Spaces:
Runtime error
Runtime error
File size: 7,094 Bytes
0d604bc b60db7e 0d604bc 462bb22 0d604bc fb370ac c8b0e99 0d604bc f501bc2 0d604bc f501bc2 0d604bc e88ad6a 0d604bc ab26cd9 0d604bc 832c49c 0d604bc 832c49c 0d604bc f501bc2 0d604bc f501bc2 0d604bc c1777d4 1fcad52 c1777d4 ab26cd9 bf3e5b9 ab26cd9 c1777d4 97e475b 33d89ab 97e475b 33d89ab 97e475b 33d89ab ab26cd9 97e475b f501bc2 97e475b d2d0a4d 97e475b 79e162c 97e475b 0d604bc f501bc2 c1777d4 97e475b f501bc2 c1777d4 97e475b 0d604bc 49a40ea c8b0e99 f501bc2 ed661bb 1fcad52 a250999 49a40ea 0d604bc f501bc2 1fcad52 49a40ea 8106f16 f501bc2 df4dacd |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
# Bissmillah
import streamlit as st
import os
from os import listdir
import wget
from PIL import Image
import io
import numpy as np
import cv2
import itertools
import sys
def load_model():
wpath = 'test_detection/yolov5/weights/crowdhuman_yolov5m.pt'
if not os.path.exists(wpath):
#st.write('path didnt exist, so creation ! ')
#os.system("python pip uninstall opencv-python")
os.system("python -m pip install numpy torch pandas Pillow opencv-python-headless PyYAML>=5.3.1 torchvision>=0.8.1 matplotlib seaborn>=0.11.0 easydict")
with st.spinner('Downloading model weights for crowdhuman_yolov5m'):
#os.system('wget -O yolov5/weights/crowdhuman_yolov5m.pt https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/releases/download/v.2.0/crowdhuman_yolov5m.pt')
os.system('wget -nc https://github.com/mikel-brostrom/Yolov5_DeepSort_Pytorch/releases/download/v.2.0/crowdhuman_yolov5m.pt -O test_detection/yolov5/weights/crowdhuman_yolov5m.pt')
#st.write('in function load_model', os.listdir('yolov5/weights/'))
else:
#st.write('path alredy exist, so no creation ! ')
print("Model is here.")
#===================================================================================
# Ft saving uploaded video to directory
def save_uploaded_vid(uploadedfile):
with open(os.path.join("test_detection/data", uploadedfile.name),"wb") as f:
f.write(uploadedfile.getbuffer())
return #st.success("Video saved in data dir ")
#@st.cache(ttl=3600, max_entries=10)
def load_output_video(vid):
if isinstance(vid, str):
video = open(vid, 'rb')
else:
video = vid.read()
vname = vid.name
save_uploaded_vid(vid)
return video
def starter():
st.image('test_detection/data/LOGOGlob.png', width = 400)
st.title("Test of Person detection")
st.text("")
st.text("")
#st.success("Welcome! Please upload a video!")
args = { 'HirakAlger' : '112vHirakAlger_09042021_s.mp4' }
vid_upload = st.file_uploader(label= 'Welcome! Please upload a video! ', type = ['mp4', 'avi'])
vid_open = "test_detection/data/"+args['HirakAlger'] if vid_upload is None else vid_upload
vname = args['HirakAlger'] if vid_upload is None else vid_upload.name
video = load_output_video(vid_open)
st.video(video)
#st.write('in function : vname = ', vname)
#st.write('in function ', os.listdir('test_detection/data/'))
#st.write('in function ', os.listdir('test_detection/yolov5/weights/'))
vidcap = cv2.VideoCapture( "test_detection/data/"+vname)
#frames = cv.get_frames("data/"+vname)
success, frame0 = vidcap.read()
frame0 = cv2.cvtColor(frame0, cv2.COLOR_BGR2RGB)
#st.write('shape of frame 01 : ', frame0.shape)
return vname, frame0
#===================================================================================
def prediction(vname):
vpath='test_detection/data/'+vname
wpath = 'test_detection/yolov5/weights/crowdhuman_yolov5m.pt'
if os.path.exists(wpath):
with st.spinner('Running detection...'):
os.system("python test_detection/track.py --yolo_weights test_detection/yolov5/weights/crowdhuman_yolov5m.pt --img 352 --save-vid --save-txt --classes 1 --conf-thres 0.4 --source " + vpath)
os.system("ffmpeg -i inference/output/"+vname + " -vcodec libx264 -y inference/output/output_"+vname)
path = 'inference/output/output_'+vname
if os.path.exists(path):
video_file = open(path, 'rb')
video_bytes = video_file.read()
st.video(video_bytes)
#===================================================================================
def extract_heads(filepath, frame0):
nbperson = 0
listhead = []
if os.path.exists(filepath):
#st.write("filepath : ", filepath)
array_from_file = np.loadtxt(filepath, dtype=int)
#st.write('np of array load : ', array_from_file.shape)
array_from_file = np.delete(array_from_file,np.s_[7:10], axis=1)
nbperson = np.unique(array_from_file[:,1]).shape[0]
st.subheader('Display some detected heads :')
st.write(' Number of heads detected : ', nbperson )
rows = 5
cols = 10
nbheads = rows*cols
frame = frame0
cont = array_from_file
for a in range(nbheads):
numh = a
head = frame[cont[numh][3]:cont[numh][3]+cont[numh][5],cont[numh][2]:cont[numh][2]+cont[numh][4],:]
listhead.append(head)
#st.write('Len of liste heads : ', len(listhead))
return nbperson, listhead
def display_heads(nbperson, listhead):
#os.system("streamlit run https://gist.githubusercontent.com/treuille/da70b4888f8b706fca7afc765751cd85/raw/0727bb67defd93774dae65a2bc6917f72e267460/image_paginator.py")
image_iterator = paginator("Select a sunset page", listhead)
indices_on_page, images_on_page = map(list, zip(*image_iterator))
st.image(images_on_page, width=150, caption=indices_on_page)
return
def paginator(label, items, items_per_page=10, on_sidebar=True):
# Figure out where to display the paginator
if on_sidebar:
location = st.sidebar.empty()
else:
location = st.empty()
# Display a pagination selectbox in the specified location.
items = list(items)
n_pages = len(items)
n_pages = (len(items) - 1) // items_per_page + 1
page_format_func = lambda i: "Page %s" % i
page_number = location.selectbox(label, range(n_pages), format_func=page_format_func)
# Iterate over the items in the page to let the user display them.
min_index = page_number * items_per_page
max_index = min_index + items_per_page
return itertools.islice(enumerate(items), min_index, max_index)
#===================================================================================
def main():
vname, frame0 = starter()
#st.write('vname befor prediction ',vname)
if st.button('Heads detection!'):
prediction(vname)
filepath = 'inference/output/'+vname
filepath = filepath[:-3]+'txt'
#st.write('filepath : ',filepath)
st.success("Click again to retry or try a different video by uploading")
nbperson, listhead = extract_heads(filepath, frame0)
display_heads(nbperson, listhead)
return
if __name__ == '__main__':
os.system('git clone --recurse-submodules https://github.com/nnassime/test_detection.git')
#st.write(os.listdir())
#st.write(os.listdir('test_detection/'))
#os.system('python test_detection/__streamlit_app.py')
load_model()
st.write("bismillah")
print("Bismillah")
main()
#st.write('out function ', os.listdir('test_detection/data/'))
#st.write('out function ', os.listdir('test_detection/yolov5/weights/'))
|