def detect_fall(YoloV3,
img,
input_size=416,
CLASSES=YOLO_COCO_CLASSES,
score_threshold=0.3,
iou_threshold=0.45,
rectangle_colors=''):
try:
original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
except:
raise ('Invalid image!')
image_data = image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = tf.expand_dims(image_data, 0)
t1 = time.time()
pred_bbox = YoloV3.predict(image_data)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_image, input_size,
score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
ms = (t2 - t1) * 1000
fps = 1000 / ms
print('Time: {:.2f}ms, {:.1f} FPS'.format(ms, fps))
fall_bboxes = []
for i, bbox in enumerate(bboxes):
coor = np.array(bbox[:4], dtype=np.int32)
class_ind = int(bbox[5])
(x1, y1), (x2, y2) = (coor[0], coor[1]), (coor[2], coor[3])
if check_fall(CLASSES, class_ind, x2 - x1, y2 - y1):
fall_bboxes.append(bbox)
if len(fall_bboxes) > 0:
image = draw_bbox(original_image,
fall_bboxes,
rectangle_colors=rectangle_colors)
cv2.imwrite('fall-detection.jpg', image)
return True
else:
return False
def Object_tracking(Yolo, video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors='', Track_only = []):
# Definition of the parameters
max_cosine_distance = 0.7
nn_budget = None
#initialize deep sort object
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
times, times_2 = [], []
if video_path:
vid = cv2.VideoCapture(video_path) # detect on video
else:
vid = cv2.VideoCapture(0) # detect from webcam
# by default VideoCapture returns float instead of int
length = int(vid.get(cv2.CAP_PROP_FRAME_COUNT))
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
print("VIDEO PROPERTIES:FrameCount:{}\tWidth:{}\tHeight:{}\tFps:{}\t".format(length,width,height,fps))
codec = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
#1.BACKGROUND DETECTION
backSub = cv2.createBackgroundSubtractorMOG2(history = 400, varThreshold = 16, detectShadows = False)
bgMask=None
frame_no=0
while True:
_, frame = vid.read()
frame_no=frame_no+1
try:
original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
except:
break
#1.1 BACKGROUND Update
fgMask = backSub.apply(original_frame)
bgMask = backSub.getBackgroundImage()
if frame_no % 100==0:
print(frame_no)
image_data = image_preprocess(np.copy(original_frame), [input_size, input_size])
#image_data = tf.expand_dims(image_data, 0)
image_data = image_data[np.newaxis, ...].astype(np.float32)
t1 = time.time()
if YOLO_FRAMEWORK == "tf":
pred_bbox = Yolo.predict(image_data)
elif YOLO_FRAMEWORK == "trt":
batched_input = tf.constant(image_data)
result = Yolo(batched_input)
pred_bbox = []
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
#t1 = time.time()
#pred_bbox = Yolo.predict(image_data)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_frame, input_size, score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(Track_only) !=0 and NUM_CLASS[int(bbox[5])] in Track_only or len(Track_only) == 0:
boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int)-bbox[0].astype(int), bbox[3].astype(int)-bbox[1].astype(int)])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores)
features = np.array(encoder(original_frame, boxes))
detections = [Detection(bbox, score, class_name, feature) for bbox, score, class_name, feature in zip(boxes, scores, names, features)]
# Pass detections to the deepsort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class() #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(class_name)] # Get predicted object index by object name
tracked_bboxes.append(bbox.tolist() + [tracking_id, index]) # Structure data, that we could use it with our draw_bbox function
#Save to File
box_item=bbox.tolist() + [tracking_id, index,frame_no]
ts.save(box_item)
# draw detection on frame
image = draw_bbox(original_frame, tracked_bboxes, CLASSES=CLASSES, tracking=True)
t3 = time.time()
times.append(t2-t1)
times_2.append(t3-t1)
times = times[-20:]
times_2 = times_2[-20:]
ms = sum(times)/len(times)*1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2)/len(times_2)*1000)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# draw original yolo detection
#image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
if output_path != '': out.write(image)
if show:
cv2.imshow('output', image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
cv2.imwrite(ts.out_bg_img,bgMask)
cv2.destroyAllWindows()
def Object_tracking(Yolo, video_path, output_path, input_size=416, show=False, CLASSES=YOLO_COCO_CLASSES, score_threshold=0.3, iou_threshold=0.45, rectangle_colors='', Track_only = [], custom_yolo=None, custom_classes=YOLO_CUSTOM_CLASSES, Custom_track_only=[]):
# Definition of the parameters
max_cosine_distance = 0.7
nn_budget = None
#initialize deep sort object
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine", max_cosine_distance, nn_budget)
tracker = Tracker(metric)
times, times_2 = [], []
if video_path:
vid = cv2.VideoCapture(video_path) # detect on video
else:
vid = cv2.VideoCapture(0) # detect from webcam
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(output_path, codec, fps, (width, height)) # output_path must be .mp4
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
# set a bunch of flags and variables for made baskets and possessions
possession = None
possession_list = []
combined_possession_avg = 0.5
total_basket_count=0
basket_frame_list = []
baskets_dict = {"Dark": 0, "Light": 0}
made_basket_first_frame = 0
made_basket_frames = 0
basket_marked = False
if custom_yolo:
NUM_CUSTOM_CLASS = read_class_names(custom_classes)
custom_key_list = list(NUM_CUSTOM_CLASS.keys())
custom_val_list = list(NUM_CUSTOM_CLASS.values())
frame_counter = 0
# loop through each frame in video
while True:
_, frame = vid.read()
try:
first_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
original_frame = cv2.cvtColor(first_frame, cv2.COLOR_BGR2RGB)
frame_counter += 1
except:
break
image_data = image_preprocess(np.copy(first_frame), [input_size, input_size])
#image_data = tf.expand_dims(image_data, 0)
image_data = image_data[np.newaxis, ...].astype(np.float32)
t1 = time.time()
# CUSTOM BLOCK FOR BASKETBALL
if custom_yolo:
if YOLO_FRAMEWORK == "tf":
# use yolo model to make prediction on the image data
custom_pred_bbox = custom_yolo.predict(image_data)
# reshape our data to be in correct form for processing
custom_pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in custom_pred_bbox]
custom_pred_bbox = tf.concat(custom_pred_bbox, axis=0)
# get boxes based on threshhold
custom_bboxes = postprocess_boxes(custom_pred_bbox, original_frame, input_size, 0.3)
# custom_bboxes = nms(custom_bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
custom_boxes, custom_scores, custom_names = [], [], []
for bbox in custom_bboxes:
if len(Custom_track_only) !=0 and NUM_CUSTOM_CLASS[int(bbox[5])] in Custom_track_only or len(Custom_track_only) == 0:
custom_boxes.append([bbox[0].astype(int), bbox[1].astype(int), bbox[2].astype(int)-bbox[0].astype(int), bbox[3].astype(int)-bbox[1].astype(int)])
custom_scores.append(bbox[4])
custom_names.append(NUM_CUSTOM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
custom_boxes = np.array(custom_boxes)
custom_names = np.array(custom_names)
custom_scores = np.array(custom_scores)
# take note of the highest "scoring" made basket and basketball obj in each frame
highest_scoring_basketball = 0
basketball_box = None
basketball_center = None
highest_scoring_made_basket = 0
made_basket_box = None
for i, bbox in enumerate(custom_bboxes):
# loop through each bounding box to get the "best one" of the frame
# we do this because sometimes our model will detect two, and we know there can only be one
name = custom_names[i]
score = round(custom_scores[i], 3)
if name == 'basketball':
if score > highest_scoring_basketball:
highest_scoring_basketball = score
basketball_box = bbox
if name == 'made-basket':
if score > .85 and score > highest_scoring_made_basket:
highest_scoring_made_basket = score
made_basket_box = bbox
# if it sees a basketball, put a box on it and note the center (for possession)
if basketball_box is not None:
cv2.rectangle(original_frame, (int(basketball_box[0]), int(basketball_box[1])), (int(basketball_box[2]), int(basketball_box[3])), (0,0,255), 1)
cv2.rectangle(original_frame, (int(basketball_box[0]), int(basketball_box[1]-30)), (int(basketball_box[0])+(10)*17, int(basketball_box[1])), (0,0,255), -1)
cv2.putText(original_frame, "basketball" + "-" + str(highest_scoring_basketball),(int(basketball_box[0]), int(basketball_box[1]-10)),0, 0.5, (255,255,255),1)
basketball_center = ( (basketball_box[2]+basketball_box[0])/2, (basketball_box[3]+basketball_box[1])/2 )
if made_basket_box is not None:
# if theres a made basket put the box on it
cv2.rectangle(original_frame, (int(made_basket_box[0]), int(made_basket_box[1])), (int(made_basket_box[2]), int(made_basket_box[3])), (0,255,0), 1)
cv2.rectangle(original_frame, (int(made_basket_box[0]), int(made_basket_box[1]-30)), (int(made_basket_box[0])+(15)*17, int(made_basket_box[1])), (0,255,0), -1)
cv2.putText(original_frame, "made-basket" + " " + str(highest_scoring_made_basket),(int(made_basket_box[0]), int(made_basket_box[1]-10)),0, 0.6, (0,0,0),1)
if made_basket_frames == 0:
# if this is the first frame in the sequence
made_basket_first_frame = frame_counter
# increment a counter for made basket frames
made_basket_frames += 1
# if there were 3 consecuative frames AND we havnt marked the basket yet then lets count it!
if made_basket_frames >= 3 and not basket_marked:
basket_marked = True
basket_frame_list.append(made_basket_first_frame)
if possession:
# record which "team" scored the basket
baskets_dict[possession] += 1
# if no made basket make sure the made basket counter is at zero
else:
# no made basket
made_basket_frames = 0
# 60 frames after a made basket we can reset the "marked basket" flag to False
# in essence this means we start looking for made baskets again
if basket_marked and frame_counter > basket_frame_list[-1] + 60:
basket_marked = False
# END CUSTOM BLOCK
# PRESON PREDICTION and TRACKING BLOCK
if YOLO_FRAMEWORK == "tf":
pred_bbox = Yolo.predict(image_data)
elif YOLO_FRAMEWORK == "trt":
batched_input = tf.constant(image_data)
result = Yolo(batched_input)
pred_bbox = []
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_frame, input_size, score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(Track_only) !=0 and NUM_CLASS[int(bbox[5])] in Track_only or len(Track_only) == 0:
w = bbox[2].astype(int)-bbox[0].astype(int)
h = bbox[3].astype(int)-bbox[1].astype(int)
if h < height/3 and w < width/4:
if h > 120:
boxes.append([bbox[0].astype(int), bbox[1].astype(int), w, h])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores)
# detect jersey color using the tracked persons bounding box
patches = [gdet.extract_image_patch(frame, box, [box[3], box[2]]) for box in boxes]
color_ratios = [find_color(patch) for patch in patches]
features = np.array(encoder(original_frame, boxes))
# mark the detection
detections = [Detection(bbox, score, class_name, feature, color_ratio) for bbox, score, class_name, feature, color_ratio in zip(boxes, scores, names, features, color_ratios)]
# Pass detections to the deepsort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
color_ratio_list = []
check_possession = False
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
color_ratio = track.get_color_ratio()
color_ratio_list.append(color_ratio)
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class() #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(class_name)] # Get predicted object index by object name
tracked_bboxes.append(bbox.tolist() + [tracking_id, index]) # Structure data, that we could use it with our draw_bbox function
# if there is a basketball in the frame, and its "in" a ersons bounding box, check what box it is in for psosession
if basketball_center:
if basketball_center[0] >= bbox[0] and basketball_center[0] <= bbox[2]:
if basketball_center[1] >= bbox[1] and basketball_center[1] <= bbox[3]:
check_possession = True
if color_ratio <= .2:
# light team
possession_list.append(0)
else:
# dark team
possession_list.append(1)
else:
# no basketball in frame
# possession_list.append(-1)
# test_list.pop(0)
pass
# if the ball is in a bounding box, update out possession tracker
if check_possession:
if len(possession_list) > 60:
# this function takes an average of the last 60 posessions marked to determine current position
# it weights the most recent detections more
# this algo is a WIP
possession_list = possession_list[-60:]
# full_avg = sum(possession_list)/len(possession)
last_60_avg = sum(possession_list[-60:])/60
last_30_avg = sum(possession_list[-30:])/30
last_15_avg = sum(possession_list[-15:])/15
last_5_avg = sum(possession_list[-5:])/5
combined_possession_avg = round((last_60_avg + last_30_avg + last_15_avg + last_5_avg)/4,3)
#most_common_possession = stats.mode(possession_list)[0]
else:
combined_possession_avg = round(sum(possession_list)/len(possession_list),3)
# use our possession average to determine who has the ball right now
if combined_possession_avg < 0.5:
possession = "Light"
elif combined_possession_avg > 0.5:
possession = "Dark"
# draw detection on frame
image = draw_bbox(original_frame, tracked_bboxes, color_ratios=color_ratio_list, CLASSES=CLASSES, tracking=True)
t3 = time.time()
times.append(t2-t1)
times_2.append(t3-t1)
times = times[-20:]
times_2 = times_2[-20:]
ms = sum(times)/len(times)*1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2)/len(times_2)*1000)
if possession == "Light":
image = cv2.putText(image, "Posession: {}".format(possession), (width-400, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (50, 255, 255), 2)
else:
image = cv2.putText(image, "Posession: {}".format(possession), (width-400, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# image = cv2.putText(image, "Light: {} Dark: {} None: {}".format(possession_list.count(0), possession_list.count(1), possession_list.count(-1)), (400, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
image = cv2.putText(image, "Posession Avg: {}".format(combined_possession_avg), (400, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# draw original yolo detection
#image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
if output_path != '': out.write(image)
if show:
cv2.imshow('output', image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()
return_data = {"baskets_dict": baskets_dict, "basket_frame_list": basket_frame_list}
print("video saved to {}".format(output_path))
return(return_data)
def Object_tracking(YoloV3,
video_path,
output_path,
input_size=416,
show=False,
CLASSES=YOLO_COCO_CLASSES,
score_threshold=0.3,
iou_threshold=0.45,
rectangle_colors='',
Track_only=[]):
# Definition of the parameters
max_cosine_distance = 0.7
nn_budget = None
#initialize deep sort object
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
times = []
if video_path:
vid = cv2.VideoCapture(video_path) # detect on video
else:
vid = cv2.VideoCapture(0) # detect from webcam
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(output_path, codec, fps,
(width, height)) # output_path must be .mp4
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
while True:
_, img = vid.read()
try:
original_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)
except:
break
image_data = image_preprocess(np.copy(original_image),
[input_size, input_size])
image_data = tf.expand_dims(image_data, 0)
t1 = time.time()
pred_bbox = YoloV3.predict(image_data)
t2 = time.time()
times.append(t2 - t1)
times = times[-20:]
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_image, input_size,
score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(Track_only) != 0 and NUM_CLASS[int(
bbox[5])] in Track_only or len(Track_only) == 0:
boxes.append([
bbox[0].astype(int), bbox[1].astype(int),
bbox[2].astype(int) - bbox[0].astype(int),
bbox[3].astype(int) - bbox[1].astype(int)
])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores)
features = np.array(encoder(original_image, boxes))
detections = [
Detection(bbox, score, class_name, feature)
for bbox, score, class_name, feature in zip(
boxes, scores, names, features)
]
# Pass detections to the deepsort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class(
) #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(
class_name)] # Get predicted object index by object name
tracked_bboxes.append(
bbox.tolist() + [tracking_id, index]
) # Structure data, that we could use it with our draw_bbox function
ms = sum(times) / len(times) * 1000
fps = 1000 / ms
# draw detection on frame
image = draw_bbox(original_image,
tracked_bboxes,
CLASSES=CLASSES,
tracking=True)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# draw original yolo detection
#image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
#print("Time: {:.2f}ms, {:.1f} FPS".format(ms, fps))
if output_path != '': out.write(image)
if show:
cv2.imshow('output', image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()
print(f'found box : {len(pred_bbox)}')
# %%
bboxes = postprocess_boxes(pred_bbox,
original_image,
input_size,
score_threshold=0.3)
print(f'after post-process found box : {len(bboxes)}')
bboxes = nms(bboxes, iou_threshold=0.45, method='nms')
print(f'after nms found box : {len(bboxes)}')
# bboxes
# %%
image = draw_bbox(original_image,
bboxes,
CLASSES='../' + YOLO_COCO_CLASSES,
rectangle_colors='')
# %%
display(Image.fromarray(image))
# plt.figure()
# plt.imshow(image)
# plt.colorbar()
# plt.grid(False)
# plt.show()
continue
bbox = track.to_tlbr(
) # Get the corrected/predicted bounding box #`(min x, miny, max x,max y)`.
class_name = track.get_class(
) #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(
class_name)] # Get predicted object index by object name
tracked_bboxes.append(
bbox.tolist() + [tracking_id, index]
) # Structure data, that we could use it with our draw_bbox function
# draw detection on frame
image = draw_bbox(image,
tracked_bboxes,
CLASSES=CLASSES,
tracking=True)
#indices = cv2.dnn.NMSBoxes(boxes_l, confidences_l, 0.1, 0.1)
centroids = []
coordinates = []
if tracked_bboxes != []:
for box in tracked_bboxes:
coords = calc_coord_trackbox(box)
#print(coords)
centr = calculate_centr(coords)
centroids.append(centr)
coordinates.append(coords)
#if class_ids[i]==0:
def track_object(Yolo,
video_path,
vid_output_path,
text_output_path,
input_size=416,
show=False,
CLASSES=YOLO_COCO_CLASSES,
score_threshold=0.3,
iou_threshold=0.45,
rectangle_colors='',
tracking=True,
track_only=[],
tracker_max_age=30,
passenger_det=False,
face_score_threshold=0.3,
color="bincount"):
"""
Do detection on video
:param Yolo: <model_obj> YOLO model for vehicle detection
:param video_path: <str> Path to video file. Leave empty to use camera
:param vid_output_path: <str> Path to save processed video. Leave empty to not save
:param input_size: <int> YOLO model input size
:param show: <bool> True if you want to see processing live
:param CLASSES: <obj> YOLO model classed. By default they are taken from the config file
:param score_threshold: <float> minimum confidence for vehicle detection
:param iou_threshold: <float> minimum bounding box overlap for them to be counted as same object
:param rectangle_colors: bounding box colors. Currently does nothing
:param tracking: whether to use vehicle tracking
:param track_only: <list> List of objects to track if detector detects more
:param tracker_max_age: <int> number of missed before track is deleted
:param face_det: <bool> whether to initialize face detection
:param face_score_threshold: <float> minimum confidence for face detection
:param color: <str> Color detection method to use. None if neither one
:return:
"""
if not Yolo:
Yolo = load_yolo_model()
if passenger_det:
passenger_det = FaceDetector()
else:
passenger_det = None
if text_output_path:
write_csv([[
"x1", "y1", "x2", "y2", "id", "class", "probability",
"color" if color else None, "passengers" if passenger_det else None
]], text_output_path)
# Definition of the deep sort parameters
max_cosine_distance = 0.7
nn_budget = None
# initialize deep sort object
model_filename = 'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric, max_age=tracker_max_age)
times, times_2 = [], []
if video_path:
vid = cv2.VideoCapture(video_path) # detect on video
else:
vid = cv2.VideoCapture(0) # detect from webcam
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*'XVID')
out = cv2.VideoWriter(vid_output_path, codec, fps,
(width, height)) # vid_output_path must be .mp4
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
while True:
_, frame = vid.read()
try:
original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
except:
break
image_data = image_preprocess(np.copy(original_frame),
[input_size, input_size])
# image_data = tf.expand_dims(image_data, 0)
image_data = image_data[np.newaxis, ...].astype(np.float32)
t1 = time.time()
if YOLO_FRAMEWORK == "tf":
pred_bbox = Yolo.predict(image_data)
elif YOLO_FRAMEWORK == "trt":
batched_input = tf.constant(image_data)
result = Yolo(batched_input)
pred_bbox = []
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_frame, input_size,
score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(track_only) != 0 and NUM_CLASS[int(
bbox[5])] in track_only or len(track_only) == 0:
boxes.append([
bbox[0].astype(int), bbox[1].astype(int),
bbox[2].astype(int) - bbox[0].astype(int),
bbox[3].astype(int) - bbox[1].astype(int)
])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores)
features = np.array(encoder(original_frame, boxes))
detections = [
Detection(bbox, score, class_name, feature)
for bbox, score, class_name, feature in zip(
boxes, scores, names, features)
] # if score >= confidence_threshold]
# Pass detections to the deep sort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class(
) # Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(
class_name)] # Get predicted object index by object name
tracked_bboxes.append(
bbox.tolist() + [tracking_id, index, track.class_confidence]
) # Structure data, that we could use it with our draw_bbox function
# draw detection on frame
image = draw_bbox(original_frame,
tracked_bboxes,
CLASSES=CLASSES,
tracking=True,
color=color,
text_output_path=text_output_path,
passenger_detector=passenger_det,
passenger_threshold=face_score_threshold)
t3 = time.time()
times.append(t2 - t1)
times_2.append(t3 - t1)
times = times[-20:]
times_2 = times_2[-20:]
ms = sum(times) / len(times) * 1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2) / len(times_2) * 1000)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# print("Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(ms, fps, fps2))
if vid_output_path != '':
out.write(image)
if show:
cv2.imshow('output', image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
cv2.destroyAllWindows()
def Object_tracking(Yolo,
video_path,
output_path,
input_size=416,
show=True,
CLASSES=YOLO_COCO_CLASSES,
score_threshold=0.3,
iou_threshold=0.45,
rectangle_colors='',
Track_only=[]):
output_file = "D:/PELUSO/ITSligo/lectures_MENG/4-Symulation and Testing/assignments/assignment 2 - group/simV5_anotation.CSV"
csv_file = open(output_file, mode='a') #new
results_csv = csv.writer(csv_file,
delimiter=',',
quotechar='"',
quoting=csv.QUOTE_MINIMAL)
results_csv.writerow([
'Frame_index', 'Score', 'Confidence', 'Pixel_Area', 'X1', 'Y1', 'X2',
'Y2', 'ClassID'
])
# Definition of the parameters
max_cosine_distance = 0.9
nn_budget = None
#initialize deep sort object
model_filename = "D:/PELUSO/ITSligo/lectures_MENG/4-Symulation and Testing/assignments/assignment 2 - group/TensorFlow-2.x-YOLOv3-master/model_data/mars-small128.pb" ##'model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
times, times_2 = [], []
if video_path:
vid = cv2.VideoCapture(video_path) # detect on video
else:
vid = cv2.VideoCapture(0) # detect from webcam
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*'mp4v') ##(*'XVID')
out = cv2.VideoWriter(output_path, codec, fps,
(width, height)) # output_path must be .mp4
print("FPS:::::" + str(fps))
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
frame_idx = 0
while True:
_, frame = vid.read()
try:
original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
except:
break
image_data = image_preprocess(np.copy(original_frame),
[input_size, input_size])
#image_data = tf.expand_dims(image_data, 0)
image_data = image_data[np.newaxis, ...].astype(np.float32)
t1 = time.time()
if YOLO_FRAMEWORK == "tf":
pred_bbox = Yolo.predict(image_data)
elif YOLO_FRAMEWORK == "trt":
batched_input = tf.constant(image_data)
result = Yolo(batched_input)
pred_bbox = []
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
#t1 = time.time()
#pred_bbox = Yolo.predict(image_data)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_frame, input_size,
score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms', sigma=0.4)
print(np.argmax(pred_bbox[:, 5:], axis=-1))
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(Track_only) != 0 and NUM_CLASS[int(
bbox[5])] in Track_only or len(Track_only) == 0:
boxes.append([
bbox[0].astype(int), bbox[1].astype(int),
bbox[2].astype(int) - bbox[0].astype(int),
bbox[3].astype(int) - bbox[1].astype(int)
])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores) ##---esse eh o fidaputi
features = np.array(encoder(original_frame, boxes))
detections = [
Detection(bbox, score, class_name, feature)
for bbox, score, class_name, feature in zip(
boxes, scores, names, features)
]
# Pass detections to the deepsort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
tracked_scores = []
for i, track in enumerate(tracker.tracks):
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class(
) #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(
class_name)] # Get predicted object index by object name
tracked_bboxes.append(
bbox.tolist() + [tracking_id, index]
) # Structure data, that we could use it with our draw_bbox function
try:
tracked_scores.append(track.Confidence) ##new
except:
print("skip")
# draw detection on frame draw_bbox(image, bboxes, CLASSES=YOLO_COCO_CLASSES, show_label=True, show_confidence = True, Text_colors=(255,255,0), rectangle_colors='', tracking=False)
frame_idx += 1
print("------frame_idx-------" + str(frame_idx))
image = draw_bbox(original_frame,
results_csv,
tracked_bboxes,
frame_idx,
tracked_scores,
show_label=True,
show_confidence=True,
CLASSES=CLASSES,
tracking=True) ##new
t3 = time.time()
times.append(t2 - t1)
times_2.append(t3 - t1)
times = times[-20:]
times_2 = times_2[-20:]
ms = sum(times) / len(times) * 1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2) / len(times_2) * 1000)
##image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# draw original yolo detection
#image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
print(
"Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(
ms, fps, fps2))
if output_path != '': out.write(image)
if show:
cv2.imshow('output', image)
if cv2.waitKey(25) & 0xFF == ord("q"):
cv2.destroyAllWindows()
break
results_csv.close()
cv2.destroyAllWindows()
tracker.predict()
tracker.update(detections)
tracked_bboxes = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr()
class_name = track.get_class()
tracking_id = track.track_id
index = key_list[val_list.index(class_name)]
tracked_bboxes.append(bbox.tolist() + [tracking_id, index])
image = draw_bbox(original_frame,
tracked_bboxes,
CLASSES=YOLO_COCO_CLASSES,
tracking=True)
t3 = time.time()
times.append(tEnd - tStart)
times_2.append(t3 - tStart)
ms = sum(times) / len(times_2) * 1000
ms = sum(times) / len(times) * 1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2) / len(times_2) * 1000)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
print("Time:{:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(
ms, fps, fps2))
def Object_tracking(Yolo,
video_path,
input_size=416,
CLASSES=YOLO_COCO_CLASSES,
score_threshold=0.3,
iou_threshold=0.45,
Track_only=["person"]):
# Definition of the parameters
max_cosine_distance = 0.7
nn_budget = None
#initialize deep sort object
model_filename = 'data/model_data/mars-small128.pb'
encoder = gdet.create_box_encoder(model_filename, batch_size=1)
metric = nn_matching.NearestNeighborDistanceMetric("cosine",
max_cosine_distance,
nn_budget)
tracker = Tracker(metric)
times, times_2 = [], []
vid = cv2.VideoCapture(video_path) # detect on video
# by default VideoCapture returns float instead of int
width = int(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(vid.get(cv2.CAP_PROP_FPS))
codec = cv2.VideoWriter_fourcc(*'XVID')
NUM_CLASS = read_class_names(CLASSES)
key_list = list(NUM_CLASS.keys())
val_list = list(NUM_CLASS.values())
for x in range(120):
_, frame = vid.read()
try:
original_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
original_frame = cv2.cvtColor(original_frame, cv2.COLOR_BGR2RGB)
except:
break
image_data = image_preprocess(np.copy(original_frame),
[input_size, input_size])
#image_data = tf.expand_dims(image_data, 0)
image_data = image_data[np.newaxis, ...].astype(np.float32)
t1 = time.time()
if YOLO_FRAMEWORK == "tf":
pred_bbox = Yolo.predict(image_data)
elif YOLO_FRAMEWORK == "trt":
batched_input = tf.constant(image_data)
result = Yolo(batched_input)
pred_bbox = []
for key, value in result.items():
value = value.numpy()
pred_bbox.append(value)
#t1 = time.time()
#pred_bbox = Yolo.predict(image_data)
t2 = time.time()
pred_bbox = [tf.reshape(x, (-1, tf.shape(x)[-1])) for x in pred_bbox]
pred_bbox = tf.concat(pred_bbox, axis=0)
bboxes = postprocess_boxes(pred_bbox, original_frame, input_size,
score_threshold)
bboxes = nms(bboxes, iou_threshold, method='nms')
# extract bboxes to boxes (x, y, width, height), scores and names
boxes, scores, names = [], [], []
for bbox in bboxes:
if len(Track_only) != 0 and NUM_CLASS[int(
bbox[5])] in Track_only or len(Track_only) == 0:
boxes.append([
bbox[0].astype(int), bbox[1].astype(int),
bbox[2].astype(int) - bbox[0].astype(int),
bbox[3].astype(int) - bbox[1].astype(int)
])
scores.append(bbox[4])
names.append(NUM_CLASS[int(bbox[5])])
# Obtain all the detections for the given frame.
boxes = np.array(boxes)
names = np.array(names)
scores = np.array(scores)
features = np.array(encoder(original_frame, boxes))
detections = [
Detection(bbox, score, class_name, feature)
for bbox, score, class_name, feature in zip(
boxes, scores, names, features)
]
# Pass detections to the deepsort object and obtain the track information.
tracker.predict()
tracker.update(detections)
# Obtain info from the tracks
tracked_bboxes = []
for track in tracker.tracks:
if not track.is_confirmed() or track.time_since_update > 5:
continue
bbox = track.to_tlbr() # Get the corrected/predicted bounding box
class_name = track.get_class(
) #Get the class name of particular object
tracking_id = track.track_id # Get the ID for the particular track
index = key_list[val_list.index(
class_name)] # Get predicted object index by object name
tracked_bboxes.append(
bbox.tolist() + [tracking_id, index]
) # Structure data, that we could use it with our draw_bbox function
# draw detection on frame
image = draw_bbox(original_frame,
tracked_bboxes,
CLASSES=CLASSES,
tracking=True)
t3 = time.time()
times.append(t2 - t1)
times_2.append(t3 - t1)
times = times[-20:]
times_2 = times_2[-20:]
ms = sum(times) / len(times) * 1000
fps = 1000 / ms
fps2 = 1000 / (sum(times_2) / len(times_2) * 1000)
image = cv2.putText(image, "Time: {:.1f} FPS".format(fps), (0, 30),
cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 2)
# draw original yolo detection
#image = draw_bbox(image, bboxes, CLASSES=CLASSES, show_label=False, rectangle_colors=rectangle_colors, tracking=True)
out_tracked_bboxes.append(tracked_bboxes)
print(
"Time: {:.2f}ms, Detection FPS: {:.1f}, total FPS: {:.1f}".format(
ms, fps, fps2))
