import subprocess import os if os.getenv('SYSTEM') == 'spaces': subprocess.call('pip install gradio==4.29.0'.split()) subprocess.call('pip install -U openmim'.split()) subprocess.call('pip install python-dotenv'.split()) subprocess.call('pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113'.split()) subprocess.call('mim install mmcv>=2.0.0'.split()) subprocess.call('mim install mmengine==0.7.2'.split()) subprocess.call('mim install mmdet==3.0.0'.split()) subprocess.call('pip install opencv-python'.split()) subprocess.call('pip install git+https://github.com/cocodataset/panopticapi.git'.split()) import gradio as gr from huggingface_hub import snapshot_download import cv2 import dotenv dotenv.load_dotenv() import numpy as np import gradio as gr import glob from inference import inference_frame,inference_frame_serial from inference import inference_frame_par_ready from inference import process_frame from inference import classes from inference import class_sizes_lower from metrics import process_results_for_plot from metrics import prediction_dashboard import os import pathlib import multiprocessing as mp from time import time if not os.path.exists('videos_example'): REPO_ID='SharkSpace/videos_examples' snapshot_download(repo_id=REPO_ID, token=os.environ.get('SHARK_MODEL'),repo_type='dataset',local_dir='videos_example') theme = gr.themes.Soft( primary_hue="sky", neutral_hue="slate", ) def add_border(frame, color = (255, 0, 0), thickness = 2): # Add a red border to the image relative = max(frame.shape[0],frame.shape[1]) top = int(relative*0.025) bottom = int(relative*0.025) left = int(relative*0.025) right = int(relative*0.025) # Add the border to the image bordered_image = cv2.copyMakeBorder(frame, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) return bordered_image def overlay_text_on_image(image, text_list, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=0.5, font_thickness=1, margin=10, color=(255, 255, 255), box_color=(0,0,0)): relative = min(image.shape[0], image.shape[1]) y0, dy = margin, int(relative*0.1) # start y position and line gap for i, line in enumerate(text_list): y = y0 + i * dy if 'Shark' in line or 'Human' in line: current_font_size = font_size * 1.2 text_width, text_height = cv2.getTextSize(line, font, current_font_size, font_thickness)[0] cv2.rectangle(image, (image.shape[1] - text_width - margin - 5, y - text_height), (image.shape[1] - margin + 5, y + 5), box_color, -1) cv2.putText(image, line, (image.shape[1] - text_width - margin, y), font, current_font_size, color, font_thickness, lineType=cv2.LINE_AA) else: current_font_size = font_size text_width, text_height = cv2.getTextSize(line, font, current_font_size, font_thickness)[0] cv2.rectangle(image, (image.shape[1] - text_width - margin - 5, y - text_height), (image.shape[1] - margin + 5, y + 5), box_color, -1) cv2.putText(image, line, (image.shape[1] - text_width - margin, y), font, current_font_size, color, font_thickness, lineType=cv2.LINE_AA) return image def overlay_logo(frame,logo, position=(10, 10)): """ Overlay a transparent logo (with alpha channel) on a frame. Parameters: - frame: The main image/frame to overlay the logo on. - logo_path: Path to the logo image. - position: (x, y) tuple indicating where the logo starts (top left corner). """ # Load the logo and its alpha channel alpha_channel = np.ones(logo.shape[:2], dtype=logo.dtype) print(logo.min(),logo.max()) logo = np.dstack((logo, alpha_channel)) indexes = logo[:,:,1]>150 logo[indexes,3] = 0 l_channels = cv2.split(logo) if len(l_channels) != 4: raise ValueError("Logo doesn't have an alpha channel!") l_b, l_g, l_r, l_alpha = l_channels cv2.imwrite('l_alpha.png',l_alpha*255) # Extract regions of interest (ROI) from both images roi = frame[position[1]:position[1]+logo.shape[0], position[0]:position[0]+logo.shape[1]] # Blend the logo using the alpha channel for channel in range(0, 3): roi[:, :, channel] = (l_alpha ) * l_channels[channel] + (1.0 - l_alpha ) * roi[:, :, channel] return frame def add_danger_symbol_from_image(frame, top_pred): relative = max(frame.shape[0],frame.shape[1]) if top_pred['shark_sighted'] and top_pred['dangerous_dist']: # Add the danger symbol danger_symbol = cv2.imread('static/danger_symbol.jpeg') danger_symbol = cv2.resize(danger_symbol, (int(relative*0.1), int(relative*0.1)), interpolation = cv2.INTER_AREA)[:,:,::-1] frame = overlay_logo(frame,danger_symbol, position=(int(relative*0.05), int(relative*0.05))) return frame def draw_cockpit(frame, top_pred,cnt): # Bullet points: high_danger_color = (255,0,0) low_danger_color = yellowgreen = (154,205,50) if top_pred['shark_sighted'] > 0: shark_suspected = 'Shark Sighted !' elif top_pred['shark_suspected'] > 0: shark_suspected = 'Shark Suspected !' else: shark_suspected = 'No Sharks ...' if top_pred['human_sighted'] > 0: human_suspected = 'Human Sighted !' elif top_pred['human_suspected'] > 0: human_suspected = 'Human Suspected !' else: human_suspected = 'No Humans ...' shark_size_estimate = 'Biggest shark size: ' + str(top_pred['biggest_shark_size']) if top_pred['biggest_shark_size'] else 'Biggest shark size: ...' shark_weight_estimate = 'Biggest shark weight: ' + str(top_pred['biggest_shark_weight']) if top_pred['biggest_shark_weight'] else 'Biggest shark weight: ...' danger_level = 'Danger Level: ' danger_level += 'High' if top_pred['dangerous_dist_confirmed'] else 'Low' danger_color = 'orangered' if top_pred['dangerous_dist_confirmed'] else 'yellowgreen' # Create a list of strings to plot strings = [shark_suspected, human_suspected, shark_size_estimate, danger_level] # shark_sighted = 'Shark Detected: ' + str(top_pred['shark_sighted']) # human_sighted = 'Number of Humans: ' + str(top_pred['human_n']) # shark_size_estimate = 'Biggest shark size: ' + str(top_pred['biggest_shark_size']) # shark_weight_estimate = 'Biggest shark weight: ' + str(top_pred['biggest_shark_weight']) # danger_level = 'Danger Level: ' # danger_level += 'High' if top_pred['dangerous_dist'] else 'Low' # danger_color = 'orangered' if top_pred['dangerous_dist'] else 'yellowgreen' # # Create a list of strings to plot # strings = [shark_sighted, human_sighted, shark_size_estimate, shark_weight_estimate, danger_level] relative = max(frame.shape[0],frame.shape[1]) if top_pred['shark_sighted'] and top_pred['dangerous_dist_confirmed'] and cnt%2 == 0: #frame = add_border(frame, color=high_danger_color, thickness=int(relative*0.025)) frame = add_danger_symbol_from_image(frame, top_pred) elif top_pred['shark_sighted'] and not top_pred['dangerous_dist_confirmed'] and cnt%2 == 0: #frame = add_border(frame, color=low_danger_color, thickness=int(relative*0.025)) frame = add_danger_symbol_from_image(frame, top_pred) else: frame = add_border(frame, color=(0,0,0), thickness=int(relative*0.025)) overlay_text_on_image(frame, strings, font=cv2.FONT_HERSHEY_SIMPLEX, font_size=relative*0.0007, font_thickness=1, margin=int(relative*0.05), color=(255, 255, 255)) return frame def process_video(input_video, out_fps = 'auto', skip_frames = 12): print('Processing video: ') try: cap = cv2.VideoCapture(input_video.name) except: cap = cv2.VideoCapture(input_video) output_path = "output.mp4" if out_fps != 'auto' and type(out_fps) == int: fps = int(out_fps) else: fps = int(cap.get(cv2.CAP_PROP_FPS)) if out_fps == 'auto': fps = int(fps / skip_frames) width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) if width > 2200 or height > 2000: width = int(width//4) height = int(height//4) video = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) iterating, frame = cap.read() cnt = 0 drawn_count = 0 last_5_shark_detected = np.array([0, 0, 0, 0, 0]) last_5_human_detected = np.array([0, 0, 0, 0, 0]) last_5_dangerous_dist = np.array([0, 0, 0, 0, 0]) while iterating: print('overall count ', cnt) if (cnt % skip_frames) == 0: drawn_count += 1 frame = cv2.resize(frame, (int(width), int(height))) print('starting Frame: ', cnt) # flip frame vertically display_frame, result = inference_frame_serial(frame) #print(result) top_pred = process_results_for_plot(predictions = result.numpy(), classes = classes, class_sizes = class_sizes_lower) # add to last 5 last_5_shark_detected[drawn_count % 5] = int(top_pred['shark_n'] > 0) last_5_human_detected[drawn_count % 5] = int(top_pred['human_n'] > 0) last_5_dangerous_dist[drawn_count % 5] = int(top_pred['dangerous_dist'] > 0) top_pred['shark_sighted'] = int(np.sum(last_5_shark_detected) > 2) top_pred['human_sighted'] = int(np.sum(last_5_human_detected) > 2) top_pred['dangerous_dist_confirmed'] = int(np.sum(last_5_dangerous_dist) > 2) frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) prediction_frame = cv2.cvtColor(display_frame, cv2.COLOR_BGR2RGB) # #video.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) if cnt*skip_frames %2==0: prediction_frame = cv2.resize(prediction_frame, (int(width), int(height))) frame = prediction_frame #if top_pred['shark_sighted'] or top_pred['shark_suspected']: frame = draw_cockpit(frame, top_pred,cnt*skip_frames) frame = cv2.resize(frame, (int(width), int(height))) video.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) pred_dashbord = prediction_dashboard(top_pred = top_pred) drawn_count += 1 #print('sending frame') print('finalizing frame:',cnt) #print(pred_dashbord.shape) #print(frame.shape) #print(prediction_frame.shape) #print(width, height) yield frame cnt += 1 iterating, frame = cap.read() video.release() yield None with gr.Blocks(theme=theme) as demo: gr.Markdown("Alpha Demo of the Sharkpatrol Oceanlife Detector.") with gr.Row(): input_video = gr.File(label="Input",height=50) #output_video = gr.File(label="Output Video",height=50) #.style(equal_height=True,height='25%'): original_frames = gr.Image(label="Processed Frame") #.style( height=650) #processed_frames = gr.Image(label="Shark Engine") #dashboard = gr.Image(label="Events") with gr.Row(): paths = sorted(pathlib.Path('videos_example/').rglob('*.mp4')) samples=[[path.as_posix()] for path in paths if 'raw_videos' in str(path)] examples = gr.Examples(samples, inputs=input_video) process_video_btn = gr.Button("Process Video") #process_video_btn.click(process_video, input_video, [processed_frames, original_frames, output_video, dashboard]) process_video_btn.click(process_video, input_video, [ original_frames]) demo.queue() if os.getenv('SYSTEM') == 'spaces': demo.launch(width='40%',auth=(os.environ.get('SHARK_USERNAME'), os.environ.get('SHARK_PASSWORD'))) else: demo.launch(debug=True,share=True)