From 2f133a452848c045e796eff9b6bcff35cd1f35f3 Mon Sep 17 00:00:00 2001
From: Guillaume Vergnolle <guillaume.vergnolle@gmail.com>
Date: Wed, 26 Feb 2020 20:01:58 -0500
Subject: [PATCH] superposition solved. scale and position randomized
---
robots-at-runes/research/constants.py | 2 +
robots-at-runes/research/superposition.py | 59 +++++++++++++++--------
2 files changed, 41 insertions(+), 20 deletions(-)
diff --git a/robots-at-runes/research/constants.py b/robots-at-runes/research/constants.py
index e69de29..e670e58 100644
--- a/robots-at-runes/research/constants.py
+++ b/robots-at-runes/research/constants.py
@@ -0,0 +1,2 @@
+FACT_RESIZE = 1 # Facteur de redimensionnement. La taille de l'image sera
+ # divisée ou multipliée par ce chiffre au maximum
\ No newline at end of file
diff --git a/robots-at-runes/research/superposition.py b/robots-at-runes/research/superposition.py
index 7cd5e00..949fd4a 100644
--- a/robots-at-runes/research/superposition.py
+++ b/robots-at-runes/research/superposition.py
@@ -2,26 +2,47 @@ import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import cv2
+import random as rd
+import constants as cst
-threshold = 100
+percent_scale = np.round((2*rd.random())-1, 3)
-
-def superimpose(img1, img2, mask) :
+def superimpose(img1, img2, mask):
"""
Superimpose two pictures based on a mask. The two pictures
must have the same shape.
:param img1: first picture
:param img2: second picture
- :param mask: mask applied on both pictures.
- 'True' values will be applied on first picture
+ :param mask: mask applied on both pictures. Values between 0 and 255
:return: Composition
"""
dst_shape = img1.shape
- composition = np.\
- zeros(dst_shape, dtype=np.uint8)
- for i in range(dst_shape[2]) :
- composition[:,:,i] = mask * img1[:,:,i] + ~mask * img2[:,:,i]
- return composition
+ img1f = img1.astype(np.float)
+ img2f = img2.astype(np.float)
+ mix = np.zeros(dst_shape, dtype=np.uint8)
+ for i in range(dst_shape[2]):
+ mix[:, :, i] = ((~mask * img1f[:, :, i] + mask * img2f[:, :, i]) / 255).astype(np.uint8)
+ return mix
+
+
+def get_subset_shapes(img_extract, hs, ws):
+ he, we, _ = img_extract.shape
+ delta_h = he - hs
+ delta_w = we - ws
+ h_start = int(rd.random() * delta_h)
+ w_start = int(rd.random() * delta_w)
+ return img_extract[h_start:h_start + hs, w_start:w_start + ws, :], h_start, w_start
+
+
+def reshape_percentage(img_base, percent):
+ intensity = 1 + abs(percent) * cst.FACT_RESIZE
+ h, w, _ = img_base.shape
+ if percent < 0:
+ h_dest, w_dest = int(h / intensity), int(w / intensity)
+ else:
+ h_dest, w_dest = h * int(intensity), w * int(intensity)
+
+ return cv2.resize(img_base, (w_dest, h_dest), interpolation=cv2.INTER_AREA)
background = cv2.imread("images_sup/back1.jpg")
@@ -29,22 +50,20 @@ background = cv2.cvtColor(background, cv2.COLOR_BGR2RGB)
logo = cv2.imread("images_sup/logo.png", cv2.IMREAD_UNCHANGED)
logo = cv2.cvtColor(logo, cv2.COLOR_BGRA2RGBA)
-
-h,w,_ = background.shape
-logo = cv2.resize(logo, (w,h), interpolation = cv2.INTER_AREA)
-
-mask_alpha = (logo[:,:,3] < threshold)
-
+logo = reshape_percentage(logo, percent_scale)
+mask_alpha = logo[:, :, 3]
logo = cv2.cvtColor(logo, cv2.COLOR_RGBA2RGB)
+hs, ws, _ = logo.shape
-# logo = cv2.imread("images_sup/logo.png")
-# logo = cv2.cvtColor(logo, cv2.COLOR_BGR2RGB)
+background_subset, h_start, w_start = get_subset_shapes(background, hs, ws)
+composition_subset = superimpose(background_subset, logo, mask_alpha)
-composition = superimpose(background, logo, mask_alpha)
+composition = background.copy()
+composition[h_start:h_start + hs, w_start:w_start + ws, :] = composition_subset
plt.imshow(background)
plt.show()
plt.imshow(logo)
plt.show()
plt.imshow(composition)
-plt.show()
\ No newline at end of file
+plt.show()
--
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