Travis CI, test suite

.gitignore

@@ -15,6 +15,8 @@ results/
 temp/
 runner/
 temporary/
+test/data-models/
+test/data-temp/
 *.db
 
 # PyCharm

.travis.yaml

@@ -0,0 +1,27 @@
+dist: xenial
+sudo: required
+language: python
+python:
+  - '3.5'
+  - '3.6'
+  - '3.7'
+install:
+  - pip install -r requirements.txt
+  - pip install pytest
+script:
+  - mkdir tests/data-models
+  - mkdir tests/data-temp
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/DenseNet-BC-121-32.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/inception_v3_weights_tf_dim_ordering_tf_kernels.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_weights_tf_dim_ordering_tf_kernels.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/squeezenet_weights_tf_dim_ordering_tf_kernels.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo.h5
+  - wget /tests/data-models/ https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/yolo-tiny.h5
+  - python setup.py
+  - cd test
+  - pytest
+
+
+
+

requirements.txt

@@ -1,7 +1,7 @@
 tensorflow
 keras
 numpy
-Pillow
+pillow
 scipy
 h5py
 matplotlib

test/data-json/idenprof.json

@@ -0,0 +1,12 @@
+{
+    "9" : "waiter",
+    "6" : "mechanic",
+    "2" : "engineer",
+    "4" : "fireman",
+    "0" : "chef",
+    "5" : "judge",
+    "7" : "pilot",
+    "8" : "police",
+    "3" : "farmer",
+    "1" : "doctor"
+}

test/test_custom_recognition.py

@@ -0,0 +1,130 @@
+from imageai.Prediction.Custom import CustomImagePrediction
+import os
+import pytest
+from os.path import dirname
+
+
+main_folder = os.getcwd()
+
+all_images = os.listdir(os.path.join(main_folder, "data-images"))
+all_images_array = []
+
+
+def images_to_image_array():
+    for image in all_images:
+        all_images_array.append(os.path.join(main_folder, "data-images", image))
+
+
+
+@pytest.mark.resnet
+@pytest.mark.recognition_custom
+def test_custom_recognition_model_resnet():
+    predictor = CustomImagePrediction()
+    predictor.setModelTypeAsResNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_resnet.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadModel(num_objects=10)
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "9.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], str)
+
+
+@pytest.mark.resnet
+@pytest.mark.recognition_custom
+def test_custom_recognition_full_model_resnet():
+    predictor = CustomImagePrediction()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadFullModel(num_objects=10)
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "9.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], str)
+
+
+@pytest.mark.densenet
+@pytest.mark.recognition_custom
+def test_custom_recognition_model_densenet():
+    predictor = CustomImagePrediction()
+    predictor.setModelTypeAsDenseNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_densenet-0.763500.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadModel(num_objects=10)
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "9.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], str)
+
+
+
+
+@pytest.mark.resnet
+@pytest.mark.recognition_custom
+@pytest.mark.recognition_multi
+def test_custom_recognition_model_resnet_multi():
+    predictor = CustomImagePrediction()
+    predictor.setModelTypeAsResNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_resnet.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadModel(num_objects=10)
+    images_to_image_array()
+    result_array = predictor.predictMultipleImages(sent_images_array=all_images_array)
+
+    assert isinstance(result_array, list)
+    for result in result_array:
+        assert "predictions" in result
+        assert "percentage_probabilities" in result
+        assert isinstance(result["predictions"], list)
+        assert isinstance(result["percentage_probabilities"], list)
+        assert isinstance(result["predictions"][0], str)
+        assert isinstance(result["percentage_probabilities"][0], str)
+
+
+@pytest.mark.resnet
+@pytest.mark.recognition_custom
+@pytest.mark.recognition_multi
+def test_custom_recognition_full_model_resnet_multi():
+    predictor = CustomImagePrediction()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_full_resnet_ex-001_acc-0.119792.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadFullModel(num_objects=10)
+    images_to_image_array()
+    result_array = predictor.predictMultipleImages(sent_images_array=all_images_array)
+
+    assert isinstance(result_array, list)
+    for result in result_array:
+        assert "predictions" in result
+        assert "percentage_probabilities" in result
+        assert isinstance(result["predictions"], list)
+        assert isinstance(result["percentage_probabilities"], list)
+        assert isinstance(result["predictions"][0], str)
+        assert isinstance(result["percentage_probabilities"][0], str)
+
+
+@pytest.mark.densenet
+@pytest.mark.recognition_custom
+@pytest.mark.recognition_multi
+def test_custom_recognition_model_densenet_multi():
+    predictor = CustomImagePrediction()
+    predictor.setModelTypeAsDenseNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "idenprof_densenet-0.763500.h5"))
+    predictor.setJsonPath(model_json=os.path.join(main_folder, "data-json", "idenprof.json"))
+    predictor.loadModel(num_objects=10)
+    images_to_image_array()
+    result_array = predictor.predictMultipleImages(sent_images_array=all_images_array)
+
+    assert isinstance(result_array, list)
+    for result in result_array:
+        assert "predictions" in result
+        assert "percentage_probabilities" in result
+        assert isinstance(result["predictions"], list)
+        assert isinstance(result["percentage_probabilities"], list)
+        assert isinstance(result["predictions"][0], str)
+        assert isinstance(result["percentage_probabilities"][0], str)

test/test_image_recognition.py

@@ -0,0 +1,131 @@
+from imageai.Prediction import ImagePrediction
+import os
+import cv2
+import pytest
+from os.path import dirname
+# wget /data-models/ model_file.h5
+
+main_folder = os.getcwd()
+
+@pytest.mark.squeezenet
+@pytest.mark.recognition
+def test_recognition_model_squeezenet():
+
+    print("TOP FOLDER: ", main_folder)
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsSqueezeNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.resnet
+@pytest.mark.recognition
+def test_recognition_model_resnet():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsResNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.inceptionv3
+@pytest.mark.recognition
+def test_recognition_model_inceptionv3():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsInceptionV3()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.densenet
+@pytest.mark.recognition
+def test_recognition_model_densenet():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsDenseNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
+    predictor.loadModel()
+    predictions, probabilities = predictor.predictImage(image_input=os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+
+@pytest.mark.squeezenet
+@pytest.mark.recognition
+def test_recognition_model_squeezenet_array_input():
+
+
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsSqueezeNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "squeezenet_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    image_array = cv2.imread(os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+    predictions, probabilities = predictor.predictImage(image_input=image_array, input_type="array")
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.resnet
+@pytest.mark.recognition
+def test_recognition_model_resnet_array_input():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsResNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "resnet50_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    image_array = cv2.imread(os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+    predictions, probabilities = predictor.predictImage(image_input=image_array, input_type="array")
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.inceptionv3
+@pytest.mark.recognition
+def test_recognition_model_inceptionv3_array_input():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsInceptionV3()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "inception_v3_weights_tf_dim_ordering_tf_kernels.h5"))
+    predictor.loadModel()
+    image_array = cv2.imread(os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+    predictions, probabilities = predictor.predictImage(image_input=image_array, input_type="array")
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+@pytest.mark.densenet
+@pytest.mark.recognition
+def test_recognition_model_densenet_array_input():
+    predictor = ImagePrediction()
+    predictor.setModelTypeAsDenseNet()
+    predictor.setModelPath(os.path.join(main_folder, "data-models", "DenseNet-BC-121-32.h5"))
+    predictor.loadModel()
+    image_array = cv2.imread(os.path.join(main_folder, main_folder, "data-images", "1.jpg"))
+    predictions, probabilities = predictor.predictImage(image_input=image_array, input_type="array")
+
+    assert isinstance(predictions, list)
+    assert isinstance(probabilities, list)
+    assert isinstance(predictions[0], str)
+    assert isinstance(probabilities[0], float)
+
+test_recognition_model_squeezenet()