def detector(img):
'''
->takes input image from the callback
->YOLOV3 detects the object in the image
->returns the coordinates of the prediction
'''
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.medianBlur(
img, 5) # remove noise from the image to reduce false detections
pred, img = detect_object(yolo, img,
save_img=False) # call the YOLOV3 detector
rospy.loginfo('Prediction bounding box coordinates below: ')
rospy.loginfo(pred)
return pred
def detect():
if request.method == 'POST':
file = request.files['file']
basepath = os.path.dirname(__file__)
file_path = os.path.join(basepath, 'uploads', file.filename)
file.save(file_path)
objects = detect_object(file_path)
if len(objects) == 0:
flash('No objects detected.','primary')
else:
flash("The predictions are : -",'primary')
for x in objects:
flash(f"{x[0].capitalize()}, Probability Score - {x[1]:.3f}",'primary')
return redirect(url_for('detect'))
else:
return render_template('object_detect.html')
"score" : FLAGS.score,
"gpu_num" : FLAGS.gpu_num,
"model_image_size" : (416, 416),
}
)
# Make a dataframe for the prediction outputs
out_df = pd.DataFrame(columns=['image', 'image_path','xmin', 'ymin', 'xmax', 'ymax', 'label','confidence','x_size','y_size'])
# labels to draw on images
class_file = open(FLAGS.classes_path, 'r')
input_labels = [line.rstrip('\n') for line in class_file.readlines()]
print('Found {} input labels: {}...'.format(len(input_labels), input_labels))
start = timer()
text_out = ''
for i, img_path in enumerate(input_image_paths):
print(img_path)
prediction, image = detect_object(yolo, img_path, save_img = save_img,
save_img_path = FLAGS.output,
postfix=FLAGS.postfix)
y_size,x_size,_ = np.array(image).shape
for single_prediction in prediction:
out_df=out_df.append(pd.DataFrame([[os.path.basename(img_path.rstrip('\n')),img_path.rstrip('\n')]+single_prediction + [x_size,y_size]],columns=['image','image_path', 'xmin', 'ymin', 'xmax', 'ymax', 'label','confidence','x_size','y_size']))
end = timer()
print('Processed {} images in {:.1f}sec - {:.1f}FPS'.format(
len(input_image_paths), end-start, len(input_image_paths)/(end-start)
))
out_df.to_csv(FLAGS.box,index=False)
len(input_image_paths),
[os.path.basename(f) for f in input_image_paths[:5]],
)
)
start = timer()
text_out = ""
# This code is for images
counter = 0
for i, img_path in enumerate(input_image_paths):
print(img_path)
prediction, image, count = detect_object(
yolo,
img_path,
counter,
save_img=save_img,
save_img_path=FLAGS.output,
postfix=FLAGS.postfix,
)
counter = count
# display an predicted image
#prediction = np.asarray(prediction)
#cv2.imshow("result", prediction)
#cv2.imshow("actual", np.array(image))
y_size, x_size, _ = np.array(image).shape
for single_prediction in prediction:
out_df = out_df.append(
pd.DataFrame(
[
[
def predict_input_dir(yolo, classes_path, no_save_img, postfix, box, input_dir,
output_dir):
"""Detect faces in input_dir.
Args:
classes_path: parsed args from commandline
no_save_img: parsed args from commandline
postfix: parsed args from commandline
box: parsed args from commandline
input_dir: input directory with images for detecting faces
output_dir: output directory for saving images
yolo: initialized yolo model for faster inferring
"""
# get images paths from input directory
input_paths = GetFileList(input_dir)
# Split images
img_endings = (".jpg", ".jpeg", ".png")
input_image_paths = []
for item in input_paths:
if item.endswith(img_endings):
input_image_paths.append(item)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
save_img = not no_save_img # whether to save detected images
# Make a dataframe for the prediction outputs
out_df = pd.DataFrame(columns=[
"image",
"image_path",
"xmin",
"ymin",
"xmax",
"ymax",
"label",
"confidence",
"x_size",
"y_size",
])
# labels to draw on check
class_file = open(classes_path, "r")
input_labels = [line.rstrip("\n") for line in class_file.readlines()]
print("Found {} input labels: {} ...".format(len(input_labels),
input_labels))
if input_image_paths:
print("Found {} input images: {} ...".format(
len(input_image_paths),
[os.path.basename(f) for f in input_image_paths[:5]],
))
start = timer()
# predict every images
for i, img_path in enumerate(input_image_paths):
print(img_path)
prediction, image = detect_object(
yolo,
img_path,
save_img=save_img,
save_img_path=output_dir,
postfix=postfix,
)
y_size, x_size, _ = np.array(image).shape
for single_prediction in prediction:
out_df = out_df.append(
pd.DataFrame(
[[
os.path.basename(img_path.rstrip("\n")),
img_path.rstrip("\n"),
] + single_prediction + [x_size, y_size]],
columns=[
"image",
"image_path",
"xmin",
"ymin",
"xmax",
"ymax",
"label",
"confidence",
"x_size",
"y_size",
],
))
end = timer()
print("Processed {} images in {:.1f}sec - {:.1f}FPS".format(
len(input_image_paths),
end - start,
len(input_image_paths) / (end - start),
))
out_df.to_csv(output_dir + '/Detection_Results.csv', index=False)
from tqdm import *
x = 1
while x < 300:
out_df = pd.DataFrame()
image = pyautogui.screenshot(region=(500, 500, 800, 450))
image.save('pic/' +
time.strftime('%Y-%m-%d-%H-%M', time.localtime(time.time())) +
'.png')
img_path = ('pic/' +
time.strftime('%Y-%m-%d-%H-%M', time.localtime(time.time())) +
'.png')
prediction, image = detect_object(
yolo,
img_path,
save_img=time.strftime('%Y-%m-%d-%H-%M', time.localtime(time.time())) +
'test.png',
save_img_path='/TrainYourOwnYOLO/Data/Source_Images/Test_Images/',
postfix="monkeys",
)
y_size, x_size, _ = np.array(image).shape
for single_prediction in prediction:
out_df = out_df.append(
pd.DataFrame(
[[
os.path.basename(img_path.rstrip("\n")),
img_path.rstrip("\n"),
] + single_prediction + [x_size, y_size]],
columns=[
"image",
"image_path",
"xmin",
if input_image_paths:
print("Found {} input images: {} ...".format(
len(input_image_paths),
[os.path.basename(f) for f in input_image_paths[:5]],
))
start = timer()
text_out = ""
# This is for images
for i, img_path in enumerate(input_image_paths):
print(img_path)
prediction, image = detect_object(
yolo,
img_path,
save_img=save_img,
save_img_path="",
postfix="",
)
y_size, x_size, _ = np.array(image).shape
for single_prediction in prediction:
out_df = out_df.append(
pd.DataFrame(
[[
os.path.basename(img_path.rstrip("\n")),
img_path.rstrip("\n"),
] + single_prediction + [x_size, y_size]],
columns=[
"image",
"image_path",
"xmin",
