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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_step1(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)
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