YOLO Object Detection Python
In the realm of computer vision and data science projects, object detection plays a pivotal role. This technique allows machines to identify and locate objects within digital images or video frames, enabling a wide range of applications, from autonomous driving to surveillance systems. YOLO Object Detection Python forms the foundation for many advanced computer vision tasks, making it an essential skill for data scientists and computer vision enthusiasts alike.
Bolstering the capabilities of object detection is the YOLO (You Only Look Once) algorithm. YOLO revolutionized the field by introducing a single-stage, real-time object detection approach. Unlike traditional methods that require multiple passes over an image, YOLO takes a unified approach, enabling faster and more accurate detection results.
YOLO Object Detection Python: Importance in Computer Vision
Object detection plays a crucial role in computer vision and data science projects. It enables machines to identify and locate objects within images or videos, opening up a wide range of applications. By accurately detecting objects, we can extract valuable information, make informed decisions, and develop intelligent systems.
Object detection is the foundation for various tasks such as image classification, object tracking, and semantic segmentation. It enables autonomous vehicles to recognize and avoid obstacles, allows surveillance systems to detect suspicious activities, and aids in medical imaging for diagnosis and treatment. In the realm of e-commerce, object detection helps automate product identification and inventory management. These are just a few examples of how object detection impacts numerous industries and drives advancements in technology.
YOLO Object Detection Python: Understanding the Significance
In the realm of computer vision and data science, object detection stands as a crucial component for extracting meaningful information from visual data. With YOLO Object Detection Python, we gain a comprehensive understanding of the significance of object detection and its wide-ranging applications.
By accurately identifying and localizing objects within images or videos, we can automate tasks, analyze complex scenes, and enable machines to comprehend their surroundings. Through this powerful algorithm, we unlock the potential to transform raw visual data into actionable insights, empowering us to make informed decisions and drive innovation.
YOLO Object Detection Python: Exploring the Role
Computer vision heavily relies on object detection to interpret and understand visual data. With YOLO Object Detection Python, we embark on a journey to explore the pivotal role of object detection in computer vision. By efficiently detecting objects within images or videos, we can tackle various tasks such as image classification, tracking, and segmentation. This algorithm revolutionizes the way we approach object detection by providing real-time, accurate results. Through this exploration, we uncover the transformative capabilities of YOLO Object Detection Python and its potential to revolutionize computer vision applications across industries.
YOLO Object Detection Python: Overview of YOLO Algorithm
YOLO (You Only Look Once) stands as a game-changing algorithm in the field of object detection. With YOLO Object Detection Python, we dive into an in-depth overview of this powerful algorithm. YOLO takes a unified approach, enabling real-time object detection with exceptional speed and accuracy. By dividing images into a grid and predicting bounding boxes and class probabilities, YOLO leverages contextual information to detect objects in complex scenes. Through this comprehensive overview, we gain valuable insights into the inner workings and advantages of the YOLO algorithm, opening up new possibilities in object detection for computer vision projects.
YOLO Object Detection Python: Leveraging the Advantages
When it comes to accurate object detection, YOLO Object Detection Python shines as a remarkable solution. This title explores how we can harness the advantages of the YOLO algorithm to achieve precise and efficient object detection. With real-time processing capabilities, YOLO enables us to handle complex scenes and detect multiple objects in a single pass. Additionally, it’s simplicity and end-to-end training makes it accessible and easy to implement. By leveraging the power of YOLO Object Detection Python, we unlock the potential to enhance the accuracy and effectiveness of our object detection tasks, paving the way for innovative computer vision and data science projects.
Understanding YOLO Object Detection
YOLO (You Only Look Once) takes a unique and efficient single-stage approach to object detection. It predicts bounding boxes and class probabilities directly from the input image in one pass, treating object detection as a regression problem. This eliminates the need for region proposals and subsequent refinement steps. YOLO’s single-stage approach enables fast and accurate real-time object detection, making it ideal for time-sensitive applications.
Describing YOLO’s Architecture for Speed and Accuracy
YOLO’s architecture plays a crucial role in achieving both speed and accuracy. It divides the input image into a grid and applies convolutional neural networks (CNNs) to predict bounding boxes and class probabilities for each grid cell. Non-maximum suppression is used to refine the predictions and remove redundancies. By incorporating multiple scales and leveraging powerful CNN-based feature extraction, YOLO efficiently detects objects of various sizes. Its architecture ensures fast and accurate results in object detection tasks.
Comparing YOLO with Other Object Detection Algorithms
YOLO stands out among other object detection algorithms. In comparison to two-stage approaches like R-CNN and Faster R-CNN, YOLO offers distinct advantages. YOLO’s single-stage nature eliminates the need for separate region proposal and object detection stages, resulting in faster inference times. It excels in real-time applications such as video surveillance and autonomous vehicles.
Also visit: Data Science Projects: Top 5 Projects For A Stronger Resume
While it may sacrifice a small amount of accuracy compared to some two-stage algorithms, YOLO still achieves competitive performance while providing superior speed. The choice of object detection algorithm depends on project requirements, and YOLO serves as a valuable option for fast and efficient object detection.
Implementing YOLO Object Detection in Python
To get started with YOLO object detection in Python, the first step is to set up the environment. This involves installing the required libraries, such as TensorFlow, Keras, and OpenCV, to leverage their powerful capabilities for deep learning and computer vision tasks. Once the environment is properly set up, we can proceed with implementing YOLO.
Preparing the Dataset and Annotations for Training
Before training the YOLO model, it is essential to prepare the dataset and annotations. This involves gathering a diverse collection of images that represent the objects we want to detect. The dataset should be carefully annotated with bounding boxes around the objects of interest. Proper annotation is crucial for training the model accurately and enabling it to recognize objects in real-world scenarios.
Training the YOLO Model Using Python and Pre-trained Weights
With the dataset and annotations ready, we can move on to training the YOLO model using Python. We can leverage pre-trained weights, such as those from the COCO dataset, as a starting point. By fine-tuning the pre-trained model on our specific dataset, we can teach it to detect the objects we are interested in. Training involves feeding the model with the annotated dataset, optimizing the model’s parameters, and iteratively improving its performance.
Fine-tuning the Model for Specific Object Detection Tasks
After training the YOLO model, we may need to fine-tune it for specific object detection tasks. This involves adjusting the model’s parameters and hyperparameters to optimize its performance on our desired objects and environments. Fine-tuning allows us to enhance the model’s accuracy and adapt it to varying conditions, such as different lighting, angles, or backgrounds. By fine-tuning the YOLO model, we can ensure that it performs optimally in our specific application.
In summary, implementing YOLO object detection in Python requires setting up the environment, preparing the dataset and annotations, training the model using pre-trained weights, and fine-tuning it for specific object detection tasks. By following these steps, we can leverage the power of YOLO and Python to develop accurate and efficient object detection systems.
Evaluating and Deploying the YOLO Model
Once the YOLO model is trained, it is crucial to evaluate its performance to ensure its effectiveness in object detection. This involves using evaluation metrics such as precision, recall, and mean average precision (mAP) to assess how well the model identifies and localizes objects. By analyzing these metrics, we can gain insights into the model’s accuracy, robustness, and generalization capabilities.
Testing the Model on New Images or Videos for Object Detection
To validate the YOLO model’s performance in real-world scenarios, it is essential to test it on new images or videos that were not part of the training dataset. By applying the trained model to unseen data, we can assess its ability to detect objects accurately and reliably. This testing phase helps identify any potential limitations or areas for improvement in the model’s performance.
Optimizing the Model for Real-time or Embedded Systems
To deploy the YOLO model in real-time or embedded systems, optimization is necessary. Real-time applications, such as video surveillance or autonomous vehicles, require the model to process frames quickly and efficiently. Optimization techniques, including model quantization, model compression, and hardware acceleration, can be applied to make the model more lightweight and suitable for deployment on resource-constrained devices or systems.
In summary, evaluating and deploying the YOLO model involves assessing its performance using evaluation metrics, testing it on new data, and optimizing it for real-time or embedded systems. By rigorously evaluating the model and optimizing it for deployment, we can ensure its effectiveness in various applications and domains.
Showcase of YOLO Object Detection Projects
In this section, we showcase the wide range of real-world applications where YOLO object detection has been successfully utilized. From autonomous driving and surveillance systems to medical imaging and industrial automation, YOLO has proven its versatility in various domains. We present compelling examples of how YOLO enables accurate and efficient object detection in practical scenarios, emphasizing its value in solving complex problems.
Highlighting Successful Use Cases and Their Impact
We delve into specific use cases where YOLO object detection has made a significant impact. We highlight success stories that demonstrate how YOLO has improved efficiency, safety, and decision-making processes across different industries. By examining these success cases, we gain insights into the practical benefits of YOLO and its contribution to solving real-world challenges.
Providing Additional Resources and Project Ideas for Further Exploration
To encourage further exploration, we provide additional resources and project ideas related to YOLO object detection. These resources include online tutorials, GitHub repositories, and academic papers that offer in-depth knowledge and practical guidance for implementing YOLO. Moreover, we present exciting project ideas that readers can pursue to enhance their understanding and hands-on experience with YOLO.
In conclusion, the showcase of YOLO object detection projects demonstrates its widespread application in diverse fields. By highlighting successful use cases and providing additional resources, we inspire readers to explore the potential of YOLO and engage in their own exciting projects.
YOLO Object Detection Python: Code
You can find this code and dataset on Google Colab.
1: Importing Libraries
import tensorflow as tf import numpy as np from PIL import Image, ImageDraw, ImageFont from IPython.display import display from seaborn import color_palette import cv2
2: Model hyperparameters
_BATCH_NORM_DECAY = 0.9
_BATCH_NORM_EPSILON = 1e-05
_LEAKY_RELU = 0.1
_ANCHORS = [(10, 13), (16, 30), (33, 23),
(30, 61), (62, 45), (59, 119),
(116, 90), (156, 198), (373, 326)]
_MODEL_SIZE = (416, 416)
3: Model definition
def batch_norm(inputs, training, data_format):
"""Performs a batch normalization using a standard set of parameters."""
return tf.layers.batch_normalization(
inputs=inputs, axis=1 if data_format == 'channels_first' else 3,
momentum=_BATCH_NORM_DECAY, epsilon=_BATCH_NORM_EPSILON,
scale=True, training=training)
def fixed_padding(inputs, kernel_size, data_format):
"""ResNet implementation of fixed padding.
Pads the input along the spatial dimensions independently of input size.
Args:
inputs: Tensor input to be padded.
kernel_size: The kernel to be used in the conv2d or max_pool2d.
data_format: The input format.
Returns:
A tensor with the same format as the input.
"""
pad_total = kernel_size - 1
pad_beg = pad_total // 2
pad_end = pad_total - pad_beg
if data_format == 'channels_first':
padded_inputs = tf.pad(inputs, [[0, 0], [0, 0],
[pad_beg, pad_end],
[pad_beg, pad_end]])
else:
padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg, pad_end],
[pad_beg, pad_end], [0, 0]])
return padded_inputs
def conv2d_fixed_padding(inputs, filters, kernel_size, data_format, strides=1):
"""Strided 2-D convolution with explicit padding."""
if strides > 1:
inputs = fixed_padding(inputs, kernel_size, data_format)
return tf.layers.conv2d(
inputs=inputs, filters=filters, kernel_size=kernel_size,
strides=strides, padding=('SAME' if strides == 1 else 'VALID'),
use_bias=False, data_format=data_format)
4: Feature extraction: Darknet-53
def darknet53_residual_block(inputs, filters, training, data_format,
strides=1):
"""Creates a residual block for Darknet."""
shortcut = inputs
inputs = conv2d_fixed_padding(
inputs, filters=filters, kernel_size=1, strides=strides,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(
inputs, filters=2 * filters, kernel_size=3, strides=strides,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs += shortcut
return inputs
def darknet53(inputs, training, data_format):
"""Creates Darknet53 model for feature extraction."""
inputs = conv2d_fixed_padding(inputs, filters=32, kernel_size=3,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(inputs, filters=64, kernel_size=3,
strides=2, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = darknet53_residual_block(inputs, filters=32, training=training,
data_format=data_format)
inputs = conv2d_fixed_padding(inputs, filters=128, kernel_size=3,
strides=2, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
for _ in range(2):
inputs = darknet53_residual_block(inputs, filters=64,
training=training,
data_format=data_format)
inputs = conv2d_fixed_padding(inputs, filters=256, kernel_size=3,
strides=2, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
for _ in range(8):
inputs = darknet53_residual_block(inputs, filters=128,
training=training,
data_format=data_format)
route1 = inputs
inputs = conv2d_fixed_padding(inputs, filters=512, kernel_size=3,
strides=2, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
for _ in range(8):
inputs = darknet53_residual_block(inputs, filters=256,
training=training,
data_format=data_format)
route2 = inputs
inputs = conv2d_fixed_padding(inputs, filters=1024, kernel_size=3,
strides=2, data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
for _ in range(4):
inputs = darknet53_residual_block(inputs, filters=512,
training=training,
data_format=data_format)
return route1, route2, inputs
5: Convolution layers
def yolo_convolution_block(inputs, filters, training, data_format):
"""Creates convolution operations layer used after Darknet."""
inputs = conv2d_fixed_padding(inputs, filters=filters, kernel_size=1,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(inputs, filters=2 * filters, kernel_size=3,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(inputs, filters=filters, kernel_size=1,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(inputs, filters=2 * filters, kernel_size=3,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
inputs = conv2d_fixed_padding(inputs, filters=filters, kernel_size=1,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
route = inputs
inputs = conv2d_fixed_padding(inputs, filters=2 * filters, kernel_size=3,
data_format=data_format)
inputs = batch_norm(inputs, training=training, data_format=data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
return route, inputs
6: Detection layers
def yolo_layer(inputs, n_classes, anchors, img_size, data_format):
"""Creates Yolo final detection layer.
Detects boxes with respect to anchors.
Args:
inputs: Tensor input.
n_classes: Number of labels.
anchors: A list of anchor sizes.
img_size: The input size of the model.
data_format: The input format.
Returns:
Tensor output.
"""
n_anchors = len(anchors)
inputs = tf.layers.conv2d(inputs, filters=n_anchors * (5 + n_classes),
kernel_size=1, strides=1, use_bias=True,
data_format=data_format)
shape = inputs.get_shape().as_list()
grid_shape = shape[2:4] if data_format == 'channels_first' else shape[1:3]
if data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 2, 3, 1])
inputs = tf.reshape(inputs, [-1, n_anchors * grid_shape[0] * grid_shape[1],
5 + n_classes])
strides = (img_size[0] // grid_shape[0], img_size[1] // grid_shape[1])
box_centers, box_shapes, confidence, classes = \
tf.split(inputs, [2, 2, 1, n_classes], axis=-1)
x = tf.range(grid_shape[0], dtype=tf.float32)
y = tf.range(grid_shape[1], dtype=tf.float32)
x_offset, y_offset = tf.meshgrid(x, y)
x_offset = tf.reshape(x_offset, (-1, 1))
y_offset = tf.reshape(y_offset, (-1, 1))
x_y_offset = tf.concat([x_offset, y_offset], axis=-1)
x_y_offset = tf.tile(x_y_offset, [1, n_anchors])
x_y_offset = tf.reshape(x_y_offset, [1, -1, 2])
box_centers = tf.nn.sigmoid(box_centers)
box_centers = (box_centers + x_y_offset) * strides
anchors = tf.tile(anchors, [grid_shape[0] * grid_shape[1], 1])
box_shapes = tf.exp(box_shapes) * tf.to_float(anchors)
confidence = tf.nn.sigmoid(confidence)
classes = tf.nn.sigmoid(classes)
inputs = tf.concat([box_centers, box_shapes,
confidence, classes], axis=-1)
return inputs
7: Upsample layer
def upsample(inputs, out_shape, data_format):
"""Upsamples to `out_shape` using nearest neighbor interpolation."""
if data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 2, 3, 1])
new_height = out_shape[3]
new_width = out_shape[2]
else:
new_height = out_shape[2]
new_width = out_shape[1]
inputs = tf.image.resize_nearest_neighbor(inputs, (new_height, new_width))
if data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 3, 1, 2])
return inputs
8: Non-max suppression
def build_boxes(inputs):
"""Computes top left and bottom right points of the boxes."""
center_x, center_y, width, height, confidence, classes = \
tf.split(inputs, [1, 1, 1, 1, 1, -1], axis=-1)
top_left_x = center_x - width / 2
top_left_y = center_y - height / 2
bottom_right_x = center_x + width / 2
bottom_right_y = center_y + height / 2
boxes = tf.concat([top_left_x, top_left_y,
bottom_right_x, bottom_right_y,
confidence, classes], axis=-1)
return boxes
def non_max_suppression(inputs, n_classes, max_output_size, iou_threshold,
confidence_threshold):
"""Performs non-max suppression separately for each class.
Args:
inputs: Tensor input.
n_classes: Number of classes.
max_output_size: Max number of boxes to be selected for each class.
iou_threshold: Threshold for the IOU.
confidence_threshold: Threshold for the confidence score.
Returns:
A list containing class-to-boxes dictionaries
for each sample in the batch.
"""
batch = tf.unstack(inputs)
boxes_dicts = []
for boxes in batch:
boxes = tf.boolean_mask(boxes, boxes[:, 4] > confidence_threshold)
classes = tf.argmax(boxes[:, 5:], axis=-1)
classes = tf.expand_dims(tf.to_float(classes), axis=-1)
boxes = tf.concat([boxes[:, :5], classes], axis=-1)
boxes_dict = dict()
for cls in range(n_classes):
mask = tf.equal(boxes[:, 5], cls)
mask_shape = mask.get_shape()
if mask_shape.ndims != 0:
class_boxes = tf.boolean_mask(boxes, mask)
boxes_coords, boxes_conf_scores, _ = tf.split(class_boxes,
[4, 1, -1],
axis=-1)
boxes_conf_scores = tf.reshape(boxes_conf_scores, [-1])
indices = tf.image.non_max_suppression(boxes_coords,
boxes_conf_scores,
max_output_size,
iou_threshold)
class_boxes = tf.gather(class_boxes, indices)
boxes_dict[cls] = class_boxes[:, :5]
boxes_dicts.append(boxes_dict)
return boxes_dicts
9: Final model class
class Yolo_v3:
"""Yolo v3 model class."""
def __init__(self, n_classes, model_size, max_output_size, iou_threshold,
confidence_threshold, data_format=None):
"""Creates the model.
Args:
n_classes: Number of class labels.
model_size: The input size of the model.
max_output_size: Max number of boxes to be selected for each class.
iou_threshold: Threshold for the IOU.
confidence_threshold: Threshold for the confidence score.
data_format: The input format.
Returns:
None.
"""
if not data_format:
if tf.test.is_built_with_cuda():
data_format = 'channels_first'
else:
data_format = 'channels_last'
self.n_classes = n_classes
self.model_size = model_size
self.max_output_size = max_output_size
self.iou_threshold = iou_threshold
self.confidence_threshold = confidence_threshold
self.data_format = data_format
def __call__(self, inputs, training):
"""Add operations to detect boxes for a batch of input images.
Args:
inputs: A Tensor representing a batch of input images.
training: A boolean, whether to use in training or inference mode.
Returns:
A list containing class-to-boxes dictionaries
for each sample in the batch.
"""
with tf.variable_scope('yolo_v3_model'):
if self.data_format == 'channels_first':
inputs = tf.transpose(inputs, [0, 3, 1, 2])
inputs = inputs / 255
route1, route2, inputs = darknet53(inputs, training=training,
data_format=self.data_format)
route, inputs = yolo_convolution_block(
inputs, filters=512, training=training,
data_format=self.data_format)
detect1 = yolo_layer(inputs, n_classes=self.n_classes,
anchors=_ANCHORS[6:9],
img_size=self.model_size,
data_format=self.data_format)
inputs = conv2d_fixed_padding(route, filters=256, kernel_size=1,
data_format=self.data_format)
inputs = batch_norm(inputs, training=training,
data_format=self.data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
upsample_size = route2.get_shape().as_list()
inputs = upsample(inputs, out_shape=upsample_size,
data_format=self.data_format)
axis = 1 if self.data_format == 'channels_first' else 3
inputs = tf.concat([inputs, route2], axis=axis)
route, inputs = yolo_convolution_block(
inputs, filters=256, training=training,
data_format=self.data_format)
detect2 = yolo_layer(inputs, n_classes=self.n_classes,
anchors=_ANCHORS[3:6],
img_size=self.model_size,
data_format=self.data_format)
inputs = conv2d_fixed_padding(route, filters=128, kernel_size=1,
data_format=self.data_format)
inputs = batch_norm(inputs, training=training,
data_format=self.data_format)
inputs = tf.nn.leaky_relu(inputs, alpha=_LEAKY_RELU)
upsample_size = route1.get_shape().as_list()
inputs = upsample(inputs, out_shape=upsample_size,
data_format=self.data_format)
inputs = tf.concat([inputs, route1], axis=axis)
route, inputs = yolo_convolution_block(
inputs, filters=128, training=training,
data_format=self.data_format)
detect3 = yolo_layer(inputs, n_classes=self.n_classes,
anchors=_ANCHORS[0:3],
img_size=self.model_size,
data_format=self.data_format)
inputs = tf.concat([detect1, detect2, detect3], axis=1)
inputs = build_boxes(inputs)
boxes_dicts = non_max_suppression(
inputs, n_classes=self.n_classes,
max_output_size=self.max_output_size,
iou_threshold=self.iou_threshold,
confidence_threshold=self.confidence_threshold)
return boxes_dicts
10: Utility functions
def load_images(img_names, model_size):
"""Loads images in a 4D array.
Args:
img_names: A list of images names.
model_size: The input size of the model.
data_format: A format for the array returned
('channels_first' or 'channels_last').
Returns:
A 4D NumPy array.
"""
imgs = []
for img_name in img_names:
img = Image.open(img_name)
img = img.resize(size=model_size)
img = np.array(img, dtype=np.float32)
img = np.expand_dims(img, axis=0)
imgs.append(img)
imgs = np.concatenate(imgs)
return imgs
def load_class_names(file_name):
"""Returns a list of class names read from `file_name`."""
with open(file_name, 'r') as f:
class_names = f.read().splitlines()
return class_names
def draw_boxes(img_names, boxes_dicts, class_names, model_size):
"""Draws detected boxes.
Args:
img_names: A list of input images names.
boxes_dict: A class-to-boxes dictionary.
class_names: A class names list.
model_size: The input size of the model.
Returns:
None.
"""
colors = ((np.array(color_palette("hls", 80)) * 255)).astype(np.uint8)
for num, img_name, boxes_dict in zip(range(len(img_names)), img_names,
boxes_dicts):
img = Image.open(img_name)
draw = ImageDraw.Draw(img)
font = ImageFont.truetype(font='../input/futur.ttf',
size=(img.size[0] + img.size[1]) // 100)
resize_factor = \
(img.size[0] / model_size[0], img.size[1] / model_size[1])
for cls in range(len(class_names)):
boxes = boxes_dict[cls]
if np.size(boxes) != 0:
color = colors[cls]
for box in boxes:
xy, confidence = box[:4], box[4]
xy = [xy[i] * resize_factor[i % 2] for i in range(4)]
x0, y0 = xy[0], xy[1]
thickness = (img.size[0] + img.size[1]) // 200
for t in np.linspace(0, 1, thickness):
xy[0], xy[1] = xy[0] + t, xy[1] + t
xy[2], xy[3] = xy[2] - t, xy[3] - t
draw.rectangle(xy, outline=tuple(color))
text = '{} {:.1f}%'.format(class_names[cls],
confidence * 100)
text_size = draw.textsize(text, font=font)
draw.rectangle(
[x0, y0 - text_size[1], x0 + text_size[0], y0],
fill=tuple(color))
draw.text((x0, y0 - text_size[1]), text, fill='black',
font=font)
display(img)
11: Converting weights to Tensorflow format
def load_weights(variables, file_name):
"""Reshapes and loads official pretrained Yolo weights.
Args:
variables: A list of tf.Variable to be assigned.
file_name: A name of a file containing weights.
Returns:
A list of assign operations.
"""
with open(file_name, "rb") as f:
# Skip first 5 values containing irrelevant info
np.fromfile(f, dtype=np.int32, count=5)
weights = np.fromfile(f, dtype=np.float32)
assign_ops = []
ptr = 0
# Load weights for Darknet part.
# Each convolution layer has batch normalization.
for i in range(52):
conv_var = variables[5 * i]
gamma, beta, mean, variance = variables[5 * i + 1:5 * i + 5]
batch_norm_vars = [beta, gamma, mean, variance]
for var in batch_norm_vars:
shape = var.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(shape)
ptr += num_params
assign_ops.append(tf.assign(var, var_weights))
shape = conv_var.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(
(shape[3], shape[2], shape[0], shape[1]))
var_weights = np.transpose(var_weights, (2, 3, 1, 0))
ptr += num_params
assign_ops.append(tf.assign(conv_var, var_weights))
# Loading weights for Yolo part.
# 7th, 15th and 23rd convolution layer has biases and no batch norm.
ranges = [range(0, 6), range(6, 13), range(13, 20)]
unnormalized = [6, 13, 20]
for j in range(3):
for i in ranges[j]:
current = 52 * 5 + 5 * i + j * 2
conv_var = variables[current]
gamma, beta, mean, variance = \
variables[current + 1:current + 5]
batch_norm_vars = [beta, gamma, mean, variance]
for var in batch_norm_vars:
shape = var.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(shape)
ptr += num_params
assign_ops.append(tf.assign(var, var_weights))
shape = conv_var.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(
(shape[3], shape[2], shape[0], shape[1]))
var_weights = np.transpose(var_weights, (2, 3, 1, 0))
ptr += num_params
assign_ops.append(tf.assign(conv_var, var_weights))
bias = variables[52 * 5 + unnormalized[j] * 5 + j * 2 + 1]
shape = bias.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(shape)
ptr += num_params
assign_ops.append(tf.assign(bias, var_weights))
conv_var = variables[52 * 5 + unnormalized[j] * 5 + j * 2]
shape = conv_var.shape.as_list()
num_params = np.prod(shape)
var_weights = weights[ptr:ptr + num_params].reshape(
(shape[3], shape[2], shape[0], shape[1]))
var_weights = np.transpose(var_weights, (2, 3, 1, 0))
ptr += num_params
assign_ops.append(tf.assign(conv_var, var_weights))
return assign_ops
12: Running the model
img_names = ['../input/dog.jpg', '../input/office.jpg'] for img in img_names: display(Image.open(img))
13: Detections
batch_size = len(img_names)
batch = load_images(img_names, model_size=_MODEL_SIZE)
class_names = load_class_names('../input/coco.names')
n_classes = len(class_names)
max_output_size = 10
iou_threshold = 0.5
confidence_threshold = 0.5
model = Yolo_v3(n_classes=n_classes, model_size=_MODEL_SIZE,
max_output_size=max_output_size,
iou_threshold=iou_threshold,
confidence_threshold=confidence_threshold)
inputs = tf.placeholder(tf.float32, [batch_size, 416, 416, 3])
detections = model(inputs, training=False)
model_vars = tf.global_variables(scope='yolo_v3_model')
assign_ops = load_weights(model_vars, '../input/yolov3.weights')
with tf.Session() as sess:
sess.run(assign_ops)
detection_result = sess.run(detections, feed_dict={inputs: batch})
draw_boxes(img_names, detection_result, class_names, _MODEL_SIZE)
Conclusion
In conclusion, YOLO object detection in Python offers a powerful and efficient solution for real-time, accurate object detection in a variety of applications. By leveraging the advantages of the YOLO algorithm, such as its single-stage approach and fast processing speed, data scientists and computer vision enthusiasts can unlock new possibilities in computer vision projects.
With its seamless integration into the Python ecosystem, implementing YOLO object detection becomes accessible and convenient. By following the outlined steps for setting up the environment, preparing the dataset, training the model, and evaluating its performance, users can create robust and effective object detection systems.
The versatility of YOLO is showcased through its successful application in diverse fields, ranging from autonomous driving and surveillance systems to medical imaging and industrial automation. These real-world use cases demonstrate the practical impact of YOLO in solving complex problems and improving efficiency across industries.
For those eager to delve deeper into YOLO object detection, a wealth of additional resources and project ideas are available. Online tutorials, GitHub repositories, and academic papers provide valuable insights and practical guidance for further exploration. By embracing these resources, individuals can expand their knowledge and develop innovative applications using YOLO.
In summary, YOLO object detection in Python empowers data scientists and computer vision enthusiasts to tackle complex object detection tasks with speed and accuracy. By harnessing the power of YOLO and its integration with Python, researchers, and practitioners can unlock the potential of computer vision and drive advancements in various fields. Explore the world of YOLO object detection today and discover the endless possibilities it offers for your data science projects.