import cv2 import os import pathlib import numpy as np import random from PIL import Image, ImageChops, ImageOps, ImageEnhance from .video_audio_utilities import vid2frames, get_quick_vid_info, get_frame_name, get_next_frame from .human_masking import video2humanmasks def delete_all_imgs_in_folder(folder_path): files = list(pathlib.Path(folder_path).glob('*.jpg')) files.extend(list(pathlib.Path(folder_path).glob('*.png'))) for f in files: os.remove(f) def hybrid_generation(args, anim_args, root): video_in_frame_path = os.path.join(args.outdir, 'inputframes') hybrid_frame_path = os.path.join(args.outdir, 'hybridframes') human_masks_path = os.path.join(args.outdir, 'human_masks') if anim_args.hybrid_generate_inputframes: # create folders for the video input frames and optional hybrid frames to live in os.makedirs(video_in_frame_path, exist_ok=True) os.makedirs(hybrid_frame_path, exist_ok=True) # delete frames if overwrite = true if anim_args.overwrite_extracted_frames: delete_all_imgs_in_folder(hybrid_frame_path) # save the video frames from input video print(f"Video to extract: {anim_args.video_init_path}") print(f"Extracting video (1 every {anim_args.extract_nth_frame}) frames to {video_in_frame_path}...") video_fps = vid2frames(video_path=anim_args.video_init_path, video_in_frame_path=video_in_frame_path, n=anim_args.extract_nth_frame, overwrite=anim_args.overwrite_extracted_frames, extract_from_frame=anim_args.extract_from_frame, extract_to_frame=anim_args.extract_to_frame) # extract alpha masks of humans from the extracted input video imgs if anim_args.hybrid_generate_human_masks != "None": # create a folder for the human masks imgs to live in print(f"Checking /creating a folder for the human masks") os.makedirs(human_masks_path, exist_ok=True) # delete frames if overwrite = true if anim_args.overwrite_extracted_frames: delete_all_imgs_in_folder(human_masks_path) # in case that generate_input_frames isn't selected, we won't get the video fps rate as vid2frames isn't called, So we'll check the video fps in here instead if not anim_args.hybrid_generate_inputframes: _, video_fps, _ = get_quick_vid_info(anim_args.video_init_path) # calculate the correct fps of the masked video according to the original video fps and 'extract_nth_frame' output_fps = video_fps/anim_args.extract_nth_frame # generate the actual alpha masks from the input imgs print(f"Extracting alpha humans masks from the input frames") video2humanmasks(video_in_frame_path, human_masks_path, anim_args.hybrid_generate_human_masks, output_fps) # determine max frames from length of input frames anim_args.max_frames = len([f for f in pathlib.Path(video_in_frame_path).glob('*.jpg')]) print(f"Using {anim_args.max_frames} input frames from {video_in_frame_path}...") # get sorted list of inputfiles inputfiles = sorted(pathlib.Path(video_in_frame_path).glob('*.jpg')) # use first frame as init if anim_args.hybrid_use_first_frame_as_init_image: for f in inputfiles: args.init_image = str(f) args.use_init = True print(f"Using init_image from video: {args.init_image}") break return args, anim_args, inputfiles def hybrid_composite(args, anim_args, frame_idx, prev_img, depth_model, hybrid_comp_schedules, root): video_frame = os.path.join(args.outdir, 'inputframes', get_frame_name(anim_args.video_init_path) + f"{frame_idx:05}.jpg") video_depth_frame = os.path.join(args.outdir, 'hybridframes', get_frame_name(anim_args.video_init_path) + f"_vid_depth{frame_idx:05}.jpg") depth_frame = os.path.join(args.outdir, f"{args.timestring}_depth_{frame_idx-1:05}.png") mask_frame = os.path.join(args.outdir, 'hybridframes', get_frame_name(anim_args.video_init_path) + f"_mask{frame_idx:05}.jpg") comp_frame = os.path.join(args.outdir, 'hybridframes', get_frame_name(anim_args.video_init_path) + f"_comp{frame_idx:05}.jpg") prev_frame = os.path.join(args.outdir, 'hybridframes', get_frame_name(anim_args.video_init_path) + f"_prev{frame_idx:05}.jpg") prev_img = cv2.cvtColor(prev_img, cv2.COLOR_BGR2RGB) prev_img_hybrid = Image.fromarray(prev_img) video_image = Image.open(video_frame) video_image = video_image.resize((args.W, args.H), Image.Resampling.LANCZOS) hybrid_mask = None # composite mask types if anim_args.hybrid_comp_mask_type == 'Depth': # get depth from last generation hybrid_mask = Image.open(depth_frame) elif anim_args.hybrid_comp_mask_type == 'Video Depth': # get video depth video_depth = depth_model.predict(np.array(video_image), anim_args, root.half_precision) depth_model.save(video_depth_frame, video_depth) hybrid_mask = Image.open(video_depth_frame) elif anim_args.hybrid_comp_mask_type == 'Blend': # create blend mask image hybrid_mask = Image.blend(ImageOps.grayscale(prev_img_hybrid), ImageOps.grayscale(video_image), hybrid_comp_schedules['mask_blend_alpha']) elif anim_args.hybrid_comp_mask_type == 'Difference': # create difference mask image hybrid_mask = ImageChops.difference(ImageOps.grayscale(prev_img_hybrid), ImageOps.grayscale(video_image)) # optionally invert mask, if mask type is defined if anim_args.hybrid_comp_mask_inverse and anim_args.hybrid_comp_mask_type != "None": hybrid_mask = ImageOps.invert(hybrid_mask) # if a mask type is selected, make composition if hybrid_mask == None: hybrid_comp = video_image else: # ensure grayscale hybrid_mask = ImageOps.grayscale(hybrid_mask) # equalization before if anim_args.hybrid_comp_mask_equalize in ['Before', 'Both']: hybrid_mask = ImageOps.equalize(hybrid_mask) # contrast hybrid_mask = ImageEnhance.Contrast(hybrid_mask).enhance(hybrid_comp_schedules['mask_contrast']) # auto contrast with cutoffs lo/hi if anim_args.hybrid_comp_mask_auto_contrast: hybrid_mask = autocontrast_grayscale(np.array(hybrid_mask), hybrid_comp_schedules['mask_auto_contrast_cutoff_low'], hybrid_comp_schedules['mask_auto_contrast_cutoff_high']) hybrid_mask = Image.fromarray(hybrid_mask) hybrid_mask = ImageOps.grayscale(hybrid_mask) if anim_args.hybrid_comp_save_extra_frames: hybrid_mask.save(mask_frame) # equalization after if anim_args.hybrid_comp_mask_equalize in ['After', 'Both']: hybrid_mask = ImageOps.equalize(hybrid_mask) # do compositing and save hybrid_comp = Image.composite(prev_img_hybrid, video_image, hybrid_mask) if anim_args.hybrid_comp_save_extra_frames: hybrid_comp.save(comp_frame) # final blend of composite with prev_img, or just a blend if no composite is selected hybrid_blend = Image.blend(prev_img_hybrid, hybrid_comp, hybrid_comp_schedules['alpha']) if anim_args.hybrid_comp_save_extra_frames: hybrid_blend.save(prev_frame) prev_img = cv2.cvtColor(np.array(hybrid_blend), cv2.COLOR_RGB2BGR) # restore to np array and return return args, prev_img def get_matrix_for_hybrid_motion(frame_idx, dimensions, inputfiles, hybrid_motion): img1 = cv2.cvtColor(get_resized_image_from_filename(str(inputfiles[frame_idx-1]), dimensions), cv2.COLOR_BGR2GRAY) img2 = cv2.cvtColor(get_resized_image_from_filename(str(inputfiles[frame_idx]), dimensions), cv2.COLOR_BGR2GRAY) matrix = get_transformation_matrix_from_images(img1, img2, hybrid_motion) print(f"Calculating {hybrid_motion} RANSAC matrix for frames {frame_idx} to {frame_idx+1}") return matrix def get_matrix_for_hybrid_motion_prev(frame_idx, dimensions, inputfiles, prev_img, hybrid_motion): # first handle invalid images from cadence by returning default matrix height, width = prev_img.shape[:2] if height == 0 or width == 0 or prev_img != np.uint8: return get_hybrid_motion_default_matrix(hybrid_motion) else: prev_img_gray = cv2.cvtColor(prev_img, cv2.COLOR_BGR2GRAY) img = cv2.cvtColor(get_resized_image_from_filename(str(inputfiles[frame_idx]), dimensions), cv2.COLOR_BGR2GRAY) matrix = get_transformation_matrix_from_images(prev_img_gray, img, hybrid_motion) print(f"Calculating {hybrid_motion} RANSAC matrix for frames {frame_idx} to {frame_idx+1}") return matrix def get_flow_for_hybrid_motion(frame_idx, dimensions, inputfiles, hybrid_frame_path, method, do_flow_visualization=False): print(f"Calculating {method} optical flow for frames {frame_idx} to {frame_idx+1}") i1 = get_resized_image_from_filename(str(inputfiles[frame_idx]), dimensions) i2 = get_resized_image_from_filename(str(inputfiles[frame_idx+1]), dimensions) flow = get_flow_from_images(i1, i2, method) if do_flow_visualization: save_flow_visualization(frame_idx, dimensions, flow, inputfiles, hybrid_frame_path) return flow def get_flow_for_hybrid_motion_prev(frame_idx, dimensions, inputfiles, hybrid_frame_path, prev_img, method, do_flow_visualization=False): print(f"Calculating {method} optical flow for frames {frame_idx} to {frame_idx+1}") # first handle invalid images from cadence by returning default matrix height, width = prev_img.shape[:2] if height == 0 or width == 0: flow = get_hybrid_motion_default_flow(dimensions) else: i1 = prev_img.astype(np.uint8) i2 = get_resized_image_from_filename(str(inputfiles[frame_idx]), dimensions) flow = get_flow_from_images(i1, i2, method) if do_flow_visualization: save_flow_visualization(frame_idx, dimensions, flow, inputfiles, hybrid_frame_path) return flow def image_transform_ransac(image_cv2, xform, hybrid_motion, border_mode=cv2.BORDER_REPLICATE): if hybrid_motion == "Perspective": return image_transform_perspective(image_cv2, xform, border_mode=border_mode) else: # Affine return image_transform_affine(image_cv2, xform, border_mode=border_mode) def image_transform_optical_flow(img, flow, border_mode=cv2.BORDER_REPLICATE, flow_reverse=False): if not flow_reverse: flow = -flow h, w = img.shape[:2] flow[:, :, 0] += np.arange(w) flow[:, :, 1] += np.arange(h)[:,np.newaxis] return remap(img, flow, border_mode) def image_transform_affine(image_cv2, xform, border_mode=cv2.BORDER_REPLICATE): return cv2.warpAffine( image_cv2, xform, (image_cv2.shape[1],image_cv2.shape[0]), borderMode=border_mode ) def image_transform_perspective(image_cv2, xform, border_mode=cv2.BORDER_REPLICATE): return cv2.warpPerspective( image_cv2, xform, (image_cv2.shape[1], image_cv2.shape[0]), borderMode=border_mode ) def get_hybrid_motion_default_matrix(hybrid_motion): if hybrid_motion == "Perspective": arr = np.array([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]]) else: arr = np.array([[1., 0., 0.], [0., 1., 0.]]) return arr def get_hybrid_motion_default_flow(dimensions): cols, rows = dimensions flow = np.zeros((rows, cols, 2), np.float32) return flow def get_transformation_matrix_from_images(img1, img2, hybrid_motion, max_corners=200, quality_level=0.01, min_distance=30, block_size=3): # Detect feature points in previous frame prev_pts = cv2.goodFeaturesToTrack(img1, maxCorners=max_corners, qualityLevel=quality_level, minDistance=min_distance, blockSize=block_size) if prev_pts is None or len(prev_pts) < 8 or img1 is None or img2 is None: return get_hybrid_motion_default_matrix(hybrid_motion) # Get optical flow curr_pts, status, err = cv2.calcOpticalFlowPyrLK(img1, img2, prev_pts, None) # Filter only valid points idx = np.where(status==1)[0] prev_pts = prev_pts[idx] curr_pts = curr_pts[idx] if len(prev_pts) < 8 or len(curr_pts) < 8: return get_hybrid_motion_default_matrix(hybrid_motion) if hybrid_motion == "Perspective": # Perspective - Find the transformation between points transformation_matrix, mask = cv2.findHomography(prev_pts, curr_pts, cv2.RANSAC, 5.0) return transformation_matrix else: # Affine - Compute a rigid transformation (without depth, only scale + rotation + translation) transformation_rigid_matrix, rigid_mask = cv2.estimateAffinePartial2D(prev_pts, curr_pts) return transformation_rigid_matrix def get_flow_from_images(i1, i2, method): if method =="DIS Medium": r = get_flow_from_images_DIS(i1, i2, cv2.DISOPTICAL_FLOW_PRESET_MEDIUM) elif method =="DIS Fast": r = get_flow_from_images_DIS(i1, i2, cv2.DISOPTICAL_FLOW_PRESET_FAST) elif method =="DIS UltraFast": r = get_flow_from_images_DIS(i1, i2, cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST) elif method == "DenseRLOF": # requires running opencv-contrib-python (full opencv) INSTEAD of opencv-python r = get_flow_from_images_Dense_RLOF(i1, i2) elif method == "SF": # requires running opencv-contrib-python (full opencv) INSTEAD of opencv-python r = get_flow_from_images_SF(i1, i2) elif method =="Farneback Fine": r = get_flow_from_images_Farneback(i1, i2, 'fine') else: # Farneback Normal: r = get_flow_from_images_Farneback(i1, i2) return r def get_flow_from_images_DIS(i1, i2, preset): i1 = cv2.cvtColor(i1, cv2.COLOR_BGR2GRAY) i2 = cv2.cvtColor(i2, cv2.COLOR_BGR2GRAY) dis=cv2.DISOpticalFlow_create(preset) return dis.calc(i1, i2, None) def get_flow_from_images_Dense_RLOF(i1, i2, last_flow=None): return cv2.optflow.calcOpticalFlowDenseRLOF(i1, i2, flow = last_flow) def get_flow_from_images_SF(i1, i2, last_flow=None, layers = 3, averaging_block_size = 2, max_flow = 4): return cv2.optflow.calcOpticalFlowSF(i1, i2, layers, averaging_block_size, max_flow) def get_flow_from_images_Farneback(i1, i2, preset="normal", last_flow=None, pyr_scale = 0.5, levels = 3, winsize = 15, iterations = 3, poly_n = 5, poly_sigma = 1.2, flags = 0): flags = cv2.OPTFLOW_FARNEBACK_GAUSSIAN # Specify the operation flags pyr_scale = 0.5 # The image scale (<1) to build pyramids for each image if preset == "fine": levels = 13 # The number of pyramid layers, including the initial image winsize = 77 # The averaging window size iterations = 13 # The number of iterations at each pyramid level poly_n = 15 # The size of the pixel neighborhood used to find polynomial expansion in each pixel poly_sigma = 0.8 # The standard deviation of the Gaussian used to smooth derivatives used as a basis for the polynomial expansion else: # "normal" levels = 5 # The number of pyramid layers, including the initial image winsize = 21 # The averaging window size iterations = 5 # The number of iterations at each pyramid level poly_n = 7 # The size of the pixel neighborhood used to find polynomial expansion in each pixel poly_sigma = 1.2 # The standard deviation of the Gaussian used to smooth derivatives used as a basis for the polynomial expansion i1 = cv2.cvtColor(i1, cv2.COLOR_BGR2GRAY) i2 = cv2.cvtColor(i2, cv2.COLOR_BGR2GRAY) flags = 0 # flags = cv2.OPTFLOW_USE_INITIAL_FLOW flow = cv2.calcOpticalFlowFarneback(i1, i2, last_flow, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags) return flow def save_flow_visualization(frame_idx, dimensions, flow, inputfiles, hybrid_frame_path): flow_img_file = os.path.join(hybrid_frame_path, f"flow{frame_idx:05}.jpg") flow_img = cv2.imread(str(inputfiles[frame_idx])) flow_img = cv2.resize(flow_img, (dimensions[0], dimensions[1]), cv2.INTER_AREA) flow_img = cv2.cvtColor(flow_img, cv2.COLOR_RGB2GRAY) flow_img = cv2.cvtColor(flow_img, cv2.COLOR_GRAY2BGR) flow_img = draw_flow_lines_in_grid_in_color(flow_img, flow) flow_img = cv2.cvtColor(flow_img, cv2.COLOR_BGR2RGB) cv2.imwrite(flow_img_file, flow_img) print(f"Saved optical flow visualization: {flow_img_file}") def draw_flow_lines_in_grid_in_color(img, flow, step=8, magnitude_multiplier=1, min_magnitude = 1, max_magnitude = 10000): flow = flow * magnitude_multiplier h, w = img.shape[:2] y, x = np.mgrid[step/2:h:step, step/2:w:step].reshape(2,-1).astype(int) fx, fy = flow[y,x].T lines = np.vstack([x, y, x+fx, y+fy]).T.reshape(-1, 2, 2) lines = np.int32(lines + 0.5) vis = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) vis = cv2.cvtColor(vis, cv2.COLOR_GRAY2BGR) mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1]) hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8) hsv[...,0] = ang*180/np.pi/2 hsv[...,1] = 255 hsv[...,2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) vis = cv2.add(vis, bgr) # Iterate through the lines for (x1, y1), (x2, y2) in lines: # Calculate the magnitude of the line magnitude = np.sqrt((x2 - x1)**2 + (y2 - y1)**2) # Only draw the line if it falls within the magnitude range if min_magnitude <= magnitude <= max_magnitude: b = int(bgr[y1, x1, 0]) g = int(bgr[y1, x1, 1]) r = int(bgr[y1, x1, 2]) color = (b, g, r) cv2.arrowedLine(vis, (x1, y1), (x2, y2), color, thickness=1, tipLength=0.1) return vis def draw_flow_lines_in_color(img, flow, threshold=3, magnitude_multiplier=1, min_magnitude = 0, max_magnitude = 10000): # h, w = img.shape[:2] vis = img.copy() # Create a copy of the input image # Find the locations in the flow field where the magnitude of the flow is greater than the threshold mag, ang = cv2.cartToPolar(flow[...,0], flow[...,1]) idx = np.where(mag > threshold) # Create HSV image hsv = np.zeros((flow.shape[0], flow.shape[1], 3), dtype=np.uint8) hsv[...,0] = ang*180/np.pi/2 hsv[...,1] = 255 hsv[...,2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX) # Convert HSV image to BGR bgr = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR) # Add color from bgr vis = cv2.add(vis, bgr) # Draw an arrow at each of these locations to indicate the direction of the flow for i, (y, x) in enumerate(zip(idx[0], idx[1])): # Calculate the magnitude of the line x2 = x + magnitude_multiplier * int(flow[y, x, 0]) y2 = y + magnitude_multiplier * int(flow[y, x, 1]) magnitude = np.sqrt((x2 - x)**2 + (y2 - y)**2) # Only draw the line if it falls within the magnitude range if min_magnitude <= magnitude <= max_magnitude: if i % random.randint(100, 200) == 0: b = int(bgr[y, x, 0]) g = int(bgr[y, x, 1]) r = int(bgr[y, x, 2]) color = (b, g, r) cv2.arrowedLine(vis, (x, y), (x2, y2), color, thickness=1, tipLength=0.25) return vis def autocontrast_grayscale(image, low_cutoff=0, high_cutoff=100): # Perform autocontrast on a grayscale np array image. # Find the minimum and maximum values in the image min_val = np.percentile(image, low_cutoff) max_val = np.percentile(image, high_cutoff) # Scale the image so that the minimum value is 0 and the maximum value is 255 image = 255 * (image - min_val) / (max_val - min_val) # Clip values that fall outside the range [0, 255] image = np.clip(image, 0, 255) return image def get_resized_image_from_filename(im, dimensions): img = cv2.imread(im) return cv2.resize(img, (dimensions[0], dimensions[1]), cv2.INTER_AREA) def remap(img, flow, border_mode = cv2.BORDER_REFLECT_101): # copyMakeBorder doesn't support wrap, but supports replicate. Replaces wrap with reflect101. if border_mode == cv2.BORDER_WRAP: border_mode = cv2.BORDER_REFLECT_101 h, w = img.shape[:2] displacement = int(h * 0.25), int(w * 0.25) larger_img = cv2.copyMakeBorder(img, displacement[0], displacement[0], displacement[1], displacement[1], border_mode) lh, lw = larger_img.shape[:2] larger_flow = extend_flow(flow, lw, lh) remapped_img = cv2.remap(larger_img, larger_flow, None, cv2.INTER_LINEAR, border_mode) output_img = center_crop_image(remapped_img, w, h) return output_img def center_crop_image(img, w, h): y, x, _ = img.shape width_indent = int((x - w) / 2) height_indent = int((y - h) / 2) cropped_img = img[height_indent:y-height_indent, width_indent:x-width_indent] return cropped_img def extend_flow(flow, w, h): # Get the shape of the original flow image flow_h, flow_w = flow.shape[:2] # Calculate the position of the image in the new image x_offset = int((w - flow_w) / 2) y_offset = int((h - flow_h) / 2) # Generate the X and Y grids x_grid, y_grid = np.meshgrid(np.arange(w), np.arange(h)) # Create the new flow image and set it to the X and Y grids new_flow = np.dstack((x_grid, y_grid)).astype(np.float32) # Shift the values of the original flow by the size of the border flow[:,:,0] += x_offset flow[:,:,1] += y_offset # Overwrite the middle of the grid with the original flow new_flow[y_offset:y_offset+flow_h, x_offset:x_offset+flow_w, :] = flow # Return the extended image return new_flow