anais la bg

anaislpb.py

@@ -0,0 +1,78 @@
+import cv2
+import numpy as np
+
+def image_for_mathias(name="1.jpg"):
+    # Load and resize
+    image = cv2.imread(name)
+    height, width = image.shape[:2]
+    image = cv2.resize(image, (int(width * 0.2), int(height * 0.2)))
+
+    # Grayscale + blur
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
+
+    # Threshold to isolate object
+    _, thresh = cv2.threshold(blurred, 50, 255, cv2.THRESH_BINARY_INV)
+
+    # Find contours
+    contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    c = max(contours, key=cv2.contourArea)
+
+    # --- Step 1: Create mask ---
+    mask = np.zeros_like(gray)
+    cv2.drawContours(mask, [c], -1, 255, -1)
+
+    # Add alpha channel
+    b, g, r = cv2.split(image)
+    rgba = cv2.merge([b, g, r, mask])
+
+    # --- Step 2: Crop tightly with padding ---
+    x, y, w, h = cv2.boundingRect(c)
+    padding = 10
+    x, y = max(0, x-padding), max(0, y-padding)
+    w, h = min(rgba.shape[1]-x, w+2*padding), min(rgba.shape[0]-y, h+2*padding)
+    cropped = rgba[y:y+h, x:x+w]
+
+    # --- Step 3: Perspective transform ---
+    rect = cv2.minAreaRect(c)
+    box = cv2.boxPoints(rect)   # 4 corner points
+    box = np.array(box, dtype="float32")
+
+    # Order the points
+    def order_points(pts):
+        rect = np.zeros((4, 2), dtype="float32")
+        s = pts.sum(axis=1)
+        rect[0] = pts[np.argmin(s)]  # top-left
+        rect[2] = pts[np.argmax(s)]  # bottom-right
+        diff = np.diff(pts, axis=1)
+        rect[1] = pts[np.argmin(diff)]  # top-right
+        rect[3] = pts[np.argmax(diff)]  # bottom-left
+        return rect
+
+    src_pts = order_points(box)
+
+    # Desired output size (tune for your code dimensions)
+    W, H = 600, 200
+    dst_pts = np.array([
+        [0, 0],
+        [W-1, 0],
+        [W-1, H-1],
+        [0, H-1]
+    ], dtype="float32")
+
+    # Warp with perspective correction
+    M = cv2.getPerspectiveTransform(src_pts, dst_pts)
+    warped = cv2.warpPerspective(rgba, M, (W, H), borderMode=cv2.BORDER_CONSTANT, borderValue=(0,0,0,0))
+
+    # Save transparent deskewed image
+    cv2.imwrite("spotify_code_deskewed.jpg", warped)
+
+    # Show result
+    cv2.imshow("Spotify Code Deskewed", warped)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+l = ["1.jpg", "2.jpg", "3.jpg", "4.jpg"]
+for name in l:
+    image_for_mathias(name)