def instances2dict_with_polygons(imageFileList, verbose=False):
imgCount = 0
instanceDict = {}
if not isinstance(imageFileList, list):
imageFileList = [imageFileList]
if verbose:
print("Processing {} images...".format(len(imageFileList)))
for imageFileName in imageFileList:
# Load image
img = Image.open(imageFileName)
# Image as numpy array
imgNp = np.array(img)
# Initialize label categories
instances = {}
for label in labels:
instances[label.name] = []
# Loop through all instance ids in instance image
for instanceId in np.unique(imgNp):
if instanceId < 1000:
continue
instanceObj = Instance(imgNp, instanceId)
instanceObj_dict = instanceObj.toDict()
#instances[id2label[instanceObj.labelID].name].append(instanceObj.toDict())
if id2label[instanceObj.labelID].hasInstances:
mask = (imgNp == instanceId).astype(np.uint8)
contour, hier = cv2_util.findContours(
mask.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
polygons = [c.reshape(-1).tolist() for c in contour]
instanceObj_dict['contours'] = polygons
instances[id2label[instanceObj.labelID].name].append(instanceObj_dict)
imgKey = os.path.abspath(imageFileName)
instanceDict[imgKey] = instances
imgCount += 1
if verbose:
print("\rImages Processed: {}".format(imgCount), end=' ')
sys.stdout.flush()
if verbose:
print("")
return instanceDict
def _findContours(self):
contours = []
mask = self.mask.detach().numpy()
mask = cv2.UMat(mask)
contour, hierarchy = cv2_util.findContours(mask, cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_TC89_L1)
reshaped_contour = []
for entity in contour:
assert len(entity.shape) == 3
assert entity.shape[
1] == 1, "Hierarchical contours are not allowed"
reshaped_contour.append(entity.reshape(-1).tolist())
contours.append(reshaped_contour)
return contours
def overlay_masks(self, image, predictions):
"""
Adds the instances contours for each predicted object.
Each label has a different color.
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask` and `labels`.
"""
masks = predictions.get_field("mask").numpy()
labels = predictions.get_field("labels")
colors = self.compute_colors_for_labels(labels).tolist()
for mask, color in zip(masks, colors):
thresh = mask[0, :, :, None]
contours, hierarchy = findContours(thresh, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
image = cv2.drawContours(image, contours, -1, color, 3)
composite = image
return composite
def instances2dict_with_polygons(imageFileList, verbose=False):
imgCount = 0
instanceDict = {}
if not isinstance(imageFileList, list):
imageFileList = [imageFileList]
if verbose:
print("Processing {} images...".format(len(imageFileList)))
for imageFileName in imageFileList:
# Load image
img = Image.open(imageFileName)
# Image as numpy array
imgNp = np.array(img)
# Initialize label categories
instances = {}
for label in labels:
instances[label.name] = []
# Loop through all instance ids in instance image
for instanceId in np.unique(imgNp):
if instanceId < 1000:
continue
instanceObj = Instance(imgNp, instanceId)
instanceObj_dict = instanceObj.toDict()
if id2label[instanceObj.labelID].hasInstances:
mask = (imgNp == instanceId).astype(np.uint8)
# contours = cv2.findContours(image=mask.copy(), mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_NONE)
# _, contours, _ = cv2.findContours(mask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours, hierarchy = findContours(mask.copy(), cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
segmentation = []
for contour in contours:
# Valid polygons have >= 6 coordinates (3 points)
if contour.size >= 6:
segmentation.append(contour.flatten().tolist())
instanceObj_dict['contours'] = segmentation
instances[id2label[instanceObj.labelID].name].append(
instanceObj_dict)
imgKey = os.path.abspath(imageFileName)
instanceDict[imgKey] = instances
imgCount += 1
if verbose:
print("\rImages Processed: {}".format(imgCount), end=' ')
sys.stdout.flush()
if verbose:
print("")
#print(instanceDict)
return instanceDict