def evaluate_model(self, model_path, iou_threshold = 0.5):
self.model = MaskRCNN(mode = "inference", model_dir = os.getcwd(), config = self.config)
if os.path.isfile(model_path):
model_files = [model_path]
if os.path.isdir(model_path):
model_files = sorted([os.path.join(model_path, file_name) for file_name in os.listdir(model_path)])
for modelfile in model_files:
if str(modelfile).endswith(".h5"):
self.model.load_weights(modelfile, by_name=True)
APs = []
#outputs = list()
for image_id in self.dataset_test.image_ids:
# load image, bounding boxes and masks for the image id
image, image_meta, gt_class_id, gt_bbox, gt_mask = load_image_gt(self.dataset_test, self.config, image_id)
# convert pixel values (e.g. center)
scaled_image = mold_image(image, self.config)
# convert image into one sample
sample = np.expand_dims(scaled_image, 0)
# make prediction
yhat = self.model.detect(sample, verbose=0)
# extract results for first sample
r = yhat[0]
# calculate statistics, including AP
AP, _, _, _ = compute_ap(gt_bbox, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r['masks'])
# store
APs.append(AP)
# calculate the mean AP across all images
mAP = np.mean(APs)
print(modelfile, "evaluation using iou_threshold", iou_threshold, "is", f"{mAP:01f}", '\n')
class instance_custom_training:
def __init__(self):
self.model_dir = os.getcwd()
def modelConfig(self,network_backbone = "resnet101", num_classes = 1, class_names = ["BG"], batch_size = 1, image_max_dim = 512, image_min_dim = 512, image_resize_mode ="square", gpu_count = 1):
self.config = Config(BACKBONE = network_backbone, NUM_CLASSES = 1 + num_classes, class_names = class_names,
IMAGES_PER_GPU = batch_size, IMAGE_MAX_DIM = image_max_dim, IMAGE_MIN_DIM = image_min_dim, IMAGE_RESIZE_MODE = image_resize_mode,
GPU_COUNT = gpu_count)
if network_backbone == "resnet101":
print("Using resnet101 as network backbone For Mask R-CNN model")
else:
print("Using resnet50 as network backbone For Mask R-CNN model")
def load_pretrained_model(self, model_path):
#load the weights for COCO
self.model = modellib.MaskRCNN(mode="training", model_dir = self.model_dir, config=self.config)
self.model.load_weights(model_path, by_name=True, exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox",
"mrcnn_mask"])
def load_dataset(self, dataset):
labelme_folder1 = os.path.abspath(os.path.join(dataset, "train"))
#dir where the converted json files will be saved
save_json_path1 = os.path.abspath(os.path.join(dataset, "train.json"))
#conversion of individual labelme json files into a single json file
labelme2coco.convert(labelme_folder1, save_json_path1)
# Training dataset.
self.dataset_train = Data()
self.dataset_train.load_data(save_json_path1, labelme_folder1)
self.dataset_train.prepare()
labelme_folder2 = os.path.abspath(os.path.join(dataset, "test"))
#dir where the converted json files will be saved
save_json_path2 = os.path.abspath(os.path.join(dataset, "test.json"))
#conversion of individual labelme json files into a single json file
labelme2coco.convert(labelme_folder2, save_json_path2)
# Training dataset.
self.dataset_test = Data()
self.dataset_test.load_data(save_json_path2, labelme_folder2)
self.dataset_test.prepare()
def visualize_sample(self):
image_id = np.random.choice(self.dataset_train.image_ids)
image = self.dataset_train.load_image(image_id)
mask, class_ids = self.dataset_train.load_mask(image_id)
bbox = extract_bboxes(mask)
# Display image and instances
out = display_box_instances(image, bbox, mask, class_ids, self.dataset_train.class_names)
plt.imshow(out)
plt.axis("off")
plt.show()
def train_model(self,num_epochs,path_trained_models, layers = "all", augmentation = False):
if augmentation == False:
print('Train %d' % len(self.dataset_train.image_ids), "images")
print('Validate %d' % len(self.dataset_test.image_ids), "images")
print("No augmentation")
self.model.train(self.dataset_train, self.dataset_test,models = path_trained_models, epochs=num_epochs,layers=layers)
else:
augmentation = imgaug.augmenters.Sometimes(0.5, [
imgaug.augmenters.Fliplr(0.5),
iaa.Flipud(0.5),
imgaug.augmenters.GaussianBlur(sigma=(0.0, 5.0))
])
print('Train %d' % len(self.dataset_train.image_ids), "images")
print('Validate %d' % len(self.dataset_test.image_ids), "images")
print("Applying augmentation on dataset")
self.model.train(self.dataset_train, self.dataset_test,models = path_trained_models, augmentation = augmentation, epochs=num_epochs,layers=layers)
def evaluate_model(self, model_path, iou_threshold = 0.5):
self.model = MaskRCNN(mode = "inference", model_dir = os.getcwd(), config = self.config)
if os.path.isfile(model_path):
model_files = [model_path]
if os.path.isdir(model_path):
model_files = sorted([os.path.join(model_path, file_name) for file_name in os.listdir(model_path)])
for modelfile in model_files:
if str(modelfile).endswith(".h5"):
self.model.load_weights(modelfile, by_name=True)
APs = []
#outputs = list()
for image_id in self.dataset_test.image_ids:
# load image, bounding boxes and masks for the image id
image, image_meta, gt_class_id, gt_bbox, gt_mask = load_image_gt(self.dataset_test, self.config, image_id)
# convert pixel values (e.g. center)
scaled_image = mold_image(image, self.config)
# convert image into one sample
sample = np.expand_dims(scaled_image, 0)
# make prediction
yhat = self.model.detect(sample, verbose=0)
# extract results for first sample
r = yhat[0]
# calculate statistics, including AP
AP, _, _, _ = compute_ap(gt_bbox, gt_class_id, gt_mask, r["rois"], r["class_ids"], r["scores"], r['masks'])
# store
APs.append(AP)
# calculate the mean AP across all images
mAP = np.mean(APs)
print(modelfile, "evaluation using iou_threshold", iou_threshold, "is", f"{mAP:01f}", '\n')
def load_model(self, model_path):
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=coco_config)
self.model.load_weights(model_path, by_name=True)
def load_model(self, model_path):
#load the weights for COCO
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=self.config)
self.model.load_weights(model_path, by_name=True)
class custom_segmentation:
def __init__(self):
self.model_dir = os.getcwd()
def inferConfig(self,
name=None,
network_backbone="resnet101",
num_classes=1,
class_names=["BG"],
batch_size=1,
detection_threshold=0.7,
image_max_dim=512,
image_min_dim=512,
image_resize_mode="square",
gpu_count=1):
self.config = Config(BACKBONE=network_backbone,
NUM_CLASSES=1 + num_classes,
class_names=class_names,
IMAGES_PER_GPU=batch_size,
IMAGE_MAX_DIM=image_max_dim,
IMAGE_MIN_DIM=image_min_dim,
DETECTION_MIN_CONFIDENCE=detection_threshold,
IMAGE_RESIZE_MODE=image_resize_mode,
GPU_COUNT=gpu_count)
def load_model(self, model_path):
#load the weights for COCO
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=self.config)
self.model.load_weights(model_path, by_name=True)
def segmentImage(self,
image_path,
show_bboxes=False,
output_image_name=None,
verbose=None):
image = cv2.imread(image_path)
new_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Run detection
if verbose is not None:
print("Processing image...")
results = self.model.detect([new_img])
r = results[0]
if show_bboxes == False:
#apply segmentation mask
output = display_instances(image, r['rois'], r['masks'],
r['class_ids'], self.config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
else:
#apply segmentation mask with bounding boxes
output = display_box_instances(image, r['rois'], r['masks'],
r['class_ids'],
self.config.class_names,
r['scores'])
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed Image saved successfully in your current working directory."
)
return r, output
def segmentFrame(self,
frame,
show_bboxes=False,
output_image_name=None,
verbose=None):
new_img = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
if verbose is not None:
print("Processing frame...")
# Run detection
results = self.model.detect([new_img])
r = results[0]
if show_bboxes == False:
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'], self.config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
else:
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'], r['masks'],
r['class_ids'],
self.config.class_names,
r['scores'])
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed Image saved successfully in your current working directory."
)
return r, output
def process_video(self,
video_path,
show_bboxes=False,
output_video_name=None,
frames_per_second=None):
capture = cv2.VideoCapture(video_path)
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
if frames_per_second is not None:
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = time.time()
if show_bboxes == False:
while True:
counter += 1
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame], verbose=0)
print("No. of frames:", counter)
r = results[0]
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'],
self.config.class_names)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
capture.release()
if frames_per_second is not None:
save_video.release()
return r, output
else:
while True:
counter += 1
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame], verbose=0)
print("No. of frames:", counter)
r = results[0]
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'],
r['masks'], r['class_ids'],
self.config.class_names,
r['scores'])
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
capture.release()
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
if frames_per_second is not None:
save_video.release()
return r, output
def process_camera(self,
cam,
show_bboxes=False,
output_video_name=None,
frames_per_second=None,
show_frames=None,
frame_name=None,
verbose=None,
check_fps=False):
capture = cam
if output_video_name is not None:
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = datetime.now()
if show_bboxes == False:
while True:
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
r = results[0]
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'],
self.config.class_names)
counter += 1
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
fps = counter / timetaken
print(f"{fps} frames per seconds")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return r, output
else:
while True:
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
r = results[0]
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'],
r['masks'], r['class_ids'],
self.config.class_names,
r['scores'])
counter += 1
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
fps = counter / timetaken
print(f"{fps} frames per seconds")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return r, output
class instance_segmentation():
def __init__(self, infer_speed=None):
if infer_speed == "average":
coco_config.IMAGE_MAX_DIM = 512
coco_config.IMAGE_MIN_DIM = 512
coco_config.DETECTION_MIN_CONFIDENCE = 0.45
elif infer_speed == "fast":
coco_config.IMAGE_MAX_DIM = 384
coco_config.IMAGE_MIN_DIM = 384
coco_config.DETECTION_MIN_CONFIDENCE = 0.25
elif infer_speed == "rapid":
coco_config.IMAGE_MAX_DIM = 256
coco_config.IMAGE_MIN_DIM = 256
coco_config.DETECTION_MIN_CONFIDENCE = 0.20
self.model_dir = os.getcwd()
def load_model(self, model_path):
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=coco_config)
self.model.load_weights(model_path, by_name=True)
def segmentImage(self,
image_path,
show_bboxes=False,
output_image_name=None,
verbose=None):
image = cv2.imread(image_path)
new_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Run detection
if verbose is not None:
print("Processing image...")
results = self.model.detect([new_img])
coco_config.class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
r = results[0]
if show_bboxes == False:
#apply segmentation mask
output = display_instances(image, r['rois'], r['masks'],
r['class_ids'], coco_config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
else:
#apply segmentation mask with bounding boxes
output = display_box_instances(image, r['rois'], r['masks'],
r['class_ids'],
coco_config.class_names,
r['scores'])
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed Image saved successfully in your current working directory."
)
return r, output
def segmentFrame(self,
frame,
show_bboxes=False,
output_image_name=None,
verbose=None):
new_img = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
if verbose is not None:
print("Processing frame...")
# Run detection
results = self.model.detect([new_img])
coco_config.class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
r = results[0]
if show_bboxes == False:
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'], coco_config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
else:
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'], r['masks'],
r['class_ids'],
coco_config.class_names,
r['scores'])
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed Image saved successfully in your current working directory."
)
return r, output
def process_video(self,
video_path,
show_bboxes=False,
output_video_name=None,
frames_per_second=None):
capture = cv2.VideoCapture(video_path)
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
coco_config.class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
if frames_per_second is not None:
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = time.time()
if show_bboxes == False:
while True:
counter += 1
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
print("No. of frames:", counter)
r = results[0]
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'],
coco_config.class_names)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
capture.release()
if frames_per_second is not None:
save_video.release()
return r, output
else:
while True:
counter += 1
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
print("No. of frames:", counter)
r = results[0]
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'],
r['masks'], r['class_ids'],
coco_config.class_names,
r['scores'])
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
capture.release()
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
if frames_per_second is not None:
save_video.release()
return r, output
def process_camera(self,
cam,
show_bboxes=False,
output_video_name=None,
frames_per_second=None,
show_frames=None,
frame_name=None,
verbose=None,
check_fps=False):
capture = cam
if output_video_name is not None:
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
save_video = cv2.VideoWriter(output_video_name,
cv2.VideoWriter_fourcc(*'DIVX'),
frames_per_second, (width, height))
counter = 0
start = datetime.now()
coco_config.class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
if show_bboxes == False:
while True:
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
r = results[0]
#apply segmentation mask
output = display_instances(frame, r['rois'], r['masks'],
r['class_ids'],
coco_config.class_names)
counter += 1
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
out = counter / timetaken
print(f"{out:.3f} frames per second")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return r, output
else:
while True:
ret, frame = capture.read()
if ret:
# Run detection
results = self.model.detect([frame])
r = results[0]
#apply segmentation mask with bounding boxes
output = display_box_instances(frame, r['rois'],
r['masks'], r['class_ids'],
coco_config.class_names,
r['scores'])
counter += 1
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
fps = counter / timetaken
print(f"{fps:.3f} frames per second")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return r, output
class custom_segmentation:
def __init__(self):
self.model_dir = os.getcwd()
def inferConfig(self,
name=None,
network_backbone="resnet101",
num_classes=1,
class_names=["BG"],
batch_size=1,
detection_threshold=0.7,
image_max_dim=512,
image_min_dim=512,
image_resize_mode="square",
gpu_count=1):
self.config = Config(BACKBONE=network_backbone,
NUM_CLASSES=1 + num_classes,
class_names=class_names,
IMAGES_PER_GPU=batch_size,
IMAGE_MAX_DIM=image_max_dim,
IMAGE_MIN_DIM=image_min_dim,
DETECTION_MIN_CONFIDENCE=detection_threshold,
IMAGE_RESIZE_MODE=image_resize_mode,
GPU_COUNT=gpu_count)
def load_model(self, model_path):
#load the weights for COCO
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=self.config)
self.model.load_weights(model_path, by_name=True)
def segmentImage(self,
image_path,
show_bboxes=False,
extract_segmented_objects=False,
save_extracted_objects=False,
mask_points_values=False,
process_frame=False,
output_image_name=None,
verbose=None):
if process_frame == False:
image = cv2.imread(image_path)
else:
image = image_path
new_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Run detection
if verbose is not None:
print("Processing image...")
results = self.model.detect([new_img])
r = results[0]
if show_bboxes == False:
output = display_instances(image, r['rois'], r['masks'],
r['class_ids'], self.config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
""" Code to extract and crop out each of the objects segmented in an image """
if extract_segmented_objects == False:
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
return r, output
else:
mask = r['masks']
m = 0
for a in range(mask.shape[2]):
if process_frame == False:
img = cv2.imread(image_path)
else:
img = image_path
for b in range(img.shape[2]):
img[:, :, b] = img[:, :, b] * mask[:, :, a]
m += 1
extracted_objects = img[np.ix_(mask[:, :, a].any(1),
mask[:, :, a].any(0))]
if save_extracted_objects == True:
save_path = os.path.join("segmented_object" + "_" +
str(m) + ".jpg")
cv2.imwrite(save_path, extracted_objects)
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
extract_mask = extracted_objects
object_val = []
for a in range(extract_mask.shape[2]):
m = extract_mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
object_val.append(val)
extracted_objects = object_val
""" The mask values of each of the extracted cropped object in the image
is added to the dictionary containing an array of output values:
"""
r.update({"extracted_objects": extracted_objects})
return r, output
else:
output = display_box_instances(image, r['rois'], r['masks'],
r['class_ids'],
self.config.class_names,
r['scores'])
""" Code to extract and crop out each of the objects segmented in an image """
if extract_segmented_objects == True:
mask = r['masks']
m = 0
for a in range(mask.shape[2]):
if process_frame == False:
img = cv2.imread(image_path)
else:
img = image_path
for b in range(img.shape[2]):
img[:, :, b] = img[:, :, b] * mask[:, :, a]
m += 1
extracted_objects = img[np.ix_(mask[:, :, a].any(1),
mask[:, :, a].any(0))]
if save_extracted_objects == True:
save_path = os.path.join("segmented_object" + "_" +
str(m) + ".jpg")
cv2.imwrite(save_path, extracted_objects)
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
extract_mask = extracted_objects
object_val = []
for a in range(extract_mask.shape[2]):
m = extract_mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
object_val.append(val)
extracted_objects = object_val
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
""" The mask values of each of the extracted cropped object in the image
is added to the dictionary containing an array of output values:
"""
r.update({"extracted_objects": extracted_objects})
return r, output
else:
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
def segmentFrame(self,
frame,
show_bboxes=False,
mask_points_values=False,
output_image_name=None,
verbose=None):
segmask, output = self.segmentImage(
frame,
show_bboxes=show_bboxes,
process_frame=True,
mask_points_values=mask_points_values)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return segmask, output
def process_video(self,
video_path,
show_bboxes=False,
mask_points_values=False,
output_video_name=None,
frames_per_second=None):
capture = cv2.VideoCapture(video_path)
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
if frames_per_second is not None:
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = time.time()
while True:
counter += 1
ret, frame = capture.read()
if ret:
segmask, output = self.segmentImage(
frame,
show_bboxes=show_bboxes,
process_frame=True,
mask_points_values=mask_points_values)
print("No. of frames:", counter)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
capture.release()
if frames_per_second is not None:
save_video.release()
return segmask, output
def process_camera(self,
cam,
show_bboxes=False,
mask_points_values=False,
output_video_name=None,
frames_per_second=None,
show_frames=None,
frame_name=None,
verbose=None,
check_fps=False):
capture = cam
if output_video_name is not None:
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = datetime.now()
while True:
ret, frame = capture.read()
if ret:
segmask, output = self.segmentImage(
frame,
show_bboxes=False,
process_frame=True,
mask_points_values=mask_points_values)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
fps = counter / timetaken
print(f"{fps} frames per seconds")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return segmask, output
class instance_segmentation():
def __init__(self, infer_speed=None):
if infer_speed == "average":
coco_config.IMAGE_MAX_DIM = 512
coco_config.IMAGE_MIN_DIM = 512
coco_config.DETECTION_MIN_CONFIDENCE = 0.45
elif infer_speed == "fast":
coco_config.IMAGE_MAX_DIM = 384
coco_config.IMAGE_MIN_DIM = 384
coco_config.DETECTION_MIN_CONFIDENCE = 0.25
elif infer_speed == "rapid":
coco_config.IMAGE_MAX_DIM = 256
coco_config.IMAGE_MIN_DIM = 256
coco_config.DETECTION_MIN_CONFIDENCE = 0.20
self.model_dir = os.getcwd()
def load_model(self, model_path):
self.model = MaskRCNN(mode="inference",
model_dir=self.model_dir,
config=coco_config)
self.model.load_weights(model_path, by_name=True)
def select_target_classes(self,
BG=False,
person=False,
bicycle=False,
car=False,
motorcycle=False,
airplane=False,
bus=False,
train=False,
truck=False,
boat=False,
traffic_light=False,
fire_hydrant=False,
stop_sign=False,
parking_meter=False,
bench=False,
bird=False,
cat=False,
dog=False,
horse=False,
sheep=False,
cow=False,
elephant=False,
bear=False,
zebra=False,
giraffe=False,
backpack=False,
umbrella=False,
handbag=False,
tie=False,
suitcase=False,
frisbee=False,
skis=False,
snowboard=False,
sports_ball=False,
kite=False,
baseball_bat=False,
baseball_glove=False,
skateboard=False,
surfboard=False,
tennis_racket=False,
bottle=False,
wine_glass=False,
cup=False,
fork=False,
knife=False,
spoon=False,
bowl=False,
banana=False,
apple=False,
sandwich=False,
orange=False,
broccoli=False,
carrot=False,
hot_dog=False,
pizza=False,
donut=False,
cake=False,
chair=False,
couch=False,
potted_plant=False,
bed=False,
dining_table=False,
toilet=False,
tv=False,
laptop=False,
mouse=False,
remote=False,
keyboard=False,
cell_phone=False,
microwave=False,
oven=False,
toaster=False,
sink=False,
refrigerator=False,
book=False,
clock=False,
vase=False,
scissors=False,
teddy_bear=False,
hair_dryer=False,
toothbrush=False):
detected_classes = {}
target_class_names = [
BG, person, bicycle, car, motorcycle, airplane, bus, train, truck,
boat, traffic_light, fire_hydrant, stop_sign, parking_meter, bench,
bird, cat, dog, horse, sheep, cow, elephant, bear, zebra, giraffe,
backpack, umbrella, handbag, tie, suitcase, frisbee, skis,
snowboard, sports_ball, kite, baseball_bat, baseball_glove,
skateboard, surfboard, tennis_racket, bottle, wine_glass, cup,
fork, knife, spoon, bowl, banana, apple, sandwich, orange,
broccoli, carrot, hot_dog, pizza, donut, cake, chair, couch,
potted_plant, bed, dining_table, toilet, tv, laptop, mouse, remote,
keyboard, cell_phone, microwave, oven, toaster, sink, refrigerator,
book, clock, vase, scissors, teddy_bear, hair_dryer, toothbrush
]
class_names = [
"BG", "person", "bicycle", "car", "motorcycle", "airplane", "bus",
"train", "truck", "boat", "traffic light", "fire hydrant",
"stop sign", "parking meter", "bench", "bird", "cat", "dog",
"horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
"skis", "snowboard", "sports ball", "kite", "baseball bat",
"baseball glove", "skateboard", "surfboard", "tennis racket",
"bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl",
"banana", "apple", "sandwich", "orange", "broccoli", "carrot",
"hot dog", "pizza", "donut", "cake", "chair", "couch",
"potted plant", "bed", "dining table", "toilet", "tv", "laptop",
"mouse", "remote", "keyboard", "cell phone", "microwave", "oven",
"toaster", "sink", "refrigerator", "book", "clock", "vase",
"scissors", "teddy bear", "hair dryer", "toothbrush"
]
for target_class_name, class_name in zip(target_class_names,
class_names):
if (target_class_name == True):
detected_classes[class_name] = "valid"
else:
detected_classes[class_name] = "invalid"
return detected_classes
def segmentImage(self,
image_path,
show_bboxes=False,
process_frame=False,
segment_target_classes=None,
extract_segmented_objects=False,
save_extracted_objects=False,
mask_points_values=False,
output_image_name=None,
verbose=None):
if process_frame == False:
image = cv2.imread(image_path)
else:
image = image_path
new_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
# Run detection
if verbose is not None:
print("Processing image...")
results = self.model.detect([new_img])
coco_config.class_names = [
'BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
'scissors', 'teddy bear', 'hair drier', 'toothbrush'
]
r = results[0]
""" Code to filter out unused detections and detect specific classes """
if segment_target_classes is not None:
bboxes = r['rois']
scores = r['scores']
masks = r['masks']
class_ids = r['class_ids']
com_bboxes = []
com_masks = []
com_scores = []
com_class_ids = []
final_dict = []
for a, b in enumerate(r['class_ids']):
name = coco_config.class_names[b]
box = bboxes[a]
ma = masks[:, :, a]
score = scores[a]
c_ids = class_ids[a]
if (segment_target_classes[name] == "invalid"):
continue
com_bboxes.append(box)
com_class_ids.append(c_ids)
com_masks.append(ma)
com_scores.append(score)
final_bboxes = np.array(com_bboxes)
final_class_ids = np.array(com_class_ids)
final_masks = np.array(com_masks)
if len(final_masks != 0):
final_masks = np.stack(final_masks, axis=2)
final_scores = np.array(com_scores)
final_dict.append({
"rois": final_bboxes,
"class_ids": final_class_ids,
"scores": final_scores,
"masks": final_masks,
})
r = final_dict[0]
if show_bboxes == False:
output = display_instances(image, r['rois'], r['masks'],
r['class_ids'], coco_config.class_names)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
""" Code to extract and crop out each of the objects segmented in an image """
if extract_segmented_objects == False:
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
return r, output
else:
mask = r['masks']
m = 0
for a in range(mask.shape[2]):
img = cv2.imread(image_path)
for b in range(img.shape[2]):
img[:, :, b] = img[:, :, b] * mask[:, :, a]
m += 1
extracted_objects = img[np.ix_(mask[:, :, a].any(1),
mask[:, :, a].any(0))]
if save_extracted_objects == True:
save_path = os.path.join("segmented_object" + "_" +
str(m) + ".jpg")
cv2.imwrite(save_path, extracted_objects)
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
extract_mask = extracted_objects
object_val = []
for a in range(extract_mask.shape[2]):
m = extract_mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
object_val.append(val)
extracted_objects = object_val
""" The mask values of each of the extracted cropped object in the image
is added to the dictionary containing an array of output values:
"""
r.update({"extracted_objects": extracted_objects})
return r, output
else:
output = display_box_instances(image, r['rois'], r['masks'],
r['class_ids'],
coco_config.class_names,
r['scores'])
""" Code to extract and crop out each of the objects segmented in an image """
if extract_segmented_objects == True:
mask = r['masks']
m = 0
for a in range(mask.shape[2]):
img = cv2.imread(image_path)
for b in range(img.shape[2]):
img[:, :, b] = img[:, :, b] * mask[:, :, a]
m += 1
extracted_objects = img[np.ix_(mask[:, :, a].any(1),
mask[:, :, a].any(0))]
if save_extracted_objects == True:
save_path = os.path.join("segmented_object" + "_" +
str(m) + ".jpg")
cv2.imwrite(save_path, extracted_objects)
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
extract_mask = extracted_objects
object_val = []
for a in range(extract_mask.shape[2]):
m = extract_mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
object_val.append(val)
extracted_objects = object_val
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
""" The mask values of each of the extracted cropped object in the image
is added to the dictionary containing an array of output values:
"""
r.update({"extracted_objects": extracted_objects})
return r, output
else:
if mask_points_values == True:
mask = r['masks']
contain_val = []
for a in range(mask.shape[2]):
m = mask[:, :, a]
mask_values = Mask(m).polygons()
val = mask_values.points
contain_val.append(val)
r['masks'] = contain_val
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return r, output
def segmentFrame(self,
frame,
show_bboxes=False,
segment_target_classes=None,
mask_points_values=False,
output_image_name=None):
segmask, output = self.segmentImage(
frame,
show_bboxes=show_bboxes,
process_frame=True,
segment_target_classes=segment_target_classes,
mask_points_values=mask_points_values,
output_image_name=output_image_name)
if output_image_name is not None:
cv2.imwrite(output_image_name, output)
print(
"Processed image saved successfully in your current working directory."
)
return segmask, output
def process_video(self,
video_path,
show_bboxes=False,
segment_target_classes=None,
mask_points_values=False,
output_video_name=None,
frames_per_second=None):
capture = cv2.VideoCapture(video_path)
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
codec = cv2.VideoWriter_fourcc(*'DIVX')
if frames_per_second is not None:
save_video = cv2.VideoWriter(output_video_name, codec,
frames_per_second, (width, height))
counter = 0
start = time.time()
while True:
counter += 1
ret, frame = capture.read()
if ret:
#apply segmentation mask
segmask, output = self.segmentImage(
frame,
show_bboxes=show_bboxes,
segment_target_classes=segment_target_classes,
process_frame=True,
mask_points_values=mask_points_values)
print("No. of frames:", counter)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if output_video_name is not None:
save_video.write(output)
else:
break
end = time.time()
print(f"Processed {counter} frames in {end-start:.1f} seconds")
capture.release()
if frames_per_second is not None:
save_video.release()
return segmask, output
def process_camera(self,
cam,
show_bboxes=False,
segment_target_classes=None,
mask_points_values=False,
output_video_name=None,
frames_per_second=None,
show_frames=None,
frame_name=None,
verbose=None,
check_fps=False):
capture = cam
if output_video_name is not None:
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
save_video = cv2.VideoWriter(output_video_name,
cv2.VideoWriter_fourcc(*'DIVX'),
frames_per_second, (width, height))
counter = 0
start = datetime.now()
while True:
ret, frame = capture.read()
if ret:
segmask, output = self.segmentImage(
frame,
show_bboxes=show_bboxes,
segment_target_classes=segment_target_classes,
process_frame=True,
mask_points_values=mask_points_values)
counter += 1
output = cv2.resize(output, (width, height),
interpolation=cv2.INTER_AREA)
if show_frames == True:
if frame_name is not None:
cv2.imshow(frame_name, output)
if cv2.waitKey(25) & 0xFF == ord('q'):
break
if output_video_name is not None:
save_video.write(output)
elif counter == 30:
break
end = datetime.now()
if check_fps == True:
timetaken = (end - start).total_seconds()
out = counter / timetaken
print(f"{out:.3f} frames per second")
if verbose is not None:
print(f"Processed {counter} frames in {timetaken:.1f} seconds")
capture.release()
if output_video_name is not None:
save_video.release()
return segmask, output
