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__init__.py
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import cv2
from imageai.Detection.keras_retinanet.models.resnet import resnet50_retinanet
from imageai.Detection.keras_retinanet.utils.image import read_image_bgr, read_image_array, read_image_stream, \
preprocess_image, resize_image
from imageai.Detection.keras_retinanet.utils.visualization import draw_box, draw_caption
from imageai.Detection.keras_retinanet.utils.colors import label_color
import matplotlib.pyplot as plt
import matplotlib.image as pltimage
import numpy as np
import tensorflow as tf
import os
from keras import backend as K
from keras.layers import Input
from PIL import Image
import colorsys
from imageai.Detection.YOLOv3.models import yolo_main, tiny_yolo_main
from imageai.Detection.YOLOv3.utils import letterbox_image, yolo_eval
def get_session():
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
return tf.Session(config=config)
class ObjectDetection:
"""
This is the object detection class for images in the ImageAI library. It provides support for RetinaNet
, YOLOv3 and TinyYOLOv3 object detection networks . After instantiating this class, you can set it's properties and
make object detections using it's pre-defined functions.
The following functions are required to be called before object detection can be made
* setModelPath()
* At least of of the following and it must correspond to the model set in the setModelPath()
[setModelTypeAsRetinaNet(), setModelTypeAsYOLOv3(), setModelTypeAsTinyYOLOv3()]
* loadModel() [This must be called once only before performing object detection]
Once the above functions have been called, you can call the detectObjectsFromImage() function of
the object detection instance object at anytime to obtain observable objects in any image.
"""
def __init__(self):
self.__modelType = ""
self.modelPath = ""
self.__modelPathAdded = False
self.__modelLoaded = False
self.__model_collection = []
# Instance variables for RetinaNet Model
self.__input_image_min = 1333
self.__input_image_max = 800
self.numbers_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
6: 'train',
7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign',
12: 'parking meter',
13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow',
20: 'elephant',
21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
27: 'tie',
28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball',
33: 'kite',
34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard',
38: 'tennis racket',
39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon',
45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot',
52: 'hot dog',
53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant',
59: 'bed',
60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote',
66: 'keyboard',
67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
72: 'refrigerator',
73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear',
78: 'hair drier',
79: 'toothbrush'}
# Unique instance variables for YOLOv3 and TinyYOLOv3 model
self.__yolo_iou = 0.45
self.__yolo_score = 0.1
self.__yolo_anchors = np.array(
[[10., 13.], [16., 30.], [33., 23.], [30., 61.], [62., 45.], [59., 119.], [116., 90.], [156., 198.],
[373., 326.]])
self.__yolo_model_image_size = (416, 416)
self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes = "", "", ""
self.sess = K.get_session()
# Unique instance variables for TinyYOLOv3.
self.__tiny_yolo_anchors = np.array(
[[10., 14.], [23., 27.], [37., 58.], [81., 82.], [135., 169.], [344., 319.]])
def setModelTypeAsRetinaNet(self):
"""
'setModelTypeAsRetinaNet()' is used to set the model type to the RetinaNet model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "retinanet"
def setModelTypeAsYOLOv3(self):
"""
'setModelTypeAsYOLOv3()' is used to set the model type to the YOLOv3 model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "yolov3"
def setModelTypeAsTinyYOLOv3(self):
"""
'setModelTypeAsTinyYOLOv3()' is used to set the model type to the TinyYOLOv3 model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "tinyyolov3"
def setModelPath(self, model_path):
"""
'setModelPath()' function is required and is used to set the file path to a RetinaNet
object detection model trained on the COCO dataset.
:param model_path:
:return:
"""
if (self.__modelPathAdded == False):
self.modelPath = model_path
self.__modelPathAdded = True
def loadModel(self, detection_speed="normal"):
"""
'loadModel()' function is required and is used to load the model structure into the program from the file path defined
in the setModelPath() function. This function receives an optional value which is "detection_speed".
The value is used to reduce the time it takes to detect objects in an image, down to about a 10% of the normal time, with
with just slight reduction in the number of objects detected.
* prediction_speed (optional); Acceptable values are "normal", "fast", "faster", "fastest" and "flash"
:param detection_speed:
:return:
"""
if (self.__modelType == "retinanet"):
if (detection_speed == "normal"):
self.__input_image_min = 800
self.__input_image_max = 1333
elif (detection_speed == "fast"):
self.__input_image_min = 400
self.__input_image_max = 700
elif (detection_speed == "faster"):
self.__input_image_min = 300
self.__input_image_max = 500
elif (detection_speed == "fastest"):
self.__input_image_min = 200
self.__input_image_max = 350
elif (detection_speed == "flash"):
self.__input_image_min = 100
self.__input_image_max = 250
elif (self.__modelType == "yolov3"):
if (detection_speed == "normal"):
self.__yolo_model_image_size = (416, 416)
elif (detection_speed == "fast"):
self.__yolo_model_image_size = (320, 320)
elif (detection_speed == "faster"):
self.__yolo_model_image_size = (208, 208)
elif (detection_speed == "fastest"):
self.__yolo_model_image_size = (128, 128)
elif (detection_speed == "flash"):
self.__yolo_model_image_size = (96, 96)
elif (self.__modelType == "tinyyolov3"):
if (detection_speed == "normal"):
self.__yolo_model_image_size = (832, 832)
elif (detection_speed == "fast"):
self.__yolo_model_image_size = (576, 576)
elif (detection_speed == "faster"):
self.__yolo_model_image_size = (416, 416)
elif (detection_speed == "fastest"):
self.__yolo_model_image_size = (320, 320)
elif (detection_speed == "flash"):
self.__yolo_model_image_size = (272, 272)
if (self.__modelLoaded == False):
if (self.__modelType == ""):
raise ValueError("You must set a valid model type before loading the model.")
elif (self.__modelType == "retinanet"):
model = resnet50_retinanet(num_classes=80)
model.load_weights(self.modelPath)
self.__model_collection.append(model)
self.__modelLoaded = True
elif (self.__modelType == "yolov3"):
model = yolo_main(Input(shape=(None, None, 3)), len(self.__yolo_anchors) // 3,
len(self.numbers_to_names))
model.load_weights(self.modelPath)
hsv_tuples = [(x / len(self.numbers_to_names), 1., 1.)
for x in range(len(self.numbers_to_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
self.__yolo_input_image_shape = K.placeholder(shape=(2,))
self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes = yolo_eval(model.output,
self.__yolo_anchors,
len(self.numbers_to_names),
self.__yolo_input_image_shape,
score_threshold=self.__yolo_score,
iou_threshold=self.__yolo_iou)
self.__model_collection.append(model)
self.__modelLoaded = True
elif (self.__modelType == "tinyyolov3"):
model = tiny_yolo_main(Input(shape=(None, None, 3)), len(self.__tiny_yolo_anchors) // 2,
len(self.numbers_to_names))
model.load_weights(self.modelPath)
hsv_tuples = [(x / len(self.numbers_to_names), 1., 1.)
for x in range(len(self.numbers_to_names))]
self.colors = list(map(lambda x: colorsys.hsv_to_rgb(*x), hsv_tuples))
self.colors = list(
map(lambda x: (int(x[0] * 255), int(x[1] * 255), int(x[2] * 255)),
self.colors))
np.random.seed(10101)
np.random.shuffle(self.colors)
np.random.seed(None)
self.__yolo_input_image_shape = K.placeholder(shape=(2,))
self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes = yolo_eval(model.output,
self.__tiny_yolo_anchors,
len(self.numbers_to_names),
self.__yolo_input_image_shape,
score_threshold=self.__yolo_score,
iou_threshold=self.__yolo_iou)
self.__model_collection.append(model)
self.__modelLoaded = True
def detectObjectsFromImage(self, input_image="", output_image_path="", input_type="file", output_type="file",
extract_detected_objects=False, minimum_percentage_probability=50,
display_percentage_probability=True, display_object_name=True):
"""
'detectObjectsFromImage()' function is used to detect objects observable in the given image path:
* input_image , which can be file to path, image numpy array or image file stream
* output_image_path (only if output_type = file) , file path to the output image that will contain the detection boxes and label, if output_type="file"
* input_type (optional) , file path/numpy array/image file stream of the image. Acceptable values are "file", "array" and "stream"
* output_type (optional) , file path/numpy array/image file stream of the image. Acceptable values are "file" and "array"
* extract_detected_objects (optional) , option to save each object detected individually as an image and return an array of the objects' image path.
* minimum_percentage_probability (optional, 50 by default) , option to set the minimum percentage probability for nominating a detected object for output.
* display_percentage_probability (optional, True by default), option to show or hide the percentage probability of each object in the saved/returned detected image
* display_display_object_name (optional, True by default), option to show or hide the name of each object in the saved/returned detected image
The values returned by this function depends on the parameters parsed. The possible values returnable
are stated as below
- If extract_detected_objects = False or at its default value and output_type = 'file' or
at its default value, you must parse in the 'output_image_path' as a string to the path you want
the detected image to be saved. Then the function will return:
1. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
- If extract_detected_objects = False or at its default value and output_type = 'array' ,
Then the function will return:
1. a numpy array of the detected image
2. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
- If extract_detected_objects = True and output_type = 'file' or
at its default value, you must parse in the 'output_image_path' as a string to the path you want
the detected image to be saved. Then the function will return:
1. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
2. an array of string paths to the image of each object extracted from the image
- If extract_detected_objects = True and output_type = 'array', the the function will return:
1. a numpy array of the detected image
2. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
3. an array of numpy arrays of each object detected in the image
:param input_image:
:param output_image_path:
:param input_type:
:param output_type:
:param extract_detected_objects:
:param minimum_percentage_probability:
:param display_percentage_probability:
:param display_object_name
:return output_objects_array:
:return detected_copy:
:return detected_detected_objects_image_array:
"""
if (self.__modelLoaded == False):
raise ValueError("You must call the loadModel() function before making object detection.")
elif (self.__modelLoaded == True):
try:
if (self.__modelType == "retinanet"):
output_objects_array = []
detected_objects_image_array = []
if (input_type == "file"):
image = read_image_bgr(input_image)
elif (input_type == "array"):
image = read_image_array(input_image)
elif (input_type == "stream"):
image = read_image_stream(input_image)
detected_copy = image.copy()
detected_copy = cv2.cvtColor(detected_copy, cv2.COLOR_BGR2RGB)
detected_copy2 = image.copy()
detected_copy2 = cv2.cvtColor(detected_copy2, cv2.COLOR_BGR2RGB)
image = preprocess_image(image)
image, scale = resize_image(image, min_side=self.__input_image_min, max_side=self.__input_image_max)
model = self.__model_collection[0]
_, _, detections = model.predict_on_batch(np.expand_dims(image, axis=0))
predicted_numbers = np.argmax(detections[0, :, 4:], axis=1)
scores = detections[0, np.arange(detections.shape[1]), 4 + predicted_numbers]
detections[0, :, :4] /= scale
min_probability = minimum_percentage_probability / 100
counting = 0
for index, (label, score), in enumerate(zip(predicted_numbers, scores)):
if score < min_probability:
continue
counting += 1
objects_dir = output_image_path + "-objects"
if (extract_detected_objects == True and output_type == "file"):
if (os.path.exists(objects_dir) == False):
os.mkdir(objects_dir)
color = label_color(label)
detection_details = detections[0, index, :4].astype(int)
draw_box(detected_copy, detection_details, color=color)
if (display_object_name == True and display_percentage_probability == True):
caption = "{} {:.3f}".format(self.numbers_to_names[label], (score * 100))
draw_caption(detected_copy, detection_details, caption)
elif (display_object_name == True):
caption = "{} ".format(self.numbers_to_names[label])
draw_caption(detected_copy, detection_details, caption)
elif (display_percentage_probability == True):
caption = " {:.3f}".format((score * 100))
draw_caption(detected_copy, detection_details, caption)
each_object_details = {}
each_object_details["name"] = self.numbers_to_names[label]
each_object_details["percentage_probability"] = score * 100
each_object_details["box_points"] = detection_details
output_objects_array.append(each_object_details)
if (extract_detected_objects == True):
splitted_copy = detected_copy2.copy()[detection_details[1]:detection_details[3],
detection_details[0]:detection_details[2]]
if (output_type == "file"):
splitted_image_path = os.path.join(objects_dir,
self.numbers_to_names[label] + "-" + str(
counting) + ".jpg")
pltimage.imsave(splitted_image_path, splitted_copy)
detected_objects_image_array.append(splitted_image_path)
elif (output_type == "array"):
detected_objects_image_array.append(splitted_copy)
if (output_type == "file"):
pltimage.imsave(output_image_path, detected_copy)
if (extract_detected_objects == True):
if (output_type == "file"):
return output_objects_array, detected_objects_image_array
elif (output_type == "array"):
return detected_copy, output_objects_array, detected_objects_image_array
else:
if (output_type == "file"):
return output_objects_array
elif (output_type == "array"):
return detected_copy, output_objects_array
elif (self.__modelType == "yolov3" or self.__modelType == "tinyyolov3"):
output_objects_array = []
detected_objects_image_array = []
if (input_type == "file"):
image = Image.open(input_image)
input_image = read_image_bgr(input_image)
elif (input_type == "array"):
image = Image.fromarray(np.uint8(input_image))
input_image = read_image_array(input_image)
elif (input_type == "stream"):
image = Image.open(input_image)
input_image = read_image_stream(input_image)
detected_copy = input_image
detected_copy = cv2.cvtColor(detected_copy, cv2.COLOR_BGR2RGB)
detected_copy2 = input_image
detected_copy2 = cv2.cvtColor(detected_copy2, cv2.COLOR_BGR2RGB)
new_image_size = (self.__yolo_model_image_size[0] - (self.__yolo_model_image_size[0] % 32),
self.__yolo_model_image_size[1] - (self.__yolo_model_image_size[1] % 32))
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype="float32")
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
model = self.__model_collection[0]
out_boxes, out_scores, out_classes = self.sess.run(
[self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes],
feed_dict={
model.input: image_data,
self.__yolo_input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
min_probability = minimum_percentage_probability / 100
counting = 0
for a, b in reversed(list(enumerate(out_classes))):
predicted_class = self.numbers_to_names[b]
box = out_boxes[a]
score = out_scores[a]
if score < min_probability:
continue
counting += 1
objects_dir = output_image_path + "-objects"
if (extract_detected_objects == True and output_type == "file"):
if (os.path.exists(objects_dir) == False):
os.mkdir(objects_dir)
label = "{} {:.2f}".format(predicted_class, score)
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
try:
color = label_color(b)
except:
color = (255, 0, 0)
detection_details = (left, top, right, bottom)
draw_box(detected_copy, detection_details, color=color)
if (display_object_name == True and display_percentage_probability == True):
draw_caption(detected_copy, detection_details, label)
elif (display_object_name == True):
draw_caption(detected_copy, detection_details, predicted_class)
elif (display_percentage_probability == True):
draw_caption(detected_copy, detection_details, str(score * 100))
each_object_details = {}
each_object_details["name"] = predicted_class
each_object_details["percentage_probability"] = score * 100
each_object_details["box_points"] = detection_details
output_objects_array.append(each_object_details)
if (extract_detected_objects == True):
splitted_copy = detected_copy2.copy()[detection_details[1]:detection_details[3],
detection_details[0]:detection_details[2]]
if (output_type == "file"):
splitted_image_path = os.path.join(objects_dir,
predicted_class + "-" + str(
counting) + ".jpg")
pltimage.imsave(splitted_image_path, splitted_copy)
detected_objects_image_array.append(splitted_image_path)
elif (output_type == "array"):
detected_objects_image_array.append(splitted_copy)
if (output_type == "file"):
pltimage.imsave(output_image_path, detected_copy)
if (extract_detected_objects == True):
if (output_type == "file"):
return output_objects_array, detected_objects_image_array
elif (output_type == "array"):
return detected_copy, output_objects_array, detected_objects_image_array
else:
if (output_type == "file"):
return output_objects_array
elif (output_type == "array"):
return detected_copy, output_objects_array
except:
raise ValueError(
"Ensure you specified correct input image, input type, output type and/or output image path ")
def CustomObjects(self, person=False, bicycle=False, car=False, motorcycle=False, airplane=False,
bus=False, train=False, truck=False, boat=False, traffic_light=False, fire_hydrant=False,
stop_sign=False,
parking_meter=False, bench=False, bird=False, cat=False, dog=False, horse=False, sheep=False,
cow=False, elephant=False, bear=False, zebra=False,
giraffe=False, backpack=False, umbrella=False, handbag=False, tie=False, suitcase=False,
frisbee=False, skis=False, snowboard=False,
sports_ball=False, kite=False, baseball_bat=False, baseball_glove=False, skateboard=False,
surfboard=False, tennis_racket=False,
bottle=False, wine_glass=False, cup=False, fork=False, knife=False, spoon=False, bowl=False,
banana=False, apple=False, sandwich=False, orange=False,
broccoli=False, carrot=False, hot_dog=False, pizza=False, donot=False, cake=False, chair=False,
couch=False, potted_plant=False, bed=False,
dining_table=False, toilet=False, tv=False, laptop=False, mouse=False, remote=False,
keyboard=False, cell_phone=False, microwave=False,
oven=False, toaster=False, sink=False, refrigerator=False, book=False, clock=False, vase=False,
scissors=False, teddy_bear=False, hair_dryer=False,
toothbrush=False):
"""
The 'CustomObjects()' function allows you to handpick the type of objects you want to detect
from an image. The objects are pre-initiated in the function variables and predefined as 'False',
which you can easily set to true for any number of objects available. This function
returns a dictionary which must be parsed into the 'detectCustomObjectsFromImage()'. Detecting
custom objects only happens when you call the function 'detectCustomObjectsFromImage()'
* true_values_of_objects (array); Acceptable values are 'True' and False for all object values present
:param boolean_values:
:return: custom_objects_dict
"""
custom_objects_dict = {}
input_values = [person, bicycle, car, motorcycle, airplane,
bus, train, truck, boat, traffic_light, fire_hydrant, stop_sign,
parking_meter, bench, bird, cat, dog, horse, sheep, cow, elephant, bear, zebra,
giraffe, backpack, umbrella, handbag, tie, suitcase, frisbee, skis, snowboard,
sports_ball, kite, baseball_bat, baseball_glove, skateboard, surfboard, tennis_racket,
bottle, wine_glass, cup, fork, knife, spoon, bowl, banana, apple, sandwich, orange,
broccoli, carrot, hot_dog, pizza, donot, cake, chair, couch, potted_plant, bed,
dining_table, toilet, tv, laptop, mouse, remote, keyboard, cell_phone, microwave,
oven, toaster, sink, refrigerator, book, clock, vase, scissors, teddy_bear, hair_dryer,
toothbrush]
actual_labels = ["person", "bicycle", "car", "motorcycle", "airplane",
"bus", "train", "truck", "boat", "traffic light", "fire hydrant", "stop sign",
"parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear",
"zebra",
"giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis",
"snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket",
"bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich",
"orange",
"broccoli", "carrot", "hot dog", "pizza", "donot", "cake", "chair", "couch", "potted plant",
"bed",
"dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
"microwave",
"oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
"hair dryer",
"toothbrush"]
for input_value, actual_label in zip(input_values, actual_labels):
if (input_value == True):
custom_objects_dict[actual_label] = "valid"
else:
custom_objects_dict[actual_label] = "invalid"
return custom_objects_dict
def detectCustomObjectsFromImage(self, custom_objects=None, input_image="", output_image_path="", input_type="file",
output_type="file", extract_detected_objects=False,
minimum_percentage_probability=50, display_percentage_probability=True,
display_object_name=True):
"""
'detectCustomObjectsFromImage()' function is used to detect predefined objects observable in the given image path:
* custom_objects , an instance of the CustomObject class to filter which objects to detect
* input_image , which can be file to path, image numpy array or image file stream
* output_image_path , file path to the output image that will contain the detection boxes and label, if output_type="file"
* input_type (optional) , file path/numpy array/image file stream of the image. Acceptable values are "file", "array" and "stream"
* output_type (optional) , file path/numpy array/image file stream of the image. Acceptable values are "file" and "array"
* extract_detected_objects (optional, False by default) , option to save each object detected individually as an image and return an array of the objects' image path.
* minimum_percentage_probability (optional, 50 by default) , option to set the minimum percentage probability for nominating a detected object for output.
* display_percentage_probability (optional, True by default), option to show or hide the percentage probability of each object in the saved/returned detected image
* display_display_object_name (optional, True by default), option to show or hide the name of each object in the saved/returned detected image
The values returned by this function depends on the parameters parsed. The possible values returnable
are stated as below
- If extract_detected_objects = False or at its default value and output_type = 'file' or
at its default value, you must parse in the 'output_image_path' as a string to the path you want
the detected image to be saved. Then the function will return:
1. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
- If extract_detected_objects = False or at its default value and output_type = 'array' ,
Then the function will return:
1. a numpy array of the detected image
2. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
- If extract_detected_objects = True and output_type = 'file' or
at its default value, you must parse in the 'output_image_path' as a string to the path you want
the detected image to be saved. Then the function will return:
1. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
2. an array of string paths to the image of each object extracted from the image
- If extract_detected_objects = True and output_type = 'array', the the function will return:
1. a numpy array of the detected image
2. an array of dictionaries, with each dictionary corresponding to the objects
detected in the image. Each dictionary contains the following property:
* name (string)
* percentage_probability (float)
* box_points (tuple of x1,y1,x2 and y2 coordinates)
3. an array of numpy arrays of each object detected in the image
:param input_image:
:param output_image_path:
:param input_type:
:param output_type:
:param extract_detected_objects:
:param minimum_percentage_probability:
:return output_objects_array:
:param display_percentage_probability:
:param display_object_name
:return detected_copy:
:return detected_detected_objects_image_array:
"""
if (self.__modelLoaded == False):
raise ValueError("You must call the loadModel() function before making object detection.")
elif (self.__modelLoaded == True):
try:
if (self.__modelType == "retinanet"):
output_objects_array = []
detected_objects_image_array = []
if (input_type == "file"):
image = read_image_bgr(input_image)
elif (input_type == "array"):
image = read_image_array(input_image)
elif (input_type == "stream"):
image = read_image_stream(input_image)
detected_copy = image.copy()
detected_copy = cv2.cvtColor(detected_copy, cv2.COLOR_BGR2RGB)
detected_copy2 = image.copy()
detected_copy2 = cv2.cvtColor(detected_copy2, cv2.COLOR_BGR2RGB)
image = preprocess_image(image)
image, scale = resize_image(image, min_side=self.__input_image_min, max_side=self.__input_image_max)
model = self.__model_collection[0]
_, _, detections = model.predict_on_batch(np.expand_dims(image, axis=0))
predicted_numbers = np.argmax(detections[0, :, 4:], axis=1)
scores = detections[0, np.arange(detections.shape[1]), 4 + predicted_numbers]
detections[0, :, :4] /= scale
min_probability = minimum_percentage_probability / 100
counting = 0
for index, (label, score), in enumerate(zip(predicted_numbers, scores)):
if score < min_probability:
continue
if (custom_objects != None):
check_name = self.numbers_to_names[label]
if (custom_objects[check_name] == "invalid"):
continue
counting += 1
objects_dir = output_image_path + "-objects"
if (extract_detected_objects == True and output_type == "file"):
if (os.path.exists(objects_dir) == False):
os.mkdir(objects_dir)
color = label_color(label)
detection_details = detections[0, index, :4].astype(int)
draw_box(detected_copy, detection_details, color=color)
if (display_object_name == True and display_percentage_probability == True):
caption = "{} {:.3f}".format(self.numbers_to_names[label], (score * 100))
draw_caption(detected_copy, detection_details, caption)
elif (display_object_name == True):
caption = "{} ".format(self.numbers_to_names[label])
draw_caption(detected_copy, detection_details, caption)
elif (display_percentage_probability == True):
caption = " {:.3f}".format((score * 100))
draw_caption(detected_copy, detection_details, caption)
each_object_details = {}
each_object_details["name"] = self.numbers_to_names[label]
each_object_details["percentage_probability"] = score * 100
each_object_details["box_points"] = detection_details
output_objects_array.append(each_object_details)
if (extract_detected_objects == True):
splitted_copy = detected_copy2.copy()[detection_details[1]:detection_details[3],
detection_details[0]:detection_details[2]]
if (output_type == "file"):
splitted_image_path = os.path.join(objects_dir,
self.numbers_to_names[label] + "-" + str(
counting) + ".jpg")
pltimage.imsave(splitted_image_path, splitted_copy)
detected_objects_image_array.append(splitted_image_path)
elif (output_type == "array"):
detected_objects_image_array.append(splitted_copy)
if (output_type == "file"):
pltimage.imsave(output_image_path, detected_copy)
if (extract_detected_objects == True):
if (output_type == "file"):
return output_objects_array, detected_objects_image_array
elif (output_type == "array"):
return detected_copy, output_objects_array, detected_objects_image_array
else:
if (output_type == "file"):
return output_objects_array
elif (output_type == "array"):
return detected_copy, output_objects_array
elif (self.__modelType == "yolov3" or self.__modelType == "tinyyolov3"):
output_objects_array = []
detected_objects_image_array = []
if (input_type == "file"):
image = Image.open(input_image)
input_image = read_image_bgr(input_image)
elif (input_type == "array"):
image = Image.fromarray(np.uint8(input_image))
input_image = read_image_array(input_image)
elif (input_type == "stream"):
image = Image.open(input_image)
input_image = read_image_stream(input_image)
detected_copy = input_image
detected_copy = cv2.cvtColor(detected_copy, cv2.COLOR_BGR2RGB)
detected_copy2 = input_image
detected_copy2 = cv2.cvtColor(detected_copy2, cv2.COLOR_BGR2RGB)
new_image_size = (self.__yolo_model_image_size[0] - (self.__yolo_model_image_size[0] % 32),
self.__yolo_model_image_size[1] - (self.__yolo_model_image_size[1] % 32))
boxed_image = letterbox_image(image, new_image_size)
image_data = np.array(boxed_image, dtype="float32")
image_data /= 255.
image_data = np.expand_dims(image_data, 0)
model = self.__model_collection[0]
out_boxes, out_scores, out_classes = self.sess.run(
[self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes],
feed_dict={
model.input: image_data,
self.__yolo_input_image_shape: [image.size[1], image.size[0]],
K.learning_phase(): 0
})
min_probability = minimum_percentage_probability / 100
counting = 0
for a, b in reversed(list(enumerate(out_classes))):
predicted_class = self.numbers_to_names[b]
box = out_boxes[a]
score = out_scores[a]
if score < min_probability:
continue
if (custom_objects != None):
if (custom_objects[predicted_class] == "invalid"):
continue
counting += 1
objects_dir = output_image_path + "-objects"
if (extract_detected_objects == True and output_type == "file"):
if (os.path.exists(objects_dir) == False):
os.mkdir(objects_dir)
label = "{} {:.2f}".format(predicted_class, score)
top, left, bottom, right = box
top = max(0, np.floor(top + 0.5).astype('int32'))
left = max(0, np.floor(left + 0.5).astype('int32'))
bottom = min(image.size[1], np.floor(bottom + 0.5).astype('int32'))
right = min(image.size[0], np.floor(right + 0.5).astype('int32'))
try:
color = label_color(b)
except:
color = (255, 0, 0)
detection_details = (left, top, right, bottom)
draw_box(detected_copy, detection_details, color=color)
if (display_object_name == True and display_percentage_probability == True):
draw_caption(detected_copy, detection_details, label)
elif (display_object_name == True):
draw_caption(detected_copy, detection_details, predicted_class)
elif (display_percentage_probability == True):
draw_caption(detected_copy, detection_details, str(score * 100))
each_object_details = {}
each_object_details["name"] = predicted_class
each_object_details["percentage_probability"] = score * 100
each_object_details["box_points"] = detection_details
output_objects_array.append(each_object_details)
if (extract_detected_objects == True):
splitted_copy = detected_copy2.copy()[detection_details[1]:detection_details[3],
detection_details[0]:detection_details[2]]
if (output_type == "file"):
splitted_image_path = os.path.join(objects_dir,
predicted_class + "-" + str(
counting) + ".jpg")
pltimage.imsave(splitted_image_path, splitted_copy)
detected_objects_image_array.append(splitted_image_path)
elif (output_type == "array"):
detected_objects_image_array.append(splitted_copy)
if (output_type == "file"):
pltimage.imsave(output_image_path, detected_copy)
if (extract_detected_objects == True):
if (output_type == "file"):
return output_objects_array, detected_objects_image_array
elif (output_type == "array"):
return detected_copy, output_objects_array, detected_objects_image_array
else:
if (output_type == "file"):
return output_objects_array
elif (output_type == "array"):
return detected_copy, output_objects_array
except:
raise ValueError(
"Ensure you specified correct input image, input type, output type and/or output image path ")
class VideoObjectDetection:
"""
This is the object detection class for videos and camera live stream inputs in the ImageAI library. It provides support for RetinaNet,
YOLOv3 and TinyYOLOv3 object detection networks. After instantiating this class, you can set it's properties and
make object detections using it's pre-defined functions.
The following functions are required to be called before object detection can be made
* setModelPath()
* At least of of the following and it must correspond to the model set in the setModelPath()
[setModelTypeAsRetinaNet(), setModelTypeAsYOLOv3(), setModelTinyYOLOv3()]
* loadModel() [This must be called once only before performing object detection]
Once the above functions have been called, you can call the detectObjectsFromVideo() function
or the detectCustomObjectsFromVideo() of the object detection instance object at anytime to
obtain observable objects in any video or camera live stream.
"""
def __init__(self):
self.__modelType = ""
self.modelPath = ""
self.__modelPathAdded = False
self.__modelLoaded = False
self.__model_collection = []
self.__input_image_min = 1333
self.__input_image_max = 800
self.__detection_storage = None
self.numbers_to_names = {0: 'person', 1: 'bicycle', 2: 'car', 3: 'motorcycle', 4: 'airplane', 5: 'bus',
6: 'train',
7: 'truck', 8: 'boat', 9: 'traffic light', 10: 'fire hydrant', 11: 'stop sign',
12: 'parking meter',
13: 'bench', 14: 'bird', 15: 'cat', 16: 'dog', 17: 'horse', 18: 'sheep', 19: 'cow',
20: 'elephant',
21: 'bear', 22: 'zebra', 23: 'giraffe', 24: 'backpack', 25: 'umbrella', 26: 'handbag',
27: 'tie',
28: 'suitcase', 29: 'frisbee', 30: 'skis', 31: 'snowboard', 32: 'sports ball',
33: 'kite',
34: 'baseball bat', 35: 'baseball glove', 36: 'skateboard', 37: 'surfboard',
38: 'tennis racket',
39: 'bottle', 40: 'wine glass', 41: 'cup', 42: 'fork', 43: 'knife', 44: 'spoon',
45: 'bowl',
46: 'banana', 47: 'apple', 48: 'sandwich', 49: 'orange', 50: 'broccoli', 51: 'carrot',
52: 'hot dog',
53: 'pizza', 54: 'donut', 55: 'cake', 56: 'chair', 57: 'couch', 58: 'potted plant',
59: 'bed',
60: 'dining table', 61: 'toilet', 62: 'tv', 63: 'laptop', 64: 'mouse', 65: 'remote',
66: 'keyboard',
67: 'cell phone', 68: 'microwave', 69: 'oven', 70: 'toaster', 71: 'sink',
72: 'refrigerator',
73: 'book', 74: 'clock', 75: 'vase', 76: 'scissors', 77: 'teddy bear',
78: 'hair drier',
79: 'toothbrush'}
# Unique instance variables for YOLOv3 model
self.__yolo_iou = 0.45
self.__yolo_score = 0.1
self.__yolo_anchors = np.array(
[[10., 13.], [16., 30.], [33., 23.], [30., 61.], [62., 45.], [59., 119.], [116., 90.], [156., 198.],
[373., 326.]])
self.__yolo_model_image_size = (416, 416)
self.__yolo_boxes, self.__yolo_scores, self.__yolo_classes = "", "", ""
self.sess = K.get_session()
# Unique instance variables for TinyYOLOv3.
self.__tiny_yolo_anchors = np.array(
[[10., 14.], [23., 27.], [37., 58.], [81., 82.], [135., 169.], [344., 319.]])
def setModelTypeAsRetinaNet(self):
"""
'setModelTypeAsRetinaNet()' is used to set the model type to the RetinaNet model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "retinanet"
def setModelTypeAsYOLOv3(self):
"""
'setModelTypeAsYOLOv3()' is used to set the model type to the YOLOv3 model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "yolov3"
def setModelTypeAsTinyYOLOv3(self):
"""
'setModelTypeAsTinyYOLOv3()' is used to set the model type to the TinyYOLOv3 model
for the video object detection instance instance object .
:return:
"""
self.__modelType = "tinyyolov3"
def setModelPath(self, model_path):
"""
'setModelPath()' function is required and is used to set the file path to a RetinaNet,
YOLOv3 or TinyYOLOv3 object detection model trained on the COCO dataset.
:param model_path:
:return:
"""
if (self.__modelPathAdded == False):
self.modelPath = model_path
self.__modelPathAdded = True
def loadModel(self, detection_speed="normal"):
"""
'loadModel()' function is required and is used to load the model structure into the program from the file path defined
in the setModelPath() function. This function receives an optional value which is "detection_speed".
The value is used to reduce the time it takes to detect objects in an image, down to about a 10% of the normal time, with
with just slight reduction in the number of objects detected.
* prediction_speed (optional); Acceptable values are "normal", "fast", "faster", "fastest" and "flash"