def run(self):
""" Detects objects in image and returns list of objects detected.
Measures the amount of time taken to analyze an image (regardless of the accuracy of the detection)"""
perception_results = {}
# If no checkpoint specified, will assume `accurate` by default. In this case,
# we want to use our traffic checkpoint. The Detector can also take a config
# object.
detector = Detector(self.checkpoint)
for full_image_path in self.list_of_images:
if os.path.exists(full_image_path):
image_path, image_name = os.path.split(full_image_path)
image_ext = image_name.split('.')[-1]
image = read_image(full_image_path)
perception_results.update({image_name: {}})
# Returns a dictionary with the detections.
start_time = datetime.datetime.now()
objects = detector.predict(image)
end_time = datetime.datetime.now()
time_to_get_objects = end_time - start_time
# print(objects)
perception_results[image_name].update({"objects": objects})
perception_results[image_name].update({"detection_time": time_to_get_objects})
if self.to_save_result:
self.save_image_and_objects(image, image_name, image_ext, objects)
else:
print("ERROR: image not found: %s" % full_image_path)
del detector
return perception_results
def validate_perception(img):
d = Detector(checkpoint=checkpoint_name)
image = read_image(img)
start = datetime.datetime.now()
p = d.predict(image)
end = datetime.datetime.now()
print(p)
execution_time = end - start
return {execution_time: p}
def generate_landmark_csv(image_directory, filename_csv, detection, model, csv_save_path, img_save_path):
filename_labels = pd.read_csv(filename_csv, header=None)
df = pd.DataFrame()
for i, names in enumerate(filename_labels.iloc[:, 0]):
image = read_image(image_directory + names)
landmarks = predict_vertebra(image, detection, model)
df = df.append([landmarks])
save_landmarks_image(image,landmarks,img_save_path +names)
df.to_csv(csv_save_path, index=False, header=False)
def predictor_object(
checkpoint,
image,
save_image=False,
):
prediction_objects = Detector(checkpoint=checkpoint).predict(
read_image(image))
image_output_path = str(Path.cwd() / 'images_output' /
'output-{date}.png').format(date=date.today())
if save_image:
vis_objects(
read_image(image),
prediction_objects,
).save(image_output_path)
return prediction_objects
return prediction_objects
def save_image(image_directory, detection, output_path):
all_image_boxes = []
for filename in os.listdir(image_directory):
if filename.endswith('jpg'):
print(filename)
image = read_image(image_directory + filename)
pred_image, bounding_box = predict_vertebra(image, detection)
all_image_boxes.append(bounding_box)
cv2.imwrite(output_path + filename, pred_image)
all_image_boxes = np.array(all_image_boxes)
np.save('boxes.npy', all_image_boxes)
def detect(self, image_path, detector):
image = read_image(image_path)
#image = read_image('C:\\Users\\Klaymen-Island\\PycharmProjects\\TabExImg\\src\\data\\02_preprocessed_images\\eu-009\\eu-009_1.jpg')
# If no checkpoint specified, will assume `accurate` by default. In this case,
# we want to use our traffic checkpoint. The Detector can also take a config
# object.
# Returns a dictionary with the detections.
objects = detector.predict(image)
vis_objects(image, objects).save('traffic-out.png')
return objects
def predict_anchor(
image_path="/Users/balajidr/Developer/fyp_final/mainapp/functions/RCNN/testimages/slide-Table.jpg"
):
image = read_image(image_path)
# If no checkpoint specified, will assume `accurate` by default. In this case,
# we want to use our traffic checkpoint. The Detector can also take a config
# object.
# Returns a dictionary with the detections.
objects = detector.predict(image)
print(objects)
vis_objects(image, objects).save('traffic-out.png')
return objects
def test_run_in_bulk_validations_from_file(data_provider, detector):
execution_times = []
execution_names = []
for test_image in data_provider:
image = read_image(test_image.image_path)
print("Testing image: " + test_image.image_path)
start_time = datetime.datetime.now()
detected_objects = detector.predict(image)
recognition_time = (datetime.datetime.now() -
start_time).total_seconds()
execution_names.append(test_image.image_path)
execution_times.append(recognition_time)
assert_data_recognized(detected_objects, test_image)
plot_data_to_file(execution_names, execution_times)
def read_the_image(image):
image_ = read_image(image)
return image_
def run(self, save_path):
detector = Detector(self.checkpoint)
image = read_image(self.image)
objects = detector.predict(image)
vis_objects(image, objects).save(save_path)
return objects
# Python's version used: 3.6.8 64 bit
# pip install tensorflow==1.5.0
# pip install luminoth
from luminoth import Detector, read_image, vis_objects
from PIL import Image
import os
# Changing the current directory in the one of the .py file
try:
os.chdir(os.path.dirname(__file__))
except:
pass
# Reading the .jpg image
image = read_image('Pets.jpg')
# Creating the detector
detector = Detector()
# Returning a dictionary with the detections
objects = detector.predict(image)
print(objects)
# Creating a .jpg file with the detections
vis_objects(image, objects).save('Pets-out.jpg')
# Showing the image
image = Image.open('Pets-out.jpg')
image.show()
# Deleting the image
