def processFiles(pos_dir,
neg_dir,
recurse=False,
output_file=False,
output_filename=None,
color_space="bgr",
channels=[0, 1, 2],
hog_features=False,
hist_features=False,
spatial_features=False,
hog_lib="cv",
size=(64, 64),
hog_bins=9,
pix_per_cell=(8, 8),
cells_per_block=(2, 2),
block_stride=None,
block_norm="L1",
transform_sqrt=True,
signed_gradient=False,
hist_bins=16,
spatial_size=(16, 16)):
"""
Extract features from positive samples and negative samples.
Store feature vectors in a dict and optionally save to pickle file.
@param pos_dir (str): Path to directory containing positive samples.
@param neg_dir (str): Path to directory containing negative samples.
@param recurse (bool): Traverse directories recursively (else, top-level only).
@param output_file (bool): Save processed samples to file.
@param output_filename (str): Output file filename.
@param color_space (str): Color space conversion.
@param channels (list): Image channel indices to use.
For remaining arguments, refer to Descriptor class:
@see descriptor.Descriptor#__init__(...)
@return feature_data (dict): Lists of sample features split into training,
validation, test sets; scaler object; parameters used to
construct descriptor and process images.
NOTE: OpenCV HOGDescriptor currently only supports 1-channel and 3-channel
images, not 2-channel images.
"""
if not (hog_features or hist_features or spatial_features):
raise RuntimeError(
"No features selected (set hog_features=True, " +
"hist_features=True, and/or spatial_features=True.)")
pos_dir = os.path.abspath(pos_dir)
neg_dir = os.path.abspath(neg_dir)
if not os.path.isdir(pos_dir):
raise FileNotFoundError("Directory " + pos_dir + " does not exist.")
if not os.path.isdir(neg_dir):
raise FileNotFoundError("Directory " + neg_dir + " does not exist.")
print("Building file list...")
if recurse:
pos_files = [
os.path.join(rootdir, file)
for rootdir, _, files in os.walk(pos_dir) for file in files
]
neg_files = [
os.path.join(rootdir, file)
for rootdir, _, files in os.walk(neg_dir) for file in files
]
else:
pos_files = [
os.path.join(pos_dir, file) for file in os.listdir(pos_dir)
if os.path.isfile(os.path.join(pos_dir, file))
]
neg_files = [
os.path.join(neg_dir, file) for file in os.listdir(neg_dir)
if os.path.isfile(os.path.join(neg_dir, file))
]
print("{} positive files and {} negative files found.\n".format(
len(pos_files), len(neg_files)))
# Get color space information.
color_space = color_space.lower()
if color_space == "gray":
color_space_name = "grayscale"
cv_color_const = cv2.COLOR_BGR2GRAY
channels = [0]
elif color_space == "hls":
color_space_name = "HLS"
cv_color_const = cv2.COLOR_BGR2HLS
elif color_space == "hsv":
color_space_name = "HSV"
cv_color_const = cv2.COLOR_BGR2HSV
elif color_space == "lab":
color_space_name = "Lab"
cv_color_const = cv2.COLOR_BGR2Lab
elif color_space == "luv":
color_space_name = "Luv"
cv_color_const = cv2.COLOR_BGR2Luv
elif color_space == "ycrcb" or color_space == "ycc":
color_space_name = "YCrCb"
cv_color_const = cv2.COLOR_BGR2YCrCb
elif color_space == "yuv":
color_space_name = "YUV"
cv_color_const = cv2.COLOR_BGR2YUV
else:
color_space_name = "BGR"
cv_color_const = -1
# Get names of desired features.
features = [
feature_name for feature_name, feature_bool in
zip(["HOG", "color histogram", "spatial"],
[hog_features, hist_features, spatial_features])
if feature_bool == True
]
feature_str = features[0]
for feature_name in features[1:]:
feature_str += ", " + feature_name
# Get information about channel indices.
if len(channels) == 2 and hog_features and hog_lib == "cv":
warnings.warn("OpenCV HOG does not support 2-channel images",
RuntimeWarning)
channel_index_str = str(channels[0])
for ch_index in channels[1:]:
channel_index_str += ", {}".format(ch_index)
print("Converting images to " + color_space_name + " color space and " +
"extracting " + feature_str + " features from channel(s) " +
channel_index_str + ".\n")
# Store feature vectors for positive samples in list pos_features and
# for negative samples in neg_features.
pos_features = []
neg_features = []
start_time = time.time()
# Get feature descriptor object to call on each sample.
descriptor = Descriptor(hog_features=hog_features,
hist_features=hist_features,
spatial_features=spatial_features,
hog_lib=hog_lib,
size=size,
hog_bins=hog_bins,
pix_per_cell=pix_per_cell,
cells_per_block=cells_per_block,
block_stride=block_stride,
block_norm=block_norm,
transform_sqrt=transform_sqrt,
signed_gradient=signed_gradient,
hist_bins=hist_bins,
spatial_size=spatial_size)
# Iterate through files and extract features.
for i, filepath in enumerate(pos_files + neg_files):
image = cv2.imread(filepath)
if cv_color_const > -1:
image = cv2.cvtColor(image, cv_color_const)
if len(image.shape) > 2:
image = image[:, :, channels]
feature_vector = descriptor.getFeatureVector(image)
if i < len(pos_files):
pos_features.append(feature_vector)
else:
neg_features.append(feature_vector)
print("Features extracted from {} files in {:.1f} seconds\n".format(
len(pos_features) + len(neg_features),
time.time() - start_time))
# Store the length of the feature vector produced by the descriptor.
num_features = len(pos_features[0])
##TODO: Instantiate scaler and scale features.
scaler = StandardScaler()
scaler.fit(np.concatenate((pos_features, neg_features), axis=0))
pos_features = scaler.transform(pos_features)
neg_features = scaler.transform(neg_features)
##TODO: Randomize lists of feature vectors. Split 75/20/5 into training,
# validation, and test sets.
print(
"Shuffling samples into training, cross-validation, and test sets.\n")
random.shuffle(pos_features)
random.shuffle(neg_features)
# Use pos_train, pos_val, pos_test and neg_train, neg_val, neg_test to represent
# the Train, Validation and Test sets of Positive and Negtive sets.
pos_train, pos_val, pos_test = np.split(
pos_features,
[int(.75 * len(pos_features)),
int(.95 * len(pos_features))])
neg_train, neg_val, neg_test = np.split(
neg_features,
[int(.75 * len(neg_features)),
int(.95 * len(neg_features))])
# Store sample data and parameters in dict.
# Descriptor class object seems to produce errors when unpickling and
# has been commented out below. The descriptor will be re-instantiated
# by the Detector object later.
feature_data = {
"pos_train": pos_train,
"neg_train": neg_train,
"pos_val": pos_val,
"neg_val": neg_val,
"pos_test": pos_test,
"neg_test": neg_test,
#"descriptor": descriptor,
"scaler": scaler,
"hog_features": hog_features,
"hist_features": hist_features,
"spatial_features": spatial_features,
"color_space": color_space,
"cv_color_const": cv_color_const,
"channels": channels,
"hog_lib": hog_lib,
"size": size,
"hog_bins": hog_bins,
"pix_per_cell": pix_per_cell,
"cells_per_block": cells_per_block,
"block_stride": block_stride,
"block_norm": block_norm,
"transform_sqrt": transform_sqrt,
"signed_gradient": signed_gradient,
"hist_bins": hist_bins,
"spatial_size": spatial_size,
"num_features": num_features
}
# Pickle to file if desired.
if output_file:
if output_filename is None:
output_filename = (datetime.now().strftime("%Y%m%d%H%M") +
"_data.pkl")
pickle.dump(feature_data, open(output_filename, "wb"))
print(
"Sample and parameter data saved to {}\n".format(output_filename))
return feature_data
class Detector:
"""
Class for finding objects in a video stream. Loads and utilizes a
pretrained classifier.
"""
def __init__(self,
init_size=(64, 64),
x_overlap=0.5,
y_step=0.05,
x_range=(0, 1),
y_range=(0, 1),
scale=1.5):
"""For input arguments, @see slidingwindow.#slidingWindow(...)"""
self.init_size = init_size
self.x_overlap = x_overlap
self.y_step = y_step
self.x_range = x_range
self.y_range = y_range
self.scale = scale
self.windows = None
def loadClassifier(self, filepath=None, classifier_data=None):
"""
Load a classifier trained by the functions in train.py. Either a dict
(classifier_data) or pickled file (filepath) may be supplied.
"""
if filepath is not None:
filepath = os.path.abspath(filepath)
if not os.path.isfile(filepath):
raise FileNotFoundError("File " + filepath +
" does not exist.")
classifier_data = pickle.load(open(filepath, "rb"))
else:
classifier_data = classifier_data
if classifier_data is None:
raise ValueError("Invalid classifier data supplied.")
self.classifier = classifier_data["classifier"]
self.scaler = classifier_data["scaler"]
self.cv_color_const = classifier_data["cv_color_const"]
self.channels = classifier_data["channels"]
# Simply loading the descriptor from the dict with
# self.descriptor = classifier_data["descriptor"]
# produces an error. Thus, we instantiate a new descriptor object
# using the same parameters on which the classifier was trained.
self.descriptor = Descriptor(
hog_features=classifier_data["hog_features"],
hist_features=classifier_data["hist_features"],
spatial_features=classifier_data["spatial_features"],
hog_lib=classifier_data["hog_lib"],
size=classifier_data["size"],
hog_bins=classifier_data["hog_bins"],
pix_per_cell=classifier_data["pix_per_cell"],
cells_per_block=classifier_data["cells_per_block"],
block_stride=classifier_data["block_stride"],
block_norm=classifier_data["block_norm"],
transform_sqrt=classifier_data["transform_sqrt"],
signed_gradient=classifier_data["signed_gradient"],
hist_bins=classifier_data["hist_bins"],
spatial_size=classifier_data["spatial_size"])
return self
def classify(self, image):
"""
Classify windows of an image as "positive" (containing the desired
object) or "negative". Return a list of positively classified windows.
"""
if self.cv_color_const > -1:
image = cv2.cvtColor(image, self.cv_color_const)
if len(image.shape) > 2:
image = image[:, :, self.channels]
else:
image = image[:, :, np.newaxis]
feature_vectors = [
self.descriptor.getFeatureVector(image[y_upper:y_lower,
x_upper:x_lower, :])
for (x_upper, y_upper, x_lower, y_lower) in self.windows
]
# Scale feature vectors, predict, and return predictions.
feature_vectors = self.scaler.transform(feature_vectors)
predictions = self.classifier.predict(feature_vectors)
return [
self.windows[ind] for ind in np.argwhere(predictions == 1)[:, 0]
]
def detectVideo(self,
video_capture=None,
num_frames=9,
threshold=120,
min_bbox=None,
show_video=True,
draw_heatmap=True,
draw_heatmap_size=0.2,
write=False,
write_fps=24):
"""
Find objects in each frame of a video stream by integrating bounding
boxes over several frames to produce a heatmap of pixels with high
prediction density, ignoring pixels below a threshold, and grouping
the remaining pixels into objects. Draw boxes around detected objects.
@param video_capture (VideoCapture): cv2.VideoCapture object.
@param num_frames (int): Number of frames to sum over.
@param threshold (int): Threshold for heatmap pixel values.
@param min_bbox (int, int): Minimum (width, height) of a detection
bounding box in pixels. Boxes smaller than this will not be drawn.
Defaults to 2% of image size.
@param show_video (bool): Display the video.
@param draw_heatmap (bool): Display the heatmap in an inset in the
upper left corner of the video.
@param draw_heatmap_size (float): Size of the heatmap inset as a
fraction between (0, 1) of the image size.
@param write (bool): Write the resulting video, with detection
bounding boxes and/or heatmap, to a video file.
@param write_fps (num): Frames per second for the output video.
"""
cap = video_capture
if not cap.isOpened():
raise RuntimeError("Error opening VideoCapture.")
(grabbed, frame) = cap.read()
(h, w) = frame.shape[:2]
# Store coordinates of all windows to be checked at every frame.
self.windows = slidingWindow((w, h),
init_size=self.init_size,
x_overlap=self.x_overlap,
y_step=self.y_step,
x_range=self.x_range,
y_range=self.y_range,
scale=self.scale)
if min_bbox is None:
min_bbox = (int(0.02 * w), int(0.02 * h))
# Heatmap inset size.
inset_size = (int(draw_heatmap_size * w), int(draw_heatmap_size * h))
if write:
vidFilename = datetime.now().strftime("%Y%m%d%H%M") + ".avi"
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
writer = cv2.VideoWriter(vidFilename, fourcc, write_fps, (w, h))
# Compute the heatmap for each frame and store in current_heatmap.
# Store the last num_frames heatmaps in deque last_N_frames. At each
# frame, sum in the deque to compute summed_heatmap. After
# thresholding, label blobs in summed_heatmap with
# scipy.ndimage.measurements.label and store in heatmap_labels.
current_heatmap = np.zeros((frame.shape[:2]), dtype=np.uint8)
summed_heatmap = np.zeros_like(current_heatmap, dtype=np.uint8)
last_N_frames = deque(maxlen=num_frames)
heatmap_labels = np.zeros_like(current_heatmap, dtype=np.int)
# Weights for the frames in last_N_frames for producing summed_heatmap.
# Recent frames are weighted more heavily than older frames.
weights = np.linspace(1 / (num_frames + 1), 1, num_frames)
frame_array = []
while True:
(grabbed, frame) = cap.read()
if not grabbed:
break
current_heatmap[:] = 0
summed_heatmap[:] = 0
for (x_upper, y_upper, x_lower, y_lower) in self.classify(frame):
current_heatmap[y_upper:y_lower, x_upper:x_lower] += 10
last_N_frames.append(current_heatmap)
for i, heatmap in enumerate(last_N_frames):
cv2.add(summed_heatmap,
(weights[i] * heatmap).astype(np.uint8),
dst=summed_heatmap)
# Apply blur and/or dilate to the heatmap.
#cv2.GaussianBlur(summed_heatmap, (5,5), 0, dst=summed_heatmap)
cv2.dilate(summed_heatmap,
np.ones((7, 7), dtype=np.uint8),
dst=summed_heatmap)
if draw_heatmap:
inset = cv2.resize(summed_heatmap,
inset_size,
interpolation=cv2.INTER_AREA)
inset = cv2.cvtColor(inset, cv2.COLOR_GRAY2BGR)
frame[:inset_size[1], :inset_size[0], :] = inset
# Ignore heatmap pixels below threshold.
summed_heatmap[summed_heatmap <= threshold] = 0
# Label remaining blobs with scipy.ndimage.measurements.label.
num_objects = label(summed_heatmap, output=heatmap_labels)
# Determine the largest bounding box around each object.
for obj in range(1, num_objects + 1):
(Y_coords, X_coords) = np.nonzero(heatmap_labels == obj)
x_upper, y_upper = min(X_coords), min(Y_coords)
x_lower, y_lower = max(X_coords), max(Y_coords)
# Only draw box if object is larger than min bbox size.
if (x_lower - x_upper > min_bbox[0]
and y_lower - y_upper > min_bbox[1]):
cv2.rectangle(frame, (x_upper, y_upper),
(x_lower, y_lower), (0, 255, 0), 6)
if write:
writer.write(frame)
if show_video:
cv2.imshow("Detection", frame)
cv2.waitKey(0) #control by myself
cap.release()
if write:
writer.release()
