def upload_image() -> jsonify:
if request.method == 'POST':
file = request.files['file']
name = file.filename
IMAGES.increase_id()
image_index = IMAGES.cur_id
filename = f"{image_index}_{name}"
path = os.path.join(IMAGES.working_directory, filename)
img = Image(index=image_index, file_name=filename, path=path)
IMAGES.add_image(image=img)
file.save(path)
return jsonify({'upload': 'Upload successful'})
elif request.method == 'PUT':
return jsonify({'upload': 'PUT method is not supported'})
elif request.method == 'GET':
return jsonify({'upload': 'Uploading images to server'})
elif request.method == 'DELETE':
return jsonify({'upload': 'DELETE method is not supported'})
else:
abort(404)
def _visualize_detections(self,
image: Image,
masks,
drawMask=False) -> np.ndarray:
"""
input: the original image, the full object from the mask cnn neural network, and the object ID, if it came out to get it
output: an object indicating the objects found in the image, and the image itself, with selected objects and captions
"""
bgr_image = image.read()
font = cv2.FONT_HERSHEY_DUPLEX
fontScale = 0.8
thickness = 2
for i, currentObject in enumerate(image.objects):
if currentObject.type not in config.availableObjects:
continue
y1, x1, y2, x2 = currentObject.coordinates
lineInClassName = self.CLASS_NAMES.index(currentObject.type)
color = [
int(c) for c in np.array(self.COLORS[lineInClassName]) * 255
]
text = "{}: {:.1f}".format(currentObject.type,
currentObject.scores * 100)
if (drawMask):
mask = masks[:, :, i] # берем срез
bgr_image = mrcnn.visualize.apply_mask(
bgr_image, mask, color, alpha=0.6) # рисование маски
cv2.rectangle(bgr_image, (x1, y1), (x2, y2), color, thickness)
cv2.putText(bgr_image, text, (x1, y1 - 20), font, fontScale, color,
thickness)
return bgr_image.astype(np.uint8)
def get_one_image(self, file_id):
if self.fs.exists(file_id):
im = self.fs.get(file_id)
data = cv2.imdecode(np.fromstring(im.read(), dtype=np.uint8), 1)
return Image(data, im.location, im.timestamp, im._id, im.tagged)
return None
def get_all_untagged_images(self):
images_list = []
query = {'tagged': False}
for im in self.fs.find(query):
data = cv2.imdecode(np.fromstring(im.read(), dtype=np.uint8), 1)
images_list.append(Image(data, im.location, im.timestamp, im._id, im.tagged))
return images_list
def testInit(self) -> None:
test_class = Image(index=self.test_id_1, file_name=self.test_name_1, path=self.test_path_1)
self.assertEqual(test_class.id, self.test_id_1)
self.assertEqual(isinstance(test_class.id, int), True)
self.assertEqual(test_class.name, self.test_name_1)
self.assertEqual(isinstance(test_class.name, str), True)
self.assertEqual(test_class.path, self.test_path_1)
self.assertEqual(isinstance(test_class.path, str), True)
def pipeline(self, inputPath: str, outputPath: str = None):
"""
almost main
"""
if outputPath:
dirs.createDirs(os.path.split(outputPath)[0])
filename = os.path.split(outputPath)[1]
else:
filename = os.path.split(inputPath)[1]
cameraId = filename.split('_')[0]
img = Image(inputPath, int(cameraId), outputPath=outputPath)
binaryImage = img.read()
rowDetections = self._detectByMaskCNN(img)
detections = _parseR(self._humanizeTypes(rowDetections))
img.addDetections(detections)
signedImg = self._visualize_detections(img,
rowDetections['masks'],
drawMask=False)
img.write(outputPath, signedImg)
return img
def _detectByMaskCNN(self, image: Image):
"""
input: image - the result of cv2.imread (<filename>)
output: r - dictionary of objects found (r ['masks'], r ['rois'], r ['class_ids'], r ['scores']), detailed help somewhere else
"""
rgbImage = image.getRGBImage()
r = self.model.detect([rgbImage], verbose=1)[0] # тут вся магия
# проверить что будет если сюда подать НЕ ОДНО ИЗОБРАЖЕНИЕ, А ПОТОК
return r
def get_images(self, images_ids):
images_list = []
for file_id in images_ids:
if self.fs.exists(file_id):
im = self.fs.get(file_id)
data = cv2.imdecode(np.fromstring(im.read(), dtype=np.uint8), 1)
images_list.append(Image(data, im.location, im.timestamp, im._id, im.tagged))
else:
print("file not exist")
return images_list
def setUp(self) -> None:
self.test_path = f"{os.path.dirname(os.path.abspath(__file__))}\\Images"
self.first_test_file = f"{self.test_path}\\0_testImage1.png"
self.second_test_file = f"{self.test_path}\\1_testImage2.jpg"
self.test_file_list = []
self.test_file_list.append(self.first_test_file)
self.test_file_list.append(self.second_test_file)
self.first_test_image = Image(index=0, file_name='testImage1.png', path=self.first_test_file)
self.second_test_image = Image(index=1, file_name='testImage2.jpg', path=self.second_test_file)
self.test_image_list = []
self.test_image_list.append(self.first_test_image)
self.test_image_list.append(self.second_test_image)
self.test_json_image_list = []
self.test_json_image_list.append(self.first_test_image.__dict__)
self.test_json_image_list.append(self.second_test_image.__dict__)
def start_capture(self):
self.isStart = True
while self.isStart:
self.video_stream.start()
frame = self.video_stream.get_frame()
if frame is None:
print("invalid frame")
continue
res = FaceDetection.framesFilter(frame)
if res:
im = Image(frame, "Raanana", time.time())
file_id = self.db.save_image(im)
tz_list = FaceRecognition.find_match(frame)
for tz in tz_list:
print(tz)
suspect = self.db.get_suspect_by_tz(tz)
suspect.add_image(file_id)
self.db.update_suspect(suspect)
im.tag()
self.db.update_image_metadata(im)