_inceptionv1onfire
+
+################################################################################
+
+# tflearn - load model
+
+print("Load tflearn model from: ../models/InceptionV1-OnFire ...", end = '')
+model_tflearn = construct_inceptionv1onfire (224, 224, training=False)
+model_tflearn.load(os.path.join("../models/SP-InceptionV1-OnFire", "sp-inceptiononv1onfire"),weights_only=True)
+print("OK")
+
+################################################################################
+
+# tf protocol buffer - load model (into opencv)
+
+print("Load protocolbuf (pb) model from: inceptiononv1onfire.pb ...", end = '')
+tensorflow_pb_model = cv2.dnn.readNetFromTensorflow('sp-inceptionv1onfire.pb')
+print("OK")
+
+################################################################################
+
+# tflite - load model
+
+print("Load tflite model from: inceptiononv1onfire.tflite ...", end = '')
+tflife_model = tf.lite.Interpreter(model_path="sp-inceptionv1onfire.tflite")
+tflife_model.allocate_tensors()
+print("OK")
+
+# Get input and output tensors.
+tflife_input_details = tflife_model.get_input_details()
+tflife_output_details = tflife_model.get_output_details()
+
+################################################################################
+
+# load video file
+
+video = cv2.VideoCapture("../models/test.mp4")
+print("Load test video from ../models/test.mp4 ...")
+
+# get video properties
+
+width = int(video.get(cv2.CAP_PROP_FRAME_WIDTH))
+height = int(video.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+frame_counter = 0
+fail_counter = 0
+
+while (True):
+
+    # get video frame from file, handle end of file
+
+    ret, frame = video.read()
+    if not ret:
+        print("... end of video file reached")
+        break
+
+    print("frame: " + str(frame_counter),  end = '')
+    frame_counter = frame_counter + 1
+
+    # re-size image to network input size and perform prediction
+
+    # input to networks is: 224x224x3 colour image with channel ordering as {B,G,R}
+    # as is the opencv norm, not {R,G,B} and pixel value range 0->255 for each channel
+
+    small_frame = cv2.resize(frame, (224, 224), cv2.INTER_AREA)
+
+    ############################################################################
+
+    np.set_printoptions(precision=6)
+
+    # perform predictiion with tflearn model
+
+    output_tflearn = model_tflearn.predict([small_frame])
+    print("\t: TFLearn (original): ", end = '')
+    print(output_tflearn, end = '')
+
+    # perform prediction with protocolbuf model via opencv
+
+    tensorflow_pb_model.setInput(cv2.dnn.blobFromImage(small_frame, size=(224, 224), swapRB=False, crop=False))
+    output_tensorflow_pb = tensorflow_pb_model.forward()
+
+    print("\t: Tensorflow .pb (via opencv): ", end = '')
+    print(output_tensorflow_pb, end = '')
+
+    # perform prediction with tflite model via TensorFlow
+
+    tflife_input_data = np.reshape(np.float32(small_frame), (1, 224, 224, 3))
+    tflife_model.set_tensor(tflife_input_details[0]['index'], tflife_input_data)
+
+    tflife_model.invoke()
+
+    output_tflite = tflife_model.get_tensor(tflife_output_details[0]['index'])
+    print("\t: TFLite (via tensorflow): ", end = '')
+    print(output_tflite, end = '')
+
+    try:
+        np.testing.assert_almost_equal(output_tflearn, output_tensorflow_pb, VALIDATE_TO_PRECISION_N)
+        np.testing.assert_almost_equal(output_tflearn, output_tflite, 3)
+        print(": all equal test - PASS")
+    except AssertionError:
+        print(" all equal test - FAIL")
+        fail_counter = fail_counter +1
+
+################################################################################
+print("*** FINAL cross-model validation FAILS (for precision of " + str(VALIDATE_TO_PRECISION_N) + ") = " + str(fail_counter))
+################################################################################