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handbag_data/github_datasets/Pursearch/src/preprocessing.py
214 lines
import os
import pathlib
import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tarfile
import tensorflow as tf
import zipfile
from collections import defaultdict
from io import StringIO
from matplotlib import pyplot as plt
from PIL import Image
from IPython.display import display
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
def load_model(model_name):
"""
Load the model trained using tensorflow for object detection.
Detailed model name list can be found in
/Users/jianinglu1/Documents/GitHub/models/research/object_detection/g3doc/detection_model_zoo.md
Parameters
----------
model_name : str
the name of model you will use to do the detection
"""
base_url = 'http://download.tensorflow.org/models/object_detection/'
model_file = model_name + '.tar.gz'
model_dir = tf.keras.utils.get_file(
fname=model_name,
origin=base_url + model_file,
untar=True)
model_dir = pathlib.Path(model_dir)/"saved_model"
model = tf.saved_model.load(str(model_dir))
model = model.signatures['serving_default']
return model
def run_inference_for_single_image(model, image):
"""
Get the object detection results
Parameters
----------
model : object
model loaded to do the object detection
image : array
image array used to do the object detection
Returns
-------
output_dict: dictionary for object detection results, keys include "num_detections", "detection_classes", "detection_boxes", "detection_scores"
"""
image = np.asarray(image)
# The input needs to be a tensor, convert it using `tf.convert_to_tensor`.
input_tensor = tf.convert_to_tensor(image)
# The model expects a batch of images, so add an axis with `tf.newaxis`.
input_tensor = input_tensor[tf.newaxis,...]
# Run inference
output_dict = model(input_tensor)
# All outputs are batches tensors.
# Convert to numpy arrays, and take index [0] to remove the batch dimension.
# We're only interested in the first num_detections.
num_detections = int(output_dict.pop('num_detections'))
output_dict = {key:value[0, :num_detections].numpy()
for key,value in output_dict.items()}
output_dict['num_detections'] = num_detections
# detection_classes should be ints.
output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int64)
# Handle models with masks:
if 'detection_masks' in output_dict:
# Reframe the the bbox mask to the image size.
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
output_dict['detection_masks'], output_dict['detection_boxes'],
image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(detection_masks_reframed > 0.5,
tf.uint8)
output_dict['detection_masks_reframed'] = detection_masks_reframed.numpy()
return output_dict
def crop_image(name, path, outpath, model, show=True, save=True):
"""
Based on the object detection results to crop the image.
Here, we only focus on handbag (class: 31), only keep the handbag with highest probability
Parameters
----------
name : str
image name
path : str
directory for original images
outpath : str
directory for output cropped images
show : bool, optional
whether show the original and cropped images, by default True
save : bool, optional
whether save the cropped images, by default True
Returns
-------
True --> handbag has been founded from image
False --> no handbag
"""
image = Image.open(os.path.join(path,name))
if show:
image.show()
width, hight = image.size
image_np = np.array(image)
output_dict = run_inference_for_single_image(model, image_np)
if 31 in output_dict["detection_classes"]:
max_score = max([output_dict["detection_scores"][idx] for idx, i in enumerate(output_dict["detection_classes"]) if i == 31])
index = list(output_dict["detection_scores"]).index(max_score)
[y,x,h,w] = output_dict["detection_boxes"][index]
cropped = image_np[int(y*hight):int(h*hight), int(x*width):int(w*width)]
if show:
img = Image.fromarray(cropped,"RGB")
img.show()
if save:
img = Image.fromarray(cropped,"RGB")
img.save(os.path.join(outpath, name))
return True
else:
return False
def rename(path):
"""
Rename the image by index
Parameters
----------
path : str
directory for original images
Returns
-------
The number of images in given path
"""
olddir = os.getcwd()
os.chdir(path)
images = [ i for i in os.listdir(path) if i.split(".")[1] in ["png","gif","jpeg","jpg"] ]
for idx, i in enumerate(images):
if i.split(".")[1] in ["png","gif","jpeg"]:
im = Image.open(i)
rgb_im = im.convert('RGB')
rgb_im.save(i.split(".")[0] + ".jpg")
os.system("rm " + i )
os.system("cp " + i.split(".")[0] + ".jpg " + str(idx) + ".jpg")
os.chdir(olddir)
return len(images)
def rename_otherbag(path):
"""
Rename the image (other bags) by index
Parameters
----------
path : str
directory for original images
Returns
-------
The number of images in given path
"""
olddir = os.getcwd()
os.chdir(path)
images = [ i for i in os.listdir(path) if i.split(".")[1] in ["png","gif","jpeg","jpg"] ]
for idx, i in enumerate(images):
if " " in i:
os.system("cp " + i.split()[0] + "\ " + i.split()[1].split(".")[0] + ".jpg " + str(idx) + ".jpg")
else:
os.system("cp " + i.split(".")[0] + ".jpg " + str(idx) + ".jpg")
os.chdir(olddir)
return len(images)
if __name__ == "__main__":
# patch tf1 into `utils.ops`
utils_ops.tf = tf.compat.v1
# Patch the location of gfile
tf.gfile = tf.io.gfile
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = '/Users/jianinglu1/Documents/GitHub/models/research/object_detection/data/mscoco_label_map.pbtxt'
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
model_name = 'faster_rcnn_inception_v2_coco_2018_01_28'
detection_model = load_model(model_name)
for i in ["jetsettote","mercertotebag","selmasatchel"]:
number = rename("/Users/jianinglu1/Documents/GitHub/DataInsight/data/clean/MK/" + i)
path = "/Users/jianinglu1/Documents/GitHub/DataInsight/data/clean/MK/" + i
outpath = "/Users/jianinglu1/Documents/GitHub/DataInsight/data/processed/MK/" + i
for i in range(number):
name = str(i) + ".jpg"
try:
if not crop_image(name, path, outpath, model=detection_model, show=False, save=True):
print(i)
except:
print(i)