"""Create an Object Detection Web App using PyTorch and Streamlit.""" # import libraries from PIL import Image from torchvision import models, transforms import torch import streamlit as st from yolo import YOLO import os import urllib import numpy as np from streamlit_webrtc import webrtc_streamer, WebRtcMode, RTCConfiguration import av # 设置网页的icon st.set_page_config(page_title='Gesture Detector', page_icon='✌', layout='centered', initial_sidebar_state='expanded') RTC_CONFIGURATION = RTCConfiguration( { "RTCIceServer": [{ "urls": ["stun:stun.l.google.com:19302"], "username": "pikachu", "credential": "1234", }] } ) def main(): # Render the readme as markdown using st.markdown. readme_text = st.markdown(open("instructions.md",encoding='utf-8').read()) # Once we have the dependencies, add a selector for the app mode on the sidebar. st.sidebar.title("What to do") app_mode = st.sidebar.selectbox("Choose the app mode", ["Show instructions", "Run the app", "Show the source code"]) if app_mode == "Show instructions": st.sidebar.success('To continue select "Run the app".') elif app_mode == "Show the source code": readme_text.empty() st.code(open("app.py",encoding='utf-8').read()) elif app_mode == "Run the app": # Download external dependencies. for filename in EXTERNAL_DEPENDENCIES.keys(): download_file(filename) readme_text.empty() run_the_app() # External files to download. EXTERNAL_DEPENDENCIES = { "yolov4_tiny.pth": { "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_tiny.pth", "size": 23631189 }, "yolov4_SE.pth": { "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_SE.pth", "size": 23806027 }, "yolov4_CBAM.pth":{ "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_CBAM.pth", "size": 23981478 }, "yolov4_ECA.pth":{ "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_ECA.pth", "size": 23632688 }, "yolov4_weights_ep150_608.pth":{ "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_weights_ep150_608.pth", "size": 256423031 }, "yolov4_weights_ep150_416.pth":{ "url": "https://github.com/Dreaming-future/my_weights/releases/download/v1.3/yolov4_weights_ep150_416.pth", "size": 256423031 }, } # This file downloader demonstrates Streamlit animation. def download_file(file_path): # Don't download the file twice. (If possible, verify the download using the file length.) if os.path.exists(file_path): if "size" not in EXTERNAL_DEPENDENCIES[file_path]: return elif os.path.getsize(file_path) == EXTERNAL_DEPENDENCIES[file_path]["size"]: return # print(os.path.getsize(file_path)) # These are handles to two visual elements to animate. weights_warning, progress_bar = None, None try: weights_warning = st.warning("Downloading %s..." % file_path) progress_bar = st.progress(0) with open(file_path, "wb") as output_file: with urllib.request.urlopen(EXTERNAL_DEPENDENCIES[file_path]["url"]) as response: length = int(response.info()["Content-Length"]) counter = 0.0 MEGABYTES = 2.0 ** 20.0 while True: data = response.read(8192) if not data: break counter += len(data) output_file.write(data) # We perform animation by overwriting the elements. weights_warning.warning("Downloading %s... (%6.2f/%6.2f MB)" % (file_path, counter / MEGABYTES, length / MEGABYTES)) progress_bar.progress(min(counter / length, 1.0)) except Exception as e: print(e) # Finally, we remove these visual elements by calling .empty(). finally: if weights_warning is not None: weights_warning.empty() if progress_bar is not None: progress_bar.empty() # This is the main app app itself, which appears when the user selects "Run the app". def run_the_app(): class Config(): def __init__(self, weights = 'yolov4_tiny.pth', tiny = True, phi = 0, shape = 416,nms_iou = 0.3, confidence = 0.5): self.weights = weights self.tiny = tiny self.phi = phi self.cuda = False self.shape = shape self.confidence = confidence self.nms_iou = nms_iou # set title of app st.markdown('

✌ Gesture Detection

', unsafe_allow_html=True) st.sidebar.markdown("# Gesture Detection on?") activities = ["Example","Image", "Camera", "FPS", "Heatmap","Real Time", "Video"] choice = st.sidebar.selectbox("Choose among the given options:", activities) phi = st.sidebar.selectbox("yolov4-tiny 使用的自注意力模式:",('0tiny','1SE','2CABM','3ECA')) print("") tiny = st.sidebar.checkbox('是否使用 yolov4 tiny 模型') if not tiny: shape = st.sidebar.selectbox("Choose shape to Input:", [416,608]) conf,nms = object_detector_ui() @st.cache def get_yolo(tiny,phi,conf,nms,shape=416): weights = 'yolov4_tiny.pth' if tiny: if phi == '0tiny': weights = 'yolov4_tiny.pth' elif phi == '1SE': weights = 'yolov4_SE.pth' elif phi == '2CABM': weights = 'yolov4_CBAM.pth' elif phi == '3ECA': weights = 'yolov4_ECA.pth' else: if shape == 608: weights = 'yolov4_weights_ep150_608.pth' elif shape == 416: weights = 'yolov4_weights_ep150_416.pth' opt = Config(weights = weights, tiny = tiny , phi = int(phi[0]), shape = shape,nms_iou = nms, confidence = conf) yolo = YOLO(opt) return yolo if tiny: yolo = get_yolo(tiny, phi, conf, nms) st.write("YOLOV4 tiny 模型加载完毕") else: yolo = get_yolo(tiny, phi, conf, nms, shape) st.write("YOLOV4 模型加载完毕") if choice == 'Image': detect_image(yolo) elif choice =='Camera': detect_camera(yolo) elif choice == 'FPS': detect_fps(yolo) elif choice == "Heatmap": detect_heatmap(yolo) elif choice == "Example": detect_example(yolo) elif choice == "Real Time": detect_realtime(yolo) elif choice == "Video": detect_video(yolo) # This sidebar UI lets the user select parameters for the YOLO object detector. def object_detector_ui(): st.sidebar.markdown("# Model") confidence_threshold = st.sidebar.slider("Confidence threshold", 0.0, 1.0, 0.5, 0.01) overlap_threshold = st.sidebar.slider("Overlap threshold", 0.0, 1.0, 0.3, 0.01) return confidence_threshold, overlap_threshold def predict(image,yolo): """Return predictions. Parameters ---------- :param image: uploaded image :type image: jpg :rtype: list :return: none """ crop = False count = False try: # image = Image.open(image) r_image = yolo.detect_image(image, crop = crop, count=count) transform = transforms.Compose([transforms.ToTensor()]) result = transform(r_image) st.image(result.permute(1,2,0).numpy(), caption = 'Processed Image.', use_column_width = True) except Exception as e: print(e) def fps(image,yolo): test_interval = 50 tact_time = yolo.get_FPS(image, test_interval) st.write(str(tact_time) + ' seconds, ', str(1/tact_time),'FPS, @batch_size 1') return tact_time # print(str(tact_time) + ' seconds, ' + str(1/tact_time) + 'FPS, @batch_size 1') def detect_image(yolo): # enable users to upload images for the model to make predictions file_up = st.file_uploader("Upload an image", type = ["jpg","png","jpeg"]) classes = ["up","down","left","right","front","back","clockwise","anticlockwise"] class_to_idx = {cls: idx for (idx, cls) in enumerate(classes)} st.sidebar.markdown("See the model preformance and play with it") if file_up is not None: with st.spinner(text='Preparing Image'): # display image that user uploaded image = Image.open(file_up) st.image(image, caption = 'Uploaded Image.', use_column_width = True) st.balloons() detect = st.button("开始检测Image") if detect: st.write("") st.write("Just a second ...") predict(image,yolo) st.balloons() def detect_camera(yolo): picture = st.camera_input("Take a picture") if picture: filters_to_funcs = { "No filter": predict, "Heatmap": heatmap, "FPS": fps, } filters = st.selectbox("...and now, apply a filter!", filters_to_funcs.keys()) image = Image.open(picture) with st.spinner(text='Preparing Image'): filters_to_funcs[filters](image,yolo) st.balloons() def detect_fps(yolo): file_up = st.file_uploader("Upload an image", type = ["jpg","png","jpeg"]) classes = ["up","down","left","right","front","back","clockwise","anticlockwise"] class_to_idx = {cls: idx for (idx, cls) in enumerate(classes)} st.sidebar.markdown("See the model preformance and play with it") if file_up is not None: # display image that user uploaded image = Image.open(file_up) st.image(image, caption = 'Uploaded Image.', use_column_width = True) st.balloons() detect = st.button("开始检测 FPS") if detect: with st.spinner(text='Preparing Image'): st.write("") st.write("Just a second ...") tact_time = fps(image,yolo) # st.write(str(tact_time) + ' seconds, ', str(1/tact_time),'FPS, @batch_size 1') st.balloons() def heatmap(image,yolo): heatmap_save_path = "heatmap_vision.png" yolo.detect_heatmap(image, heatmap_save_path) img = Image.open(heatmap_save_path) transform = transforms.Compose([transforms.ToTensor()]) result = transform(img) st.image(result.permute(1,2,0).numpy(), caption = 'Processed Image.', use_column_width = True) def detect_heatmap(yolo): file_up = st.file_uploader("Upload an image", type = ["jpg","png","jpeg"]) classes = ["up","down","left","right","front","back","clockwise","anticlockwise"] class_to_idx = {cls: idx for (idx, cls) in enumerate(classes)} st.sidebar.markdown("See the model preformance and play with it") if file_up is not None: # display image that user uploaded image = Image.open(file_up) st.image(image, caption = 'Uploaded Image.', use_column_width = True) st.balloons() detect = st.button("开始检测 heatmap") if detect: with st.spinner(text='Preparing Heatmap'): st.write("") st.write("Just a second ...") heatmap(image,yolo) st.balloons() def detect_example(yolo): st.sidebar.title("Choose an Image as a example") images = os.listdir('./img') images.sort() image = st.sidebar.selectbox("Image Name", images) st.sidebar.markdown("See the model preformance and play with it") image = Image.open(os.path.join('img',image)) st.image(image, caption = 'Choose Image.', use_column_width = True) st.balloons() detect = st.button("开始检测Image") if detect: st.write("") st.write("Just a second ...") predict(image,yolo) st.balloons() def detect_realtime(yolo): class VideoProcessor: def recv(self, frame): img = frame.to_ndarray(format="bgr24") img = Image.fromarray(img) crop = False count = False r_image = yolo.detect_image(img, crop = crop, count=count) transform = transforms.Compose([transforms.ToTensor()]) result = transform(r_image) result = result.permute(1,2,0).numpy() result = (result * 255).astype(np.uint8) return av.VideoFrame.from_ndarray(result, format="bgr24") webrtc_ctx = webrtc_streamer( key="example", mode=WebRtcMode.SENDRECV, rtc_configuration=RTC_CONFIGURATION, media_stream_constraints={"video": True, "audio": False}, async_processing=False, video_processor_factory=VideoProcessor ) import cv2 import time def detect_video(yolo): file_up = st.file_uploader("Upload a video", type = ["mp4"]) print(file_up) classes = ["up","down","left","right","front","back","clockwise","anticlockwise"] if file_up is not None: video_path = 'video.mp4' st.video(file_up) with open(video_path, 'wb') as f: f.write(file_up.read()) detect = st.button("开始检测 Video") if detect: video_save_path = 'video2.mp4' # display image that user uploaded capture = cv2.VideoCapture(video_path) video_fps = st.slider("Video FPS", 5, 30, int(capture.get(cv2.CAP_PROP_FPS)), 1) fourcc = cv2.VideoWriter_fourcc(*'XVID') size = (int(capture.get(cv2.CAP_PROP_FRAME_WIDTH)), int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))) out = cv2.VideoWriter(video_save_path, fourcc, video_fps, size) while(True): # 读取某一帧 ref, frame = capture.read() if not ref: break # 转变成Image # frame = Image.fromarray(np.uint8(frame)) # 格式转变，BGRtoRGB frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) # 转变成Image frame = Image.fromarray(np.uint8(frame)) # 进行检测 frame = np.array(yolo.detect_image(frame)) # RGBtoBGR满足opencv显示格式 frame = cv2.cvtColor(frame,cv2.COLOR_RGB2BGR) # print("fps= %.2f"%(fps)) # frame = cv2.putText(frame, "fps= %.2f"%(fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2) out.write(frame) out.release() capture.release() print("Save processed video to the path :" + video_save_path) with open(video_save_path, "rb") as file: btn = st.download_button( label="Download Video", data=file, file_name="video.mp4", ) st.balloons() if __name__ == "__main__": main()