Test vj
Gašper Spagnolo
parent
27c77a5928
commit
ec603b85fd
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@ -1,64 +1,77 @@
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import os
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import cv2
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class EarDataClass():
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class EarDataClass:
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def __init__(self, root_dir: str, annot_file: str, mode: str):
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if not os.path.isdir(root_dir):
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raise ValueError('root_dir must be a valid directory')
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raise ValueError("root_dir must be a valid directory")
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if os.path.isfile(os.path.join(root_dir, annot_file)):
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raise ValueError('annot_file must be a valid file')
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if mode not in ['train', 'test']:
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raise ValueError('mode must be either train or test')
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raise ValueError("annot_file must be a valid file")
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if mode not in ["train", "test"]:
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raise ValueError("mode must be either train or test")
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self.root_dir = root_dir
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self.annot_file = annot_file
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self.mode = mode
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self._set_paths()
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self._set_bboxes()
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def _set_paths(self):
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paths = []
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labels = []
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def _convert_path_to_number(path):
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return int(path.split('/')[-1].split('.')[0])
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return int(path.split("/")[-1].split(".")[0])
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with open(self.annot_file, 'r') as f:
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with open(self.annot_file, "r") as f:
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lines = f.readlines()
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for line in lines:
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line = line.split(' ')
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line = line.split(" ")
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path = os.path.join(self.root_dir, line[0])
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p_int = _convert_path_to_number(path)
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if self.mode == 'train':
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if self.mode == "train":
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if p_int % 5 != 0:
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paths.append(path)
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labels.append(line[1])
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elif self.mode == 'test':
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labels.append(int(line[1].strip()))
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elif self.mode == "test":
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if p_int % 5 == 0:
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paths.append(path)
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labels.append(line[1])
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labels.append(int(line[1].strip()))
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self.paths = paths
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self.labels = labels
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def _set_bboxes(self):
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bboxes = []
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for path in self.paths:
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path = path.replace(".png", ".txt")
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with open(path, "r") as f:
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lines = f.read().split(sep=" ")
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bbox = [float(x) for x in lines[1:]]
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bboxes.append(bbox)
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self.bboxes = bboxes
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def __getitem__(self, idx):
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image_path = self.paths[idx]
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label = self.labels[idx]
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bbox = self.bboxes[idx]
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image = cv2.imread(image_path)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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return image, label
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image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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return image, label, bbox
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def __len__(self):
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return len(self.paths)
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def main():
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dat = EarDataClass(root_dir='./ears', annot_file='identites.txt', mode='train')
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dat = EarDataClass(root_dir="./ears", annot_file="identites.txt", mode="test")
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for i in range(len(dat)):
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image, label = dat[i]
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print(image.shape, label)
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image, label, bbox = dat[i]
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print(image.shape, label, bbox)
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pass
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if __name__ == '__main__':
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if __name__ == "__main__":
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main()
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128
a1/pipeline.py
128
a1/pipeline.py
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@ -18,6 +18,14 @@ class ViolaJones:
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self.scale_factor = scale_factor
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self.min_neighbors = min_neighbors
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def _convert_to_yolo_format(self, bbox, img_width, img_height):
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x, y, w, h = bbox
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x_center = (x + w / 2) / img_width
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y_center = (y + h / 2) / img_height
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w_norm = w / img_width
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h_norm = h / img_height
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return [x_center, y_center, w_norm, h_norm]
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def _extract_ear(self, image, ear):
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x, y, w, h = ear
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return image[y : y + h, x : x + w]
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@ -30,20 +38,43 @@ class ViolaJones:
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image, self.scale_factor, self.min_neighbors
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)
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img_height, img_width = image.shape[:2]
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if len(left_ears) > 0:
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# Crop out the region of interest
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return left_ears[0], self._extract_ear(image, left_ears[0])
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return self._convert_to_yolo_format(
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left_ears[0], img_width, img_height
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), self._extract_ear(image, left_ears[0])
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elif len(right_ears) > 0:
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return right_ears[0], self._extract_ear(image, right_ears[0])
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return self._convert_to_yolo_format(
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right_ears[0], img_width, img_height
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), self._extract_ear(image, right_ears[0])
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else:
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return None, None
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def compute_iou(self, bbox1, bbox2):
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x1_min, y1_min, x1_max, y1_max = bbox1
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x2_min, y2_min, x2_max, y2_max = bbox2
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xA = max(x1_min, x2_min)
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yA = max(y1_min, y2_min)
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xB = min(x1_max, x2_max)
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yB = min(y1_max, y2_max)
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interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
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boxAArea = (x1_max - x1_min + 1) * (y1_max - y1_min + 1)
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boxBArea = (x2_max - x2_min + 1) * (y2_max - y2_min + 1)
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unionArea = boxAArea + boxBArea - interArea
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iou = interArea / unionArea if unionArea > 0 else 0
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return iou
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class LBP:
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def __init__(self, P=8, R=1):
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self.P = P
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self.R = R
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pass
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def _bilinear_interpolation(self, image, x, y):
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x1, y1 = int(x), int(y)
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@ -61,41 +92,88 @@ class LBP:
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+ f22 * (x - x1) * (y - y1)
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)
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def _get_lbp_histogram(self, lbp_image):
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"""
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Compute the normalized histogram of the LBP image.
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"""
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histogram, _ = np.histogram(
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lbp_image.ravel(), bins=np.arange(0, 2**self.P, 1), range=(0, 2**self.P)
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)
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histogram = histogram.astype("float")
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histogram /= histogram.sum()
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return histogram
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def forward(self, img):
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# Already grayscale
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try:
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gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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lbp = np.zeros_like(gray_image)
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except:
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raise ValueError("The input image must be a valid BGR image.")
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lbp_image = np.zeros_like(gray_image)
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for i in range(self.R, gray_image.shape[0] - self.R):
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for j in range(self.R, gray_image.shape[1] - self.R):
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center = gray_image[i, j]
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lbp_code = 0
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for p in range(self.P):
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# Angle in radians
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theta = 2 * np.pi * p / self.P
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x = j + self.R * np.cos(theta)
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y = i - self.R * np.sin(
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theta
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) # y coordinates go from top to bottom
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y = i - self.R * np.sin(theta)
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pixel_value = self._bilinear_interpolation(gray_image, x, y)
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lbp_code |= (1 << p) if pixel_value >= center else 0
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lbp[i, j] = lbp_code
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return lbp
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lbp_code |= (pixel_value >= center) << p
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lbp_image[i, j] = lbp_code
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return self._get_lbp_histogram(lbp_image)
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def test_vj(dat: EarDataClass):
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os.makedirs("./pred/vj", exist_ok=True)
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#scale_factors = [
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# 1.05,
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# 1.1,
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# 1.15,
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# 1.2,
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# 1.25,
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# 1.3,
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# 1.35,
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# 1.4,
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# 1.45,
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# 1.5,
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# 1.55,
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# 1.60,
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# 1.65,
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# 1.70,
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# 1.75,
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# 1.80,
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# 1.85,
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# 1.90,
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# 1.95,
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# 2.0,
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#]
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scale_factors = [1.01, 1.02, 1.03, 1.04]
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min_neighbors = [1, 2, 3, 4, 5, 6, 7, 8, 9]
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for scale_factor in scale_factors:
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for min_neigh in min_neighbors:
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vj = ViolaJones(scale_factor=scale_factor, min_neighbors=min_neigh)
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ious = []
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os.makedirs(f"./pred/vj/sf={scale_factor}_mn={min_neigh}", exist_ok=True)
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for i in tqdm(range(len(dat))):
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image, _, bbox = dat[i]
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bbox_vj, _ = vj.forward(image)
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if bbox_vj is not None:
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iou = vj.compute_iou(bbox, bbox_vj)
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ious.append(iou)
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else:
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ious.append(0)
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with open(f"./pred/vj/sf={scale_factor}_mn={min_neigh}/iou.txt", "w") as f:
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f.write(str(np.mean(ious)))
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def main():
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dat = EarDataClass(root_dir="./ears", annot_file="identites.txt", mode="train")
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vj = ViolaJones()
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lbp = LBP()
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for i in tqdm(range(len(dat))):
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img, label = dat[i]
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ear_lab, ear_img = vj.forward(img)
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if ear_lab is not None:
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# Save the image
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print(lbp.forward(ear_img).shape)
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os.makedirs("./pred", exist_ok=True)
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# Save the image
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cv2.imwrite(f"./pred/{i}.jpg", ear_img)
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pass
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test_vj(dat)
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if __name__ == "__main__":
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