# 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/'))