Custom Detection Models - Training and Inference

.gitignore

@@ -0,0 +1,6 @@
+*.db
+.idea
+*.pyc
+imageai.egg-info
+dist
+build

README.md

@@ -4,7 +4,7 @@ A python library built to empower developers to build applications and systems w
 
 <img src="logo1.png" style="width: 500px; height: auto; margin-left: 50px; " /> <br>
 
-A <b>DeepQuest AI</b> project <a href="https://deepquestai.com" >https://deepquestai.com </a>.
+An <b>DeepQuest AI</b> project <a href="https://deepquestai.com" >https://deepquestai.com </a>.
 Developed and Maintained by [Moses Olafenwa](https://twitter.com/OlafenwaMoses) and [John Olafenwa](https://twitter.com/johnolafenwa), brothers, creators of [TorchFusion](https://github.com/johnolafenwa/TorchFusion), Authors of [Introduction to Deep Computer Vision](https://john.aicommons.science/deepvision) and creators of [DeepStack AI Server](https://deepstack.cc).
 <hr>
 

build/lib/imageai/Prediction/Custom/__init__.py

@@ -81,7 +81,7 @@ def setModelTypeAsInceptionV3(self):
 
     def setDataDirectory(self, data_directory=""):
         """
-                 'setDataDirectory()' is required to set the path to which the data/dataset to be used for
+        'setDataDirectory()' is required to set the path to which the data/dataset to be used for
                  training is kept. The directory can have any name, but it must have 'train' and 'test'
                  sub-directory. In the 'train' and 'test' sub-directories, there must be sub-directories
                  with each having it's name corresponds to the name/label of the object whose images are
@@ -99,8 +99,9 @@ def setDataDirectory(self, data_directory=""):
                         >> class4 >> class4_test_images
                         >> class5 >> class5_test_images
 
-                :return:
-                """
+        :param data_directory:
+        :return:
+        """
 
         self.__data_dir = data_directory
         self.__train_dir = os.path.join(self.__data_dir, "train")
@@ -139,7 +140,7 @@ def lr_schedule(self, epoch):
     def trainModel(self, num_objects, num_experiments=200, enhance_data=False, batch_size = 32, initial_learning_rate=1e-3, show_network_summary=False, training_image_size = 224, continue_from_model=None, transfer_from_model=None, transfer_with_full_training=True, initial_num_objects = None, save_full_model = False):
 
         """
-                 'trainModel()' function starts the actual training. It accepts the following values:
+                 'trainModel()' function starts the model actual training. It accepts the following values:
                  - num_objects , which is the number of classes present in the dataset that is to be used for training
                  - num_experiments , also known as epochs, it is the number of times the network will train on all the training dataset
                  - enhance_data (optional) , this is used to modify the dataset and create more instance of the training set to enhance the training result

examples/custom_detection.py

@@ -0,0 +1,22 @@
+from imageai.Detection.Custom import CustomObjectDetection
+
+detector = CustomObjectDetection()
+detector.setModelTypeAsYOLOv3()
+detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+detector.loadModel()
+detections = detector.detectObjectsFromImage(input_image="holo2.jpg", output_image_path="holo2-detected.jpg")
+for detection in detections:
+    print(detection["name"], " : ", detection["percentage_probability"], " : ", detection["box_points"])
+
+
+"""
+EXAMPLE RESULT
+
+hololens  :  39.69653248786926  :  [611, 74, 751, 154]
+hololens  :  87.6643180847168  :  [23, 46, 90, 79]
+hololens  :  89.25175070762634  :  [191, 66, 243, 95]
+hololens  :  64.49641585350037  :  [437, 81, 514, 133]
+hololens  :  91.78624749183655  :  [380, 113, 423, 138]
+
+"""

examples/custom_detection_array_input_output.py

@@ -0,0 +1,29 @@
+from imageai.Detection.Custom import CustomObjectDetection
+import cv2
+
+image_array = cv2.imread("holo2.jpg")
+
+detector = CustomObjectDetection()
+detector.setModelTypeAsYOLOv3()
+detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+detector.loadModel()
+detected_image, detections = detector.detectObjectsFromImage(input_image=image_array, input_type="array", output_type="array")
+
+for eachObject in detections:
+    print(eachObject["name"], " : ", eachObject["percentage_probability"], " : ", eachObject["box_points"])
+
+cv2.imshow("Main Image", detected_image)
+cv2.waitKey()
+cv2.destroyAllWindows()
+
+
+"""
+SAMPLE RESULT
+
+hololens  :  39.69653248786926  :  [611, 74, 751, 154]
+hololens  :  87.6643180847168  :  [23, 46, 90, 79]
+hololens  :  89.25175070762634  :  [191, 66, 243, 95]
+hololens  :  64.49641585350037  :  [437, 81, 514, 133]
+hololens  :  91.78624749183655  :  [380, 113, 423, 138]
+"""

examples/custom_detection_extract_objects.py

@@ -0,0 +1,37 @@
+from imageai.Detection.Custom import CustomObjectDetection
+
+detector = CustomObjectDetection()
+detector.setModelTypeAsYOLOv3()
+detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+detector.loadModel()
+detections, extracted_objects_array = detector.detectObjectsFromImage(input_image="holo2.jpg", output_image_path="holo2-detected.jpg", extract_detected_objects=True)
+
+for detection, object_path in zip(detections, extracted_objects_array):
+    print(object_path)
+    print(detection["name"], " : ", detection["percentage_probability"], " : ", detection["box_points"])
+    print("---------------")
+
+"""
+SAMPLE RESULT
+
+holo2-detected.jpg-objects\hololens-1.jpg
+hololens  :  39.69653248786926  :  [611, 74, 751, 154]
+---------------
+
+holo2-detected.jpg-objects\hololens-1.jpg
+hololens  :  87.6643180847168  :  [23, 46, 90, 79]
+---------------
+
+holo2-detected.jpg-objects\hololens-1.jpg
+hololens  :  89.25175070762634  :  [191, 66, 243, 95]
+---------------
+
+holo2-detected.jpg-objects\hololens-1.jpg
+hololens  :  64.49641585350037  :  [437, 81, 514, 133]
+---------------
+
+holo2-detected.jpg-objects\hololens-1.jpg
+hololens  :  91.78624749183655  :  [380, 113, 423, 138]
+---------------
+"""

examples/custom_detection_from_array_extract_objects_array.py

@@ -0,0 +1,37 @@
+from imageai.Detection.Custom import CustomObjectDetection
+import cv2
+
+image_array = cv2.imread("holo2.jpg")
+
+detector = CustomObjectDetection()
+detector.setModelTypeAsYOLOv3()
+detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+detector.loadModel()
+detected_image, detections, extracted_objects = detector.detectObjectsFromImage(input_image=image_array, extract_detected_objects=True, input_type="array", output_type="array")
+
+
+for eachObject in detections:
+    print(eachObject["name"], " : ", eachObject["percentage_probability"], " : ", eachObject["box_points"])
+
+
+cv2.imshow("Main Image", detected_image)
+count = 0
+for img in extracted_objects:
+    count += 1
+
+    cv2.imshow("Window" + str(count), img)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
+
+"""
+SAMPLE RESULT
+
+hololens  :  39.69653248786926  :  [611, 74, 751, 154]
+hololens  :  87.6643180847168  :  [23, 46, 90, 79]
+hololens  :  89.25175070762634  :  [191, 66, 243, 95]
+hololens  :  64.49641585350037  :  [437, 81, 514, 133]
+hololens  :  91.78624749183655  :  [380, 113, 423, 138]
+"""

examples/custom_detection_from_file_extract_objects_array.py

@@ -0,0 +1,36 @@
+from imageai.Detection.Custom import CustomObjectDetection
+import cv2
+
+
+detector = CustomObjectDetection()
+detector.setModelTypeAsYOLOv3()
+detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+detector.loadModel()
+detected_image, detections, extracted_objects  = detector.detectObjectsFromImage(input_image="holo2.jpg", extract_detected_objects=True, output_type="array")
+
+
+for eachObject in detections:
+    print(eachObject["name"], " : ", eachObject["percentage_probability"], " : ", eachObject["box_points"])
+
+cv2.imshow("Main Image", detected_image)
+count = 0
+for img in extracted_objects:
+    count += 1
+
+    cv2.imshow("Window" + str(count), img)
+
+cv2.waitKey()
+cv2.destroyAllWindows()
+
+
+"""
+SAMPLE RESULT
+
+hololens  :  39.69653248786926  :  [611, 74, 751, 154]
+hololens  :  87.6643180847168  :  [23, 46, 90, 79]
+hololens  :  89.25175070762634  :  [191, 66, 243, 95]
+hololens  :  64.49641585350037  :  [437, 81, 514, 133]
+hololens  :  91.78624749183655  :  [380, 113, 423, 138]
+
+"""

examples/custom_detection_multi_model_evaluation.py

@@ -0,0 +1,70 @@
+from imageai.Detection.Custom import DetectionModelTrainer
+
+trainer = DetectionModelTrainer()
+trainer.setModelTypeAsYOLOv3()
+trainer.setDataDirectory(data_directory="hololens")
+trainer.evaluateModel(model_path="hololens/models", json_path="hololens/json/detection_config.json", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5)
+
+
+
+"""
+SAMPLE RESULT
+
+
+Model File:  hololens/models/detection_model-ex-07--loss-4.42.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9231
+mAP: 0.9231
+===============================
+Model File:  hololens/models/detection_model-ex-10--loss-3.95.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9725
+mAP: 0.9725
+===============================
+Model File:  hololens/models/detection_model-ex-05--loss-5.26.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9204
+mAP: 0.9204
+===============================
+Model File:  hololens/models/detection_model-ex-03--loss-6.44.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.8120
+mAP: 0.8120
+===============================
+Model File:  hololens/models/detection_model-ex-18--loss-2.96.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9431
+mAP: 0.9431
+===============================
+Model File:  hololens/models/detection_model-ex-17--loss-3.10.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9404
+mAP: 0.9404
+===============================
+Model File:  hololens/models/detection_model-ex-08--loss-4.16.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9725
+mAP: 0.9725
+===============================
+"""

examples/custom_detection_single_model_evaluation.py

@@ -0,0 +1,25 @@
+from imageai.Detection.Custom import DetectionModelTrainer
+
+trainer = DetectionModelTrainer()
+trainer.setModelTypeAsYOLOv3()
+trainer.setDataDirectory(data_directory="hololens")
+trainer.evaluateModel(model_path="hololens-ex-60--loss-2.76.h5", json_path="detection_config.json", iou_threshold=0.5, object_threshold=0.3, nms_threshold=0.5)
+
+# download JSON file via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+# download detection model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+
+
+
+"""
+SAMPLE RESULT
+
+
+Model File:  hololens_detection_model-ex-09--loss-4.01.h5 
+
+Using IoU :  0.5
+Using Object Threshold :  0.3
+Using Non-Maximum Suppression :  0.5
+hololens: 0.9613
+mAP: 0.9613
+===============================
+"""

examples/custom_detection_train.py

@@ -0,0 +1,40 @@
+from imageai.Detection.Custom import DetectionModelTrainer
+
+trainer = DetectionModelTrainer()
+trainer.setModelTypeAsYOLOv3()
+trainer.setDataDirectory(data_directory="hololens")
+trainer.setTrainConfig(object_names_array=["hololens"], batch_size=4, num_experiments=200, train_from_pretrained_model="pretrained-yolov3.h5") #download pre-trained model via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/pretrained-yolov3.h5
+trainer.trainModel()
+
+
+
+"""
+SAMPLE RESULT
+
+Using TensorFlow backend.
+Generating anchor boxes for training images and annotation...
+Average IOU for 9 anchors: 0.78
+Anchor Boxes generated.
+Detection configuration saved in  hololens/json/detection_config.json
+Training on: 	['hololens']
+Training with Batch Size:  4
+Number of Experiments:  200
+
+
+
+Epoch 1/200
+ - 733s - loss: 34.8253 - yolo_layer_1_loss: 6.0920 - yolo_layer_2_loss: 11.1064 - yolo_layer_3_loss: 17.6269 - val_loss: 20.5028 - val_yolo_layer_1_loss: 4.0171 - val_yolo_layer_2_loss: 7.5175 - val_yolo_layer_3_loss: 8.9683
+Epoch 2/200
+ - 648s - loss: 11.1396 - yolo_layer_1_loss: 2.1209 - yolo_layer_2_loss: 4.0063 - yolo_layer_3_loss: 5.0124 - val_loss: 7.6188 - val_yolo_layer_1_loss: 1.8513 - val_yolo_layer_2_loss: 2.2446 - val_yolo_layer_3_loss: 3.5229
+Epoch 3/200
+ - 674s - loss: 6.4360 - yolo_layer_1_loss: 1.3500 - yolo_layer_2_loss: 2.2343 - yolo_layer_3_loss: 2.8518 - val_loss: 7.2326 - val_yolo_layer_1_loss: 1.8762 - val_yolo_layer_2_loss: 2.3802 - val_yolo_layer_3_loss: 2.9762
+Epoch 4/200
+ - 634s - loss: 5.3801 - yolo_layer_1_loss: 1.0323 - yolo_layer_2_loss: 1.7854 - yolo_layer_3_loss: 2.5624 - val_loss: 6.3730 - val_yolo_layer_1_loss: 1.4272 - val_yolo_layer_2_loss: 2.0534 - val_yolo_layer_3_loss: 2.8924
+Epoch 5/200
+ - 645s - loss: 5.2569 - yolo_layer_1_loss: 0.9953 - yolo_layer_2_loss: 1.8611 - yolo_layer_3_loss: 2.4005 - val_loss: 6.0458 - val_yolo_layer_1_loss: 1.7037 - val_yolo_layer_2_loss: 1.9754 - val_yolo_layer_3_loss: 2.3667
+Epoch 6/200
+ - 655s - loss: 4.7582 - yolo_layer_1_loss: 0.9959 - yolo_layer_2_loss: 1.5986 - yolo_layer_3_loss: 2.1637 - val_loss: 5.8313 - val_yolo_layer_1_loss: 1.1880 - val_yolo_layer_2_loss: 1.9962 - val_yolo_layer_3_loss: 2.6471
+Epoch 7/200
+
+"""
+

examples/custom_detection_video.py

@@ -0,0 +1,16 @@
+from imageai.Detection.Custom import CustomVideoObjectDetection
+import os
+
+execution_path = os.getcwd()
+
+video_detector = CustomVideoObjectDetection()
+video_detector.setModelTypeAsYOLOv3()
+video_detector.setModelPath("hololens-ex-60--loss-2.76.h5") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/hololens-ex-60--loss-2.76.h5
+video_detector.setJsonPath("detection_config.json") # download via https://github.com/OlafenwaMoses/ImageAI/releases/download/essential-v4/detection_config.json
+video_detector.loadModel()
+
+video_detector.detectObjectsFromVideo(input_file_path="holo1.mp4",
+                                          output_file_path=os.path.join(execution_path, "holo1-detected3"),
+                                          frames_per_second=20,
+                                          minimum_percentage_probability=40,
+                                          log_progress=True)

examples/detection_config.json

@@ -0,0 +1,9 @@
+{
+    "labels" : [
+        "hololens"
+    ],
+    "anchors" : [
+        [160, 171, 261, 167, 301, 285],
+        [78, 86, 104, 69, 138, 105],
+        [37, 32, 58, 61, 62, 44]]
+}

imageai.egg-info/PKG-INFO

@@ -1,6 +1,6 @@
 Metadata-Version: 1.0
 Name: imageai
-Version: 2.0.3
+Version: 2.1.0
 Summary: A python library built to empower developers to build applications and systems  with self-contained Computer Vision capabilities
 Home-page: https://github.com/OlafenwaMoses/ImageAI
 Author: Moses Olafenwa and John Olafenwa