def __init__(self, row: Dict):
self.ingredient = Ingredient(row[INGREDIENT_NAME_ALIAS],
row[INGREDIENT_ID_ALIAS])
self.menu_item_name = row[MENU_ITEM_NAME_ALIAS]
self.scan = Scan(row[MENU_ITEM_ID_ALIAS], row["image_id"], row["time"],
row["scan_id"], row["user_id"])
detections = [Detection(**d) for d in jp.decode(row["detections"])]
self.detected_ingredients = DetectedIngredient(self.scan.id,
self.ingredient.id,
detections)
def run_detection(self, scan_request: ScanRequest,
scan_id: int) -> List[DetectedIngredient]:
image = np.array(scan_request.image)
depth_map = np.array(scan_request.depth_map)
segmented_circles: List[
SegmentedCircle] = self.circle_detector.get_segmented_circles(
image)
detected_ingredients: Dict[Ingredient,
List[Detection]] = defaultdict(list)
for segmented_circle in segmented_circles:
segmented_circle.draw(image)
# radius_pixels = segmented_circle.circle.r
# mm_per_pixel = (PLATE_DIAMETER_MM / 2) / radius_pixels
# max_depth_in_circle = segmented_circle.get_max_value_in_circle(depth_map)
# print("Max depth in circle was %d" % max_depth_in_circle)
# depth_map = max_depth_in_circle - depth_map
for segment in segmented_circle.segments:
ingredient = self.ingredient_detector.label(
segment.get_segment_of(image))
if ingredient is None:
continue
mass = calculate_mass(depth_map, segment, mm_per_pixel)
print(mass)
detection: Detection = Detection(segment.x1, segment.y1, mass)
detected_ingredients[ingredient].append(detection)
results = [
DetectedIngredient(scan_id, k.id, v)
for (k, v) in detected_ingredients.items()
]
print("Found {} detected ingredients: {}".format(
len(results), [r.ingredient_id for r in results]))
return results
def get_detected_ingredients(self,
ids: List[int] = None
) -> List[DetectedIngredient]:
if ids is not None:
rows = self.get(
clause="scan_id in {}".format(self.list_to_in_param(ids)))
else:
rows = self.get()
return [DetectedIngredient(**r) for r in rows]
class MasterQueryResult:
def __init__(self, row: Dict):
self.ingredient = Ingredient(row[INGREDIENT_NAME_ALIAS], row[INGREDIENT_ID_ALIAS])
self.menu_item_name = row[MENU_ITEM_NAME_ALIAS]
self.scan = Scan(row[MENU_ITEM_ID_ALIAS], row["image_id"], row["time"], row["scan_id"], row["user_id"])
detections = [Detection(**d) for d in jp.decode(row["detections"])]
self.detected_ingredients = DetectedIngredient(self.scan.id, self.ingredient.id, detections)
def get_as_dict(self):
as_dict = dict(vars(self))
as_dict['detected_ingredients'] = self.detected_ingredients.get_as_dict()
as_dict['ingredient'] = self.ingredient.get_as_dict()
as_dict['scan'] = self.scan.get_as_dict()
return as_dict
def get_as_json(self):
return json.dumps(self.get_as_dict())
class MasterQueryResult:
def __init__(self, row: Dict):
self.ingredient = Ingredient(row[INGREDIENT_NAME_ALIAS],
row[INGREDIENT_ID_ALIAS])
self.menu_item_name = row[MENU_ITEM_NAME_ALIAS]
self.scan = Scan(row[MENU_ITEM_ID_ALIAS], row["image_id"], row["time"],
row["scan_id"], row["user_id"])
detections = [Detection(**d) for d in jp.decode(row["detections"])]
self.detected_ingredients = DetectedIngredient(self.scan.id,
self.ingredient.id,
detections)
def get_as_dict(self):
as_dict = dict(vars(self))
as_dict[
'detected_ingredients'] = self.detected_ingredients.get_as_dict()
as_dict['ingredient'] = self.ingredient.get_as_dict()
as_dict['scan'] = self.scan.get_as_dict()
return as_dict
def get_as_json(self):
return json.dumps(self.get_as_dict())
def run_detection(self, scan_request: ScanRequest, scan_id: int) -> List[DetectedIngredient]:
image = np.array(scan_request.image)
depth_map = np.array(scan_request.depth_map)
segmented_circles: List[SegmentedCircle] = self.circle_detector.get_segmented_circles(image)
if len(segmented_circles) > 1:
print("WARN: Found more than one circle")
detected_ingredients: Dict[Ingredient, List[Detection]] = defaultdict(list)
for segmented_circle in segmented_circles:
# segmented_circle.draw(image)
radius_pixels = segmented_circle.circle.r
mm_per_pixel = (PLATE_DIAMETER_MM / 2) / radius_pixels
max_depth_in_circle = segmented_circle.get_max_value_in_circle(depth_map)
if (max_depth_in_circle == -1):
print("HAd no max depth, skipping")
continue
print("Max depth in circle was %{0.2f}", max_depth_in_circle)
print("mm per pixel was %d" % mm_per_pixel)
# Mutating datastructures here
# transformed_image = self.ingredient_detector.transform_image_for_classification(image)
depth_map = max_depth_in_circle - depth_map
for segment in segmented_circle.segments:
segment_image = segment.get_segment_of(image)
transformed_seg = self.ingredient_detector.transform_image_for_classification(segment_image)
ingredient = self.ingredient_detector.label(transformed_seg)
if ingredient is None or ingredient.id in IGNORE_IDS:
continue
mass = calculate_mass(depth_map, segment, mm_per_pixel)
# print(mass)
detection: Detection = Detection(segment.x1, segment.y1, mass)
detected_ingredients[ingredient].append(detection)
results = [DetectedIngredient(scan_id, k.id, v) for (k, v) in detected_ingredients.items()]
print("Found {} detected ingredients: {}".format(len(results), [INGREDIENTS[r.ingredient_id - 1] for r in results]))
return results
class DetectedIngredientsDao(BaseDao):
def __init__(self):
super().__init__(TABLE, COLUMNS)
def get_detected_ingredients(self, ids: List[int] = None) -> List[DetectedIngredient]:
if ids is not None:
rows = self.get(clause="scan_id in {}".format(self.list_to_in_param(ids)))
else:
rows = self.get()
return [DetectedIngredient(**r) for r in rows]
def insert_detected_ingredients(self, detected_ingredients: List[DetectedIngredient]) -> int:
if len(detected_ingredients) == 0:
print("No ingredients detected, skipping")
return -1
return self.insert([d.get_as_json() for d in detected_ingredients])
if __name__ == "__main__":
did = DetectedIngredientsDao()
d1 = Detection(0, 0, 100)
d2 = Detection(40, 0, 50)
detected_ingredient = DetectedIngredient(1, 1, [d1, d2])
print(detected_ingredient.get_as_json())
did.insert_detected_ingredients([detected_ingredient])
did.insert_detected_ingredients([detected_ingredient])
got = did.get_detected_ingredients()
print(got)
super().__init__(TABLE, COLUMNS)
def get_detected_ingredients(self,
ids: List[int] = None
) -> List[DetectedIngredient]:
if ids is not None:
rows = self.get(
clause="scan_id in {}".format(self.list_to_in_param(ids)))
else:
rows = self.get()
return [DetectedIngredient(**r) for r in rows]
def insert_detected_ingredients(
self, detected_ingredients: List[DetectedIngredient]) -> int:
if len(detected_ingredients) == 0:
print("No ingredients detected, skipping")
return -1
return self.insert([d.get_as_json() for d in detected_ingredients])
if __name__ == "__main__":
did = DetectedIngredientsDao()
d1 = Detection(0, 0, 100)
d2 = Detection(40, 0, 50)
detected_ingredient = DetectedIngredient(1, 1, [d1, d2])
print(detected_ingredient.get_as_json())
did.insert_detected_ingredients([detected_ingredient])
did.insert_detected_ingredients([detected_ingredient])
got = did.get_detected_ingredients()
print(got)
def __init__(self, row: Dict):
self.ingredient = Ingredient(row[INGREDIENT_NAME_ALIAS], row[INGREDIENT_ID_ALIAS])
self.menu_item_name = row[MENU_ITEM_NAME_ALIAS]
self.scan = Scan(row[MENU_ITEM_ID_ALIAS], row["image_id"], row["time"], row["scan_id"], row["user_id"])
detections = [Detection(**d) for d in jp.decode(row["detections"])]
self.detected_ingredients = DetectedIngredient(self.scan.id, self.ingredient.id, detections)