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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+hat.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,16 @@
+
+from src.prototype import activate
+import gradio 
+
+
+
+iface = gradio.Interface(
+fn= activate, 
+inputs=gradio.components.Image(type="numpy"), 
+outputs=gradio.components.Image(type="numpy"),
+title="Hatting Face",
+description="Upload the picture of your choice :)"
+)
+iface.launch()
+
+

prototype.py

@@ -0,0 +1,114 @@
+import cv2
+import numpy as np
+import os
+import mediapipe
+
+from src.utils import extract_point, compute_distance
+
+
+
+class ImageProcessor:
+    def __init__(self, picture, folder_path , model):
+        self.image = picture
+        self.height , self.width = self.image.shape[:2]
+        self.folder_path = folder_path
+        self.model = model  
+
+    def detect_and_overlay(self, write = False, output = None):
+
+        detections = self.model.process(self.image).detections
+
+        if not detections: 
+            self.image = cv2.putText(self.image, "Unable to detect faces :(", (int(self.width//10), int(self.height//2)), fontFace = cv2.FONT_HERSHEY_SIMPLEX, fontScale = 3, color = (0,0,0),thickness =  7)
+            self.image = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+            return self.image
+
+        for i, elem in enumerate(detections):
+            print(i)
+            x, y, w, h = self.get_bounding_box(elem)
+            gadget_path, nose_path = self.select_gadgets(i)
+
+
+            if nose_path:
+              nose = extract_point(self, elem.location_data.relative_keypoints[2])
+              eye = extract_point(self, elem.location_data.relative_keypoints[0]) 
+              cv2.circle(self.image,  (int(nose[0]), int(nose[1])), int(compute_distance(nose, eye)/2), (255,0,0), -1)
+
+             
+            try:
+                roi_x1, roi_y1, roi_x2, roi_y2 = self.calculate_roi_head(x, y, w, h)
+                gadget = self.read_and_resize_gadget(gadget_path, roi_x2 - roi_x1, roi_y2 - roi_y1)
+                self.overlay_gadget(gadget, roi_x1, roi_y1, roi_x2, roi_y2)
+            except: 
+                continue
+
+        # self.image =  cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
+
+        if write: 
+            self.display_result(output)
+
+        return self.image
+        
+        
+
+    def get_bounding_box(self, elem):
+        bbox = elem.location_data.relative_bounding_box
+        x = int(bbox.xmin * self.width)
+        y = int(bbox.ymin * self.height)
+        w = int(bbox.width * self.width)
+        h = int(bbox.height * self.height)
+        return x, y, w, h
+
+    def calculate_roi_head(self, x, y, w, h):
+        roi_height = 60
+        roi_width = int(w * 2)
+        roi_x1 = int(x + (w - roi_width) // 2)
+        vertical_offset = 20
+        roi_y1 = int(max(y - roi_height // 2 - vertical_offset, 0))
+        roi_y2 = roi_y1 + roi_height
+        roi_x2 = roi_x1 + roi_width
+
+        return roi_x1, roi_y1, roi_x2, roi_y2
+
+
+    def select_gadgets(self, index):
+        if index == 0:
+            gadget =  "anklers.png"
+            nose = True
+
+        else:
+            gadget = "hat.png"
+            nose = False
+
+        return gadget, nose
+
+    def read_and_resize_gadget(self, gadget_path, width, height):
+        gadget = cv2.imread(gadget_path, cv2.IMREAD_UNCHANGED)
+        gadget_resized = cv2.resize(gadget, (width, height))
+        return gadget_resized
+
+    def overlay_gadget(self, gadget, x1, y1, x2, y2):
+        alpha_gadget = gadget[:, :, 3] / 255.0
+        alpha_gadget_resized = np.stack([alpha_gadget] * 3, axis=-1)
+        gadget_bgr = gadget[:, :, :3]
+        gadget_bgr = cv2.cvtColor(gadget_bgr, cv2.COLOR_BGR2RGB)
+
+        roi = self.image[y1:y2, x1:x2]
+
+        roi = cv2.resize(roi, (gadget.shape[1], gadget.shape[0]))
+
+        overlay = (1 - alpha_gadget_resized) * roi + alpha_gadget_resized * gadget_bgr
+        self.image[y1:y2, x1:x2] = overlay
+
+    def display_result(self, output):
+        if not output:
+            output = "image"
+        cv2.imwrite( os.path.join("results", "{}.png".format(output)), self.image )
+
+
+
+def activate(image):
+    folder_path = 'gadgets/'
+    model = mediapipe.solutions.face_detection.FaceDetection(model_selection=1, min_detection_confidence=0.8)
+    processor = ImageProcessor(image, folder_path, model)
+    return processor.detect_and_overlay()

utils.py

@@ -0,0 +1,8 @@
+import math
+
+def extract_point(obj, pt):
+  return obj.width*pt.x,obj.height* pt.y
+  
+def compute_distance(pt1, pt2):
+
+  return math.sqrt((pt1[0]- pt2[0])**2 + (pt1[1]- pt2[1])**2)