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LapStore commited on 1 day ago

Commit

ceb119d

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1 Parent(s): b1f46e5

try space

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Files changed (4) hide show

app.py +3 -10
index.html +14 -0
requirements.txt +4 -0
server.py +224 -0

app.py CHANGED Viewed

@@ -8,23 +8,16 @@ from PIL import ImageOps,Image ,ImageFilter
 import matplotlib.pyplot as plt
 import numpy as np
 import ast
 #http://localhost:8000
 app = FastAPI()
 # Root route
 @app.get('/')
-def hello_world():
     return "Hello World taha"
-def get_segment_image(raw_image):
-    #pipe = pipeline("image-segmentation", model="Intel/dpt-large-ade")
-    #output = pipe(raw_image, points_per_batch=32)
-    #return output
-    pass
-def get_supported_segmentation(output,supported_types):
-    return [obj for obj in output  if (obj['label'] in supported_types)]
 @app.post('/predict')
 async  def predict(supported_types_str: str = Form(),age: str = Form() , file: UploadFile = File(...)):

 import matplotlib.pyplot as plt
 import numpy as np
 import ast
+import server
 #http://localhost:8000
 app = FastAPI()
 # Root route
 @app.get('/')
+def main():
     return "Hello World taha"
 @app.post('/predict')
 async  def predict(supported_types_str: str = Form(),age: str = Form() , file: UploadFile = File(...)):

index.html ADDED Viewed

	@@ -0,0 +1,14 @@

+<html>
+   <body bgcolor="#00cccc">
+      <center>
+         <br><br><br>
+         <form action="https://taha454-backg.hf.space/predict" method="post"  enctype="multipart/form-data">
+            <p><h3>Enter Image:</h3></p>
+            Name :<p><input type="text" name="supported_types_str" /></p><br>
+            Age :<p><input type="text" name="age" /></p><br>
+            File : <input type="file" name="file" required><br><br>
+            <p><input type="submit" value="submit" /></p>
+         </form>
+      </center>
+   </body>
+</html>

requirements.txt CHANGED Viewed

@@ -5,3 +5,7 @@ transformers
 matplotlib
 numpy
 python-multipart

 matplotlib
 numpy
 python-multipart
+PIL
+ultralytics
+cv2

server.py ADDED Viewed

	@@ -0,0 +1,224 @@

+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)