def main():
graph, sess = load_graph(FLAGS.pre_trained_model_path)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, FLAGS.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FLAGS.height)
mp = _mp.get_context("spawn")
v = mp.Value('i', 0)
lock = mp.Lock()
process = mp.Process(target=mario, args=(v, lock))
process.start()
while True:
key = cv2.waitKey(10)
if key == ord("q"):
break
_, frame = cap.read()
frame = cv2.flip(frame, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes, scores, classes = detect_hands(frame, graph, sess)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
results = predict(boxes, scores, classes, FLAGS.threshold, FLAGS.width, FLAGS.height)
if len(results) == 1:
x_min, x_max, y_min, y_max, category = results[0]
x = int((x_min + x_max) / 2)
y = int((y_min + y_max) / 2)
cv2.circle(frame, (x, y), 5, RED, -1)
if category == "Open" and x <= FLAGS.width / 3:
action = 7 # Left jump
text = "Jump left"
elif category == "Closed" and x <= FLAGS.width / 3:
action = 6 # Left
text = "Run left"
elif category == "Open" and FLAGS.width / 3 < x <= 2 * FLAGS.width / 3:
action = 5 # Jump
text = "Jump"
elif category == "Closed" and FLAGS.width / 3 < x <= 2 * FLAGS.width / 3:
action = 0 # Do nothing
text = "Stay"
elif category == "Open" and x > 2 * FLAGS.width / 3:
action = 2 # Right jump
text = "Jump right"
elif category == "Closed" and x > 2 * FLAGS.width / 3:
action = 1 # Right
text = "Run right"
else:
action = 0
text = "Stay"
with lock:
v.value = action
cv2.putText(frame, "{}".format(text), (x_min, y_min - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, GREEN, 2)
overlay = frame.copy()
cv2.rectangle(overlay, (0, 0), (int(FLAGS.width / 3), FLAGS.height), ORANGE, -1)
cv2.rectangle(overlay, (int(2 * FLAGS.width / 3), 0), (FLAGS.width, FLAGS.height), ORANGE, -1)
cv2.addWeighted(overlay, FLAGS.alpha, frame, 1 - FLAGS.alpha, 0, frame)
cv2.imshow('Detection', frame)
cap.release()
cv2.destroyAllWindows()
def baseSETemplate(uniprot_id):
prediction = predict(uniprot_id)
st.markdown("---")
st.header("**Results**")
st.markdown("######")
profile_col1, profile_col2, profile_col3 = st.beta_columns([12, 1, 12])
with profile_col1:
# Protein Profile
protein_id, protein_name, protein_gene_name, protein_org = prediction[
2]
st.subheader("**Protein Profile**")
st.markdown(
f"**UniProt ID**: \n{protein_id} \n**Gene Name**: \n{protein_gene_name} \n**Name**: \n{protein_name} \n**Organism**: \n{protein_org}"
)
st.markdown("###")
with profile_col3:
# Query Homology Profile
query_id, query_name, query_type = homology_profile(prediction[0])
st.subheader("**Predicted Protein Homology Classification**")
st.markdown(
f"**InterPro ID**: \n{query_id} \n**Name**: \n{query_name} \n**Type**: \n{query_type}"
)
st.markdown("###")
query_reference = prediction[1].columns.tolist()
query_results = result(query_reference)
# Protein Metadata Attribute Profile
st.subheader("**Protein Metadata Attributes**")
st.markdown(
"Expand/Collapse the following accordions to view metadata relevant to the protein query"
)
for i in query_results:
df_name, df_result = i[0], i[1]
with st.beta_expander(df_name):
st.plotly_chart(df_result, use_container_width=True)
st.markdown("#")
st.subheader("**Protein Homology Membership**")
st.markdown(
"Explore the protein members under the same homology group as the query protein."
)
# st.markdown("###")
st.markdown(f"**InterPro ID**: {query_id} \n**Name**: {query_name}")
protein_members, protein_members_metadata = protein_membership_profile(
query_id, query_type)
with st.beta_expander("Members List"):
st.table(protein_members)
with st.beta_expander("Members Metadata"):
st.table(protein_members_metadata)
def main():
graph, sess = load_graph(FLAGS.pre_trained_model_path)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, FLAGS.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FLAGS.height)
mp = _mp.get_context("spawn")
v = mp.Value('i', 0)
lock = mp.Lock()
process = mp.Process(target=battle_city, args=(v, lock))
process.start()
x_center = int(FLAGS.width / 2)
y_center = int(FLAGS.height / 2)
radius = int(min(FLAGS.width, FLAGS.height) / 6)
while True:
key = cv2.waitKey(10)
if key == ord("q"):
break
_, frame = cap.read()
frame = cv2.flip(frame, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes, scores, classes = detect_hands(frame, graph, sess)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
results = predict(boxes, scores, classes, FLAGS.threshold, FLAGS.width, FLAGS.height)
if len(results) == 1:
x_min, x_max, y_min, y_max, category = results[0]
x = int((x_min + x_max) / 2)
y = int((y_min + y_max) / 2)
cv2.circle(frame, (x, y), 5, RED, -1)
if category == "Closed" and np.linalg.norm((x - x_center, y - y_center)) <= radius:
action = 0 # Stay
text = "Stay"
elif category == "Closed" and is_in_triangle((x, y), [(0, 0), (FLAGS.width, 0),
(x_center, y_center)]):
action = 1 # Up
text = "Up"
elif category == "Closed" and is_in_triangle((x, y), [(0, FLAGS.height),
(FLAGS.width, FLAGS.height), (x_center, y_center)]):
action = 2 # Down
text = "Down"
elif category == "Closed" and is_in_triangle((x, y), [(0, 0),
(0, FLAGS.height),
(x_center, y_center)]):
action = 3 # Left
text = "Left"
elif category == "Closed" and is_in_triangle((x, y), [(FLAGS.width, 0), (FLAGS.width, FLAGS.height),
(x_center, y_center)]):
action = 4 # Right
text = "Right"
elif category == "Open":
action = 5 # Fire
text = "Fire"
else:
action = 0
text = "Stay"
with lock:
v.value = action
cv2.putText(frame, "{}".format(text), (x_min, y_min - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, GREEN, 2)
overlay = frame.copy()
cv2.drawContours(overlay, [np.array([(0, 0), (FLAGS.width, 0), (x_center, y_center)])], 0,
CYAN, -1)
cv2.drawContours(overlay, [
np.array([(0, FLAGS.height), (FLAGS.width, FLAGS.height), (x_center, y_center)])], 0,
CYAN, -1)
cv2.drawContours(overlay, [
np.array([(0, 0), (0, FLAGS.height), (x_center, y_center)])], 0,
YELLOW, -1)
cv2.drawContours(overlay, [np.array([(FLAGS.width, 0), (FLAGS.width, FLAGS.height), (x_center, y_center)])], 0,
YELLOW, -1)
cv2.circle(overlay, (x_center, y_center), radius, BLUE, -1)
cv2.addWeighted(overlay, FLAGS.alpha, frame, 1 - FLAGS.alpha, 0, frame)
cv2.imshow('Detection', frame)
cap.release()
cv2.destroyAllWindows()
help="provide /path/to/image, default 'flowers/test/101/image_07949.jpg'",
type=str,
default='flowers/test/101/image_07949.jpg')
parser.add_argument(
"checkpoint",
help=
"provide checkpoint to load the model for prediction, e.g. 'checkpoints/checkpoint_resnet18_1epochs.pth'",
type=str)
parser.add_argument(
"--top_k",
help="provide the number of top probabilities to display, default 1",
type=int,
default=1)
parser.add_argument(
"--category_names",
help=
"specify the location of json file to map categories to real flower names, e.g. cat_to_name.json",
type=str,
default=None)
parser.add_argument("--gpu",
help="use gpu for prediction",
action="store_true")
args = parser.parse_args()
#with active_session():
predict(image_path=args.input,
checkpoint=args.checkpoint,
topk=args.top_k,
pred_cat_names=args.category_names,
use_gpu=args.gpu)
def main():
graph, sess = load_graph(FLAGS.pre_trained_model_path)
cap = cv2.VideoCapture(0)
cap.set(cv2.CAP_PROP_FRAME_WIDTH, FLAGS.width)
cap.set(cv2.CAP_PROP_FRAME_HEIGHT, FLAGS.height)
mp = _mp.get_context("spawn")
v = mp.Value('i', 0)
lock = mp.Lock()
process = mp.Process(target=mimic, args=(v, lock))
process.start()
x_center = int(FLAGS.width / 2)
y_center = int(FLAGS.height / 2)
radius = int(min(FLAGS.width, FLAGS.height) / 4)
while True:
key = cv2.waitKey(10)
if key == ord("q"):
break
_, frame = cap.read()
frame = cv2.flip(frame, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes, scores, classes = detect_hands(frame, graph, sess)
results = predict(boxes, scores, classes, FLAGS.threshold, FLAGS.width,
FLAGS.height)
text = "Oof"
top_left_square_corr = np.array([(0, 0), (FLAGS.width // 3, 0),
(FLAGS.width // 3, FLAGS.height // 2),
(0, FLAGS.height // 2)])
bottom_left_square_corr = np.array([(0, FLAGS.height),
(0, FLAGS.height // 2),
(FLAGS.width // 3,
FLAGS.height // 2),
(FLAGS.width // 3, FLAGS.height)])
bottom_right_square_corr = np.array([
(FLAGS.width, FLAGS.height),
(FLAGS.width - FLAGS.width // 3, FLAGS.height),
(FLAGS.width - FLAGS.width // 4, FLAGS.height - FLAGS.height // 3),
(FLAGS.width, FLAGS.height - FLAGS.height // 3)
])
top_right_square_corr = np.array([(FLAGS.width, 0),
(FLAGS.width - FLAGS.width // 4, 0),
(FLAGS.width - FLAGS.width // 4,
FLAGS.height // 3),
(FLAGS.width, FLAGS.height // 3)])
if len(results) == 1:
x_min, x_max, y_min, y_max, category = results[0]
x = int((x_min + x_max) / 2)
y = int((y_min + y_max) / 2)
cv2.circle(frame, (x, y), 10, RED, -1)
if category == "Open" and np.linalg.norm(
(x - x_center, y - y_center)) <= radius:
action = 1
text = action
elif category == "Open" and is_in_square(
(x, y), top_left_square_corr):
action = 3
text = action
elif category == "Open" and is_in_square(
(x, y), top_right_square_corr):
action = 2
text = action
elif category == "Closed" and is_in_square(
(x, y), bottom_right_square_corr):
action = 4
text = action
else:
action = 0
with lock:
v.value = action
cv2.putText(frame, "{}".format(text), (x_min, y_min - 5),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, GREEN, 2)
overlay = frame.copy()
height = FLAGS.height // 3
width = FLAGS.width // 3
cv2.drawContours(overlay, [top_left_square_corr], 0, CYAN, -1)
cv2.drawContours(overlay, [bottom_right_square_corr], 0, RED, -1)
cv2.drawContours(overlay, [bottom_left_square_corr], 0, GREEN, -1)
cv2.drawContours(overlay, [top_right_square_corr], 0, YELLOW, -1)
cv2.circle(overlay, (x_center, y_center), radius, BLUE, -1)
cv2.addWeighted(overlay, FLAGS.alpha, frame, 1 - FLAGS.alpha, 0, frame)
cv2.imshow('Detection', frame)
cap.release()
cv2.destroyAllWindows()
count_last = 0
cv2.namedWindow("hand", flags=cv2.WINDOW_NORMAL)
cv2.createTrackbar("upper", "hand", 0, 255, nothing)
cv2.createTrackbar("lower", "hand", 0, 255, nothing)
while (1):
try:
#START SEGMENTING SKIN COLOR
ret, frame = vc.read()
frame = cv2.flip(frame, 1)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
boxes, scores, classes = detect_hands(frame, graph, sess)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
results = predict(boxes, scores, classes, 0.6, 640, 480)
if len(results) == 1:
H = frame.shape[0]
W = frame.shape[1]
black = frame.copy()
cv2.rectangle(black, (0, 0), (W, H), (0, 0, 0), -1)
x_min, x_max, y_min, y_max, _ = results[0]
crop = frame[y_min:y_max, x_min:x_max]
black[y_min:y_max, x_min:x_max] = crop
cv2.rectangle(frame, (x_min, y_min), (x_max, y_max),
(255, 0, 0), 2)
# cv2.rectangle(frame,(450,270),(452,272),(0,255,0),0)
# if(cv2.waitKey(20) == 32):
# bgr = frame[271, 451]