def detect_objects(request_filename):
"""
Chama o método para detectar objetos em uma imagem
:param request_filename: nome o arquivo da imagem
:return: imagem com seus objetos detectados
"""
detect_objects(request_filename)
def testDetectObjects(self):
generator = object_detection.read_video_file(
"testdata/test_video_1.mp4")
output = object_detection.detect_objects(generator,
threshold_score=0.5)
self.assertLen(output, 5)
class_names = [class_name for class_name, _ in output[0]]
scores = [score for _, score in output[0]]
self.assertIn("apple", class_names)
self.assertGreaterEqual(min(scores), 0.5)
class_names = [class_name for class_name, _ in output[1]]
scores = [score for _, score in output[0]]
self.assertIn("apple", class_names)
self.assertGreaterEqual(min(scores), 0.5)
class_names = [class_name for class_name, _ in output[2]]
scores = [score for _, score in output[0]]
self.assertEqual(class_names, ["cell phone"])
self.assertGreaterEqual(min(scores), 0.5)
class_names = [class_name for class_name, _ in output[3]]
scores = [score for _, score in output[0]]
self.assertEqual(class_names, ["cell phone"])
self.assertGreaterEqual(min(scores), 0.5)
class_names = [class_name for class_name, _ in output[4]]
scores = [score for _, score in output[0]]
self.assertEqual(class_names, ["dog"])
self.assertGreaterEqual(min(scores), 0.5)
def main():
args = parser.parse_args()
print("50")
if args.input_image_glob:
if args.input_video_path:
raise ValueError(
"--input_image_glob and --input_video_path are mutually exclusive")
if args.frame_rate is None:
raise ValueError(
"When --input_image_glob is provided, --frame_rate must be provided")
frame_generator = object_detection.read_images(
args.input_image_glob, frame_rate=args.frame_rate)
timestep_s = 1.0 / args.frame_rate
else:
if not args.input_video_path:
raise ValueError(
"One of --input_image_glob and --input_video_path must be provided")
frame_generator = object_detection.read_video_file(args.input_video_path)
timestep_s = 1.0 / object_detection.get_video_fps(args.input_video_path)
# TODO(cais): Support variable frame rate in video file.
events = object_detection.detect_objects(frame_generator)
tsv_rows = events_lib.convert_events_to_tsv_rows(
events,
tsv_data.VISUAL_OBJECTS_EVENTS_TIER,
timestep_s=timestep_s)
with open(args.output_tsv_path, mode="w") as f:
tsv_writer = csv.writer(f, delimiter="\t")
tsv_writer.writerow(tsv_data.COLUMN_HEADS)
for row in tsv_rows:
tsv_writer.writerow(row)
def build_serial_response(stream, received):
image = open_image_linux(stream)
cv2.imwrite(image_name, image)
if received == 'request':
mode = 1
elif received == 'request_2':
mode = 2
centers, dists, rots = detect_objects(image, color_params, min_contour_radius, homography_params, mode, gui=False, logg=True)
dists, rots = dists[0], rots[0] # Consider only first mask because we have only one target colour for now
output = ''
# Consider the contour the most on the left only
output += str(round(rots[0], 2)) + ' ' if len(rots) > 0 else ''
output += str(round(dists[0], 2)) if len(dists) > 0 else ''
if logg:
print('Detected position: {}'.format(output))
return 'rpi_response ' + output + '\n'
def display_image(self, path):
image = cv2.imread(path)
scale_X = 2.0
scale_Y = 2.0
if not self.toggle_rec_check.isChecked():
boxes = object_detection.detect_objects(
image, float(self.conf_line.text()),
float(self.threshold_line.text()))
image = cv2.resize(image, (0, 0), fx=scale_X, fy=scale_Y)
for x0, y0, x1, y1, confidence, class_num in boxes:
x0, y0, x1, y1 = int(scale_X * x0), int(scale_Y * y0), \
int(scale_X * x1), int(scale_Y * y1)
color = None
text = ''
if class_num == 0:
color = (0, 255, 0)
text = 'left'
elif class_num == 1:
color = (255, 0, 0)
text = 'right'
elif class_num == 2:
color = (0, 0, 255)
text = 'bad'
if color:
cv2.rectangle(image, (x0, y0), (x1, y1), color, 1,
cv2.LINE_AA)
cv2.putText(image, text + ' (' + str(confidence) + ')',
(x0, y0 - 2), 1, 1, color, 1, cv2.LINE_AA)
else:
image = cv2.resize(image, (0, 0), fx=scale_X, fy=scale_Y)
height, width, channel = image.shape
bytes_per_line = 3 * width
qimg = QImage(image.data, width, height, bytes_per_line,
QImage.Format_RGB888)
self.image_label.setPixmap(QPixmap.fromImage(qimg))
def upload_file():
print('here upload')
if request.method == 'POST':
file = request.files['file']
if file:
filename = secure_filename(file.filename)
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
execution_path = os.getcwd()
uploads_static_path = os.path.join(execution_path, 'static')
upload_path = os.path.join(uploads_static_path, 'uploads')
result_static_path = os.path.join(execution_path, 'static')
result_path = os.path.join(result_static_path, 'result')
out_name = os.path.join(result_path, filename)
in_name = os.path.join(upload_path, filename)
detections = detect_objects(in_name, out_name, detector)
return {'response': 'OK', 'filename': filename}
return {response: 'Error'}