import PIL from PIL import ImageDraw, ImageFont, Image ,ImageOps ,ImageFilter from ultralytics import YOLO import warnings import cv2 import numpy as np import subprocess import os import matplotlib.pyplot as plt warnings.filterwarnings("ignore", category=FutureWarning) class SegmenterBackground(): def __init__(self) -> None: self.segment_names = {} # This dictionary will store names for segments across multiple inferences self.person=['person'] self.animal=[ 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear','zebra', 'giraffe'] self.drive=['bicycle','car','motorcycle', 'airplane', 'bus', 'train','truck','boat'] def predict_image(self,raw_image: Image): model = YOLO("yolov8n-seg.pt") class_names = model.names results = model(raw_image) return results, class_names def assign_segment_name(self,label, segment_id): """ Assigns a unique name for each detected segment (e.g., Person 1, Person 2). """ if label not in self.segment_names: self.segment_names[label] = {} if segment_id not in self.segment_names[label]: segment_count = len(self.segment_names[label]) + 1 self.segment_names[label][segment_id] = f"{label} {segment_count}" return self.segment_names[label][segment_id] def putMaskImage(self,raw_image,masks,background_image="remove",blur_radius=23): combined_mask = np.max(masks, axis=0) # Create mask for areas to replace mask = combined_mask == True # Ensure the mask has the same shape as the raw image (broadcast the mask to RGB channels) mask_rgb = np.stack([mask] * 3, axis=-1) # Initialize the output array as a copy of the background image ##outpt = np.array(background_image.copy()) if type(background_image)==PIL.Image.Image: # not PIL.JpegImagePlugin.JpegImageFile as resized outpt = np.array(background_image.copy()) elif(background_image=="cam"): outpt=np.array(raw_image.filter(ImageFilter.GaussianBlur(radius=blur_radius))) else:#default ,say on "remove" outpt=np.zeros_like(raw_image) # Replace the background in the output image with the raw image where the mask is True outpt[mask_rgb] = np.array(raw_image)[mask_rgb] # Resize the output for better experience outpt = Image.fromarray(outpt) return outpt def getFont(self): try: font = ImageFont.truetype("arial.ttf", size=20) except IOError: font = ImageFont.load_default() return font def Back_step1(self,raw_image: Image, background_image: Image,blur_radius=23): org_size = raw_image.size raw_image = raw_image.resize((640, 480)) if type(background_image) == PIL.JpegImagePlugin.JpegImageFile: background_image = background_image.resize((640, 480)) label_counter = [] results, class_names = self.predict_image(raw_image) masks = [results[0].masks.data[i].cpu().numpy() for i in range(len(results[0].masks.data))] ##### put masks on image outpt = self.putMaskImage(raw_image,masks,background_image,blur_radius) # Draw bounding boxes and labels font=self.getFont() draw = ImageDraw.Draw(outpt) for box, label, seg_id in zip(results[0].boxes.xyxy.cpu().numpy(), results[0].boxes.cls.cpu().numpy(), range(len(results[0].boxes))): # segment_id for each box label_name = class_names[int(label)] # Assign a unique name for each detected object based on its segment current_label = self.assign_segment_name(label_name, seg_id) x1, y1, x2, y2 = map(int, box) draw.rectangle([x1, y1, x2, y2], outline="red", width=2) draw.text((x1, y1), current_label+" " + str(seg_id), fill="black", font=font) label_counter.append(current_label) return outpt.resize(org_size), label_counter def Back_step2(self,raw_image:Image,background_image:Image,things_replace:list,blur_radius=23): org_size = raw_image.size raw_image = raw_image.resize((640, 480)) print(type(background_image)) if type(background_image)==PIL.JpegImagePlugin.JpegImageFile: background_image = background_image.resize((640, 480)) results, class_names = self.predict_image(raw_image) masks=[] for segm, label,seg_id in zip(results[0].masks.data,results[0].boxes.cls.cpu().numpy(),range(len(results[0].boxes))): label_name = class_names[int(label)] current_label = self.assign_segment_name(label_name, seg_id) if current_label in things_replace: masks.append(segm.cpu().numpy()) masked_image=self.putMaskImage(raw_image,masks,background_image,blur_radius) return masked_image.resize(org_size) def get_labels(self,kind_back): list_output=[] if ('person' in kind_back): list_output=list_output + self.person if ('animal' in kind_back): list_output=list_output + self.animal if ('drive' in kind_back): list_output=list_output + self.drive return list_output def Back_video(self,video_path,output_path,background_image,kind_back,blur_radius=35):#background_image,what_remove,blur_radius=23): # back_image and video? #what_remove if many person it is not identify so same cap = cv2.VideoCapture(video_path) # Get video properties frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) fps = int(cap.get(cv2.CAP_PROP_FPS)) #number_frames= int(cv2.CAP_PROP_FRAME_COUNT) # Define the codec and create VideoWriter object to save the output video fourcc = cv2.VideoWriter_fourcc(*'XVID') out = cv2.VideoWriter(output_path, fourcc, fps, (frame_width, frame_height)) if isinstance(background_image, Image.Image): background_image = background_image.resize((640, 480)) sound_tmp_file='audio.mp3' if os.path.exists(sound_tmp_file):# to not give error os.remove(sound_tmp_file) subprocess.run(['ffmpeg', '-i', video_path, '-q:a', '0', '-map', 'a',sound_tmp_file]) else: subprocess.run(['ffmpeg', '-i', video_path, '-q:a', '0', '-map', 'a',sound_tmp_file]) i=0 while True: ret, frame = cap.read() if not ret: break # End of video # Convert the current frame to RGB frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) frame_rgb = Image.fromarray(np.array(frame_rgb)) org_size = frame_rgb.size frame_rgb = frame_rgb.resize((640, 480)) results,class_names = self.predict_image(frame_rgb) masks=[] things_replace=self.get_labels(kind_back) for segm, label in zip(results[0].masks.data,results[0].boxes.cls.cpu().numpy()): label_name = class_names[int(label)] if label_name in things_replace: masks.append(segm.cpu().numpy()) masked_image = self.putMaskImage(frame_rgb,masks,background_image,blur_radius) out.write(cv2.cvtColor(np.array(masked_image.resize(org_size)), cv2.COLOR_RGB2BGR)) print(f"Completed frame {i+1} ") i=i+1 #if (i==10): # break print("Finished frames") # adding original sound ,by extracting sound then put in new video cap.release() out.release() cv2.destroyAllWindows() #subprocess.run(['ffmpeg', '-i', output_path, '-i', 'audio.mp3', '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental',"temp_output_video.mp4" ]) os.remove(sound_tmp_file)