diff options
Diffstat (limited to 'check/app/utils/im_utils.py')
| -rw-r--r-- | check/app/utils/im_utils.py | 280 |
1 files changed, 140 insertions, 140 deletions
diff --git a/check/app/utils/im_utils.py b/check/app/utils/im_utils.py index 1d1affb..6b75bc3 100644 --- a/check/app/utils/im_utils.py +++ b/check/app/utils/im_utils.py @@ -1,7 +1,7 @@ import sys import os from os.path import join -import cv2 as cv +# import cv2 as cv import imagehash from PIL import Image, ImageDraw, ImageFilter, ImageOps from skimage.filters.rank import entropy @@ -64,30 +64,30 @@ def compute_features(fe,frames,phashes,phash_thresh=1): return vals -def np2pil(im, swap=True): - """Ensure image is Pillow format - :param im: image in numpy or PIL.Image format - :returns: image in Pillow RGB format - """ - try: - im.verify() - return im - except: - if swap: - im = cv.cvtColor(im,cv.COLOR_BGR2RGB) - return Image.fromarray(im.astype('uint8'), 'RGB') +# def np2pil(im, swap=True): +# """Ensure image is Pillow format +# :param im: image in numpy or PIL.Image format +# :returns: image in Pillow RGB format +# """ +# try: +# im.verify() +# return im +# except: +# if swap: +# im = cv.cvtColor(im,cv.COLOR_BGR2RGB) +# return Image.fromarray(im.astype('uint8'), 'RGB') -def pil2np(im, swap=True): - """Ensure image is Numpy.ndarry format - :param im: image in numpy or PIL.Image format - :returns: image in Numpy uint8 format - """ - if type(im) == np.ndarray: - return im - im = np.asarray(im, np.uint8) - if swap: - im = cv.cvtColor(im, cv.COLOR_RGB2BGR) - return im +# def pil2np(im, swap=True): +# """Ensure image is Numpy.ndarry format +# :param im: image in numpy or PIL.Image format +# :returns: image in Numpy uint8 format +# """ +# if type(im) == np.ndarray: +# return im +# im = np.asarray(im, np.uint8) +# if swap: +# im = cv.cvtColor(im, cv.COLOR_RGB2BGR) +# return im def resize(im, width=0, height=0): @@ -150,39 +150,39 @@ def filter_pixellate(im,num_cells): # Utilities for analyzing frames -def compute_gray(im): - im = cv.cvtColor(im,cv.COLOR_BGR2GRAY) - n_vals = float(im.shape[0] * im.shape[1]) - avg = np.sum(im[:]) / n_vals - return avg +# def compute_gray(im): +# im = cv.cvtColor(im,cv.COLOR_BGR2GRAY) +# n_vals = float(im.shape[0] * im.shape[1]) +# avg = np.sum(im[:]) / n_vals +# return avg -def compute_rgb(im): - im = cv.cvtColor(im,cv.COLOR_BGR2RGB) - n_vals = float(im.shape[0] * im.shape[1]) - avg_r = np.sum(im[:,:,0]) / n_vals - avg_g = np.sum(im[:,:,1]) / n_vals - avg_b = np.sum(im[:,:,2]) / n_vals - avg_rgb = np.sum(im[:,:,:]) / (n_vals * 3.0) - return avg_r, avg_b, avg_g, avg_rgb +# def compute_rgb(im): +# im = cv.cvtColor(im,cv.COLOR_BGR2RGB) +# n_vals = float(im.shape[0] * im.shape[1]) +# avg_r = np.sum(im[:,:,0]) / n_vals +# avg_g = np.sum(im[:,:,1]) / n_vals +# avg_b = np.sum(im[:,:,2]) / n_vals +# avg_rgb = np.sum(im[:,:,:]) / (n_vals * 3.0) +# return avg_r, avg_b, avg_g, avg_rgb -def compute_hsv(im): - im = cv.cvtColor(im,cv.COLOR_BGR2HSV) - n_vals = float(im.shape[0] * im.shape[1]) - avg_h = np.sum(frame[:,:,0]) / n_vals - avg_s = np.sum(frame[:,:,1]) / n_vals - avg_v = np.sum(frame[:,:,2]) / n_vals - avg_hsv = np.sum(frame[:,:,:]) / (n_vals * 3.0) - return avg_h, avg_s, avg_v, avg_hsv +# def compute_hsv(im): +# im = cv.cvtColor(im,cv.COLOR_BGR2HSV) +# n_vals = float(im.shape[0] * im.shape[1]) +# avg_h = np.sum(frame[:,:,0]) / n_vals +# avg_s = np.sum(frame[:,:,1]) / n_vals +# avg_v = np.sum(frame[:,:,2]) / n_vals +# avg_hsv = np.sum(frame[:,:,:]) / (n_vals * 3.0) +# return avg_h, avg_s, avg_v, avg_hsv -def pys_dhash(im, hashSize=8): - # resize the input image, adding a single column (width) so we - # can compute the horizontal gradient - resized = cv.resize(im, (hashSize + 1, hashSize)) - # compute the (relative) horizontal gradient between adjacent - # column pixels - diff = resized[:, 1:] > resized[:, :-1] - # convert the difference image to a hash - return sum([2 ** i for (i, v) in enumerate(diff.flatten()) if v]) +# def pys_dhash(im, hashSize=8): +# # resize the input image, adding a single column (width) so we +# # can compute the horizontal gradient +# resized = cv.resize(im, (hashSize + 1, hashSize)) +# # compute the (relative) horizontal gradient between adjacent +# # column pixels +# diff = resized[:, 1:] > resized[:, :-1] +# # convert the difference image to a hash +# return sum([2 ** i for (i, v) in enumerate(diff.flatten()) if v]) ############################################ @@ -283,62 +283,62 @@ def compute_entropy(im): # OpenCV ############################################ -def bgr2gray(im): - """Wrapper for cv2.cvtColor transform - :param im: Numpy.ndarray (BGR) - :returns: Numpy.ndarray (Gray) - """ - return cv.cvtColor(im,cv.COLOR_BGR2GRAY) +# def bgr2gray(im): +# """Wrapper for cv2.cvtColor transform +# :param im: Numpy.ndarray (BGR) +# :returns: Numpy.ndarray (Gray) +# """ +# return cv.cvtColor(im,cv.COLOR_BGR2GRAY) -def gray2bgr(im): - """Wrapper for cv2.cvtColor transform - :param im: Numpy.ndarray (Gray) - :returns: Numpy.ndarray (BGR) - """ - return cv.cvtColor(im,cv.COLOR_GRAY2BGR) +# def gray2bgr(im): +# """Wrapper for cv2.cvtColor transform +# :param im: Numpy.ndarray (Gray) +# :returns: Numpy.ndarray (BGR) +# """ +# return cv.cvtColor(im,cv.COLOR_GRAY2BGR) -def bgr2rgb(im): - """Wrapper for cv2.cvtColor transform - :param im: Numpy.ndarray (BGR) - :returns: Numpy.ndarray (RGB) - """ - return cv.cvtColor(im,cv.COLOR_BGR2RGB) +# def bgr2rgb(im): +# """Wrapper for cv2.cvtColor transform +# :param im: Numpy.ndarray (BGR) +# :returns: Numpy.ndarray (RGB) +# """ +# return cv.cvtColor(im,cv.COLOR_BGR2RGB) -def compute_laplacian(im): - # below 100 is usually blurry - return cv.Laplacian(im, cv.CV_64F).var() +# def compute_laplacian(im): +# # below 100 is usually blurry +# return cv.Laplacian(im, cv.CV_64F).var() -# http://radjkarl.github.io/imgProcessor/index.html# +# # http://radjkarl.github.io/imgProcessor/index.html# -def modifiedLaplacian(img): - ''''LAPM' algorithm (Nayar89)''' - M = np.array([-1, 2, -1]) - G = cv.getGaussianKernel(ksize=3, sigma=-1) - Lx = cv.sepFilter2D(src=img, ddepth=cv.CV_64F, kernelX=M, kernelY=G) - Ly = cv.sepFilter2D(src=img, ddepth=cv.CV_64F, kernelX=G, kernelY=M) - FM = np.abs(Lx) + np.abs(Ly) - return cv.mean(FM)[0] +# def modifiedLaplacian(img): +# ''''LAPM' algorithm (Nayar89)''' +# M = np.array([-1, 2, -1]) +# G = cv.getGaussianKernel(ksize=3, sigma=-1) +# Lx = cv.sepFilter2D(src=img, ddepth=cv.CV_64F, kernelX=M, kernelY=G) +# Ly = cv.sepFilter2D(src=img, ddepth=cv.CV_64F, kernelX=G, kernelY=M) +# FM = np.abs(Lx) + np.abs(Ly) +# return cv.mean(FM)[0] -def varianceOfLaplacian(img): - ''''LAPV' algorithm (Pech2000)''' - lap = cv.Laplacian(img, ddepth=-1)#cv.cv.CV_64F) - stdev = cv.meanStdDev(lap)[1] - s = stdev[0]**2 - return s[0] +# def varianceOfLaplacian(img): +# ''''LAPV' algorithm (Pech2000)''' +# lap = cv.Laplacian(img, ddepth=-1)#cv.cv.CV_64F) +# stdev = cv.meanStdDev(lap)[1] +# s = stdev[0]**2 +# return s[0] -def tenengrad(img, ksize=3): - ''''TENG' algorithm (Krotkov86)''' - Gx = cv.Sobel(img, ddepth=cv.CV_64F, dx=1, dy=0, ksize=ksize) - Gy = cv.Sobel(img, ddepth=cv.CV_64F, dx=0, dy=1, ksize=ksize) - FM = Gx**2 + Gy**2 - return cv.mean(FM)[0] +# def tenengrad(img, ksize=3): +# ''''TENG' algorithm (Krotkov86)''' +# Gx = cv.Sobel(img, ddepth=cv.CV_64F, dx=1, dy=0, ksize=ksize) +# Gy = cv.Sobel(img, ddepth=cv.CV_64F, dx=0, dy=1, ksize=ksize) +# FM = Gx**2 + Gy**2 +# return cv.mean(FM)[0] -def normalizedGraylevelVariance(img): - ''''GLVN' algorithm (Santos97)''' - mean, stdev = cv.meanStdDev(img) - s = stdev[0]**2 / mean[0] - return s[0] +# def normalizedGraylevelVariance(img): +# ''''GLVN' algorithm (Santos97)''' +# mean, stdev = cv.meanStdDev(img) +# s = stdev[0]**2 / mean[0] +# return s[0] def compute_if_blank(im,width=100,sigma=0,thresh_canny=.1,thresh_mean=4,mask=None): # im is graysacale np @@ -363,48 +363,48 @@ def print_timing(t,n): print('Elapsed time: {:.2f}'.format(t)) print('FPS: {:.2f}'.format(n/t)) -def vid2frames(fpath, limit=5000, width=None, idxs=None): - """Convert a video file into list of frames - :param fpath: filepath to the video file - :param limit: maximum number of frames to read - :param fpath: the indices of frames to keep (rest are skipped) - :returns: (fps, number of frames, list of Numpy.ndarray frames) - """ - frames = [] - try: - cap = cv.VideoCapture(fpath) - except: - print('[-] Error. Could not read video file: {}'.format(fpath)) - try: - cap.release() - except: - pass - return frames +# def vid2frames(fpath, limit=5000, width=None, idxs=None): +# """Convert a video file into list of frames +# :param fpath: filepath to the video file +# :param limit: maximum number of frames to read +# :param fpath: the indices of frames to keep (rest are skipped) +# :returns: (fps, number of frames, list of Numpy.ndarray frames) +# """ +# frames = [] +# try: +# cap = cv.VideoCapture(fpath) +# except: +# print('[-] Error. Could not read video file: {}'.format(fpath)) +# try: +# cap.release() +# except: +# pass +# return frames - fps = cap.get(cv.CAP_PROP_FPS) - nframes = int(cap.get(cv.CAP_PROP_FRAME_COUNT)) +# fps = cap.get(cv.CAP_PROP_FPS) +# nframes = int(cap.get(cv.CAP_PROP_FRAME_COUNT)) - if idxs is not None: - # read sample indices by seeking to frame index - for idx in idxs: - cap.set(cv.CAP_PROP_POS_FRAMES, idx) - res, frame = cap.read() - if width is not None: - frame = imutils.resize(frame, width=width) - frames.append(frame) - else: - while(True and len(frames) < limit): - res, frame = cap.read() - if not res: - break - if width is not None: - frame = imutils.resize(frame, width=width) - frames.append(frame) +# if idxs is not None: +# # read sample indices by seeking to frame index +# for idx in idxs: +# cap.set(cv.CAP_PROP_POS_FRAMES, idx) +# res, frame = cap.read() +# if width is not None: +# frame = imutils.resize(frame, width=width) +# frames.append(frame) +# else: +# while(True and len(frames) < limit): +# res, frame = cap.read() +# if not res: +# break +# if width is not None: +# frame = imutils.resize(frame, width=width) +# frames.append(frame) - cap.release() - del cap - #return fps,nframes,frames - return frames +# cap.release() +# del cap +# #return fps,nframes,frames +# return frames def convolve_filter(vals,filters=[1]): for k in filters: |