def anay_video():
in_path = filedialog.askopenfilename(initialdir="./Cameras/" +
camera_ip + "/Videos")
out_path = "./Cameras/" + camera_ip + "/Videos" + '/out_temp.mp4'
json_file_path = "./Cameras/" + camera_ip + "/" + camera_ip + "_metadata.json"
# password
with open(json_file_path, "r") as json_file:
json_data = json.load(json_file)
cap = cv2.VideoCapture(in_path)
flag, frame = cap.read()
cv2.imwrite('./Cameras/' + camera_ip + '/temp.jpg', frame)
ori_reso = (frame.shape[1], frame.shape[0])
x, _ = region_select(camera_ip)
os.remove('./Cameras/' + camera_ip + '/temp.jpg')
VA = VideoAnalysis(in_path,
out_path,
json_data['resolution'],
ori_reso,
30,
json_data['frame_rate'],
sorted_refPt=x)
VA.mask_video()
arr = VA.mask_rcnn_apply()
interested_frame = VA.find_interested_frames(arr, 2)
VA.clip_video(interested_frame)
def compute_analysis(records):
if (len(records)) == 0:
return
video_analysis = VideoAnalysis(records)
st.markdown(get_table_download_link(records), unsafe_allow_html=True)
st.header('Registros de detecciones')
st.dataframe(records)
df = video_analysis.get_complete_class_counts(as_seconds=True)
fig = px.bar(df, x='timestamp', y=df.columns, title='Cantidad de instancias detectadas durante el video')
fig.update_layout(
yaxis=dict(
title=dict(text='instancias'),
),
xaxis=dict(
title=dict(text='segundos'),
rangeslider=dict(
visible=True
),
type="linear"
)
)
st.plotly_chart(
fig)
st.header('Buscar rangos de tiempo donde aparecen un conjunto de clases')
single_class_time_tolerance = st.number_input('Tolerancia de separación de frames (en ms)', min_value=0, step=1000, value=1000)
s1_col1, s1_col2 = st.beta_columns(2)
with s1_col1:
class_selection = st.multiselect('Selecciona las clases que deseas buscar', options=video_analysis.get_unique_classes())
with s1_col2:
ranges = []
if len(class_selection) > 0:
ranges = video_analysis.get_timeranges_with_classes(class_selection, single_class_time_tolerance)
range_table = []
for rng in ranges:
range_table.append({
'Desde': rng[0] / 1000,
'Hasta': rng[1] / 1000
})
st.write('Rangos de tiempo donde aparecen las clases seleccionadas (en segundos)')
st.table(range_table)
instance_counts_selection = {}
st.header('Buscar rangos de tiempo donde aparecen una cantidad especifica de instancias')
instance_count_time_tolerance = st.number_input('Tolerancia de separación de frames (en ms)', min_value=0, step=1000, value=1000, key='instance_tolerance')
s2_col1, s2_col2 = st.beta_columns(2)
with s2_col1:
for classname in video_analysis.get_unique_classes():
instance_counts_selection[classname] = st.number_input(classname, min_value=0, value=0, step=1)
with s2_col2:
clean_selections = {}
for key in instance_counts_selection:
if instance_counts_selection[key] > 0:
clean_selections[key] = int(instance_counts_selection[key])
ranges = []
if len(clean_selections.keys()) > 0:
ranges = video_analysis.get_timeranges_by_instance_counts(clean_selections, instance_count_time_tolerance)
range_table = []
for rng in ranges:
range_table.append({
'Desde': rng[0] / 1000,
'Hasta': rng[1] / 1000
})
st.write('Rangos de tiempo donde aparecen las clases seleccionadas (en segundos)')
st.table(range_table)
def mjpeg():
"""Video streaming route. Put this in the src attribute of an img tag."""
return Response(VideoAnalysis(**parameters).mjpeg_generator(),
mimetype='multipart/x-mixed-replace; boundary=frame',
direct_passthrough=True)
def jpeg():
return Response(VideoAnalysis(**parameters).request_image(),
mimetype='image/jpeg',
direct_passthrough=True)
from region_select import region_select from tkinter import filedialog import json camera_ip = '[email protected]' root_path = '/home/ke/TrafficProject/Mask-RCNN/' in_path = filedialog.askopenfilename(initialdir=root_path + "/Cameras/" + camera_ip + "/Videos") out_path = '/home/ke/TrafficProject/Mask-RCNN/' + "/Cameras/" + camera_ip + "/Videos" + '/out_temp.mp4' json_file_path = root_path + "/Cameras/" + camera_ip + "/" + camera_ip + "_metadata.json" # password with open(json_file_path, "r") as json_file: json_data = json.load(json_file) cap = cv2.VideoCapture(in_path) flag, frame = cap.read() cv2.imwrite('./Cameras/haha/haha.jpg', frame) ori_reso = (frame.shape[1], frame.shape[0]) x, _ = region_select('haha') # A = VideoAnalysis('/home/ke/Downloads/test2.mp4', '/home/ke/Downloads/13.mp4', (640, 400), ori_reso, 30, 1) VA = VideoAnalysis(in_path, out_path, json_data['resolution'], ori_reso, 30, json_data['frame_rate'], sorted_refPt=x) VA.mask_video() arr = VA.mask_rcnn_apply() interested_frame = VA.find_interested_frames(arr, 2) VA.clip_video(interested_frame)
