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How to use transform_lazy method in pandera

Best Python code snippet using pandera_python

avi.py

Source:avi.py

...43                    for frame_i in frames]44        mask_i=np.mean(frames,axis=0)45        frames=[frame_i-mask_i for frame_i in frames]46        return frames47    data.imgs.transform_lazy(in_path,out_path,helper,recreate=True)48def remove_noise(in_path,out_path):49    se1 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))50    se2 = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))51    def helper(name_i,action_i):52        mask = cv2.morphologyEx(action_i, cv2.MORPH_CLOSE, se1)53        mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, se2)54        action_i= mask*action_i55        action_i[action_i!=0]=10056        return action_i57    data.actions.transform_lazy(in_path,out_path,helper)58def detect_circles(in_path,out_path):59    minDist,param1,param2 = 100,30,50  60    minRadius,maxRadius = 5,2061    def helper(name_i,frame_i):62        gray = cv2.cvtColor(frame_i, cv2.COLOR_BGR2GRAY)63        blurred = cv2.medianBlur(gray, 25)64        circles = cv2.HoughCircles(blurred, cv2.HOUGH_GRADIENT, 1, 65            minDist, param1=param1, param2=param2, 66            minRadius=minRadius, maxRadius=maxRadius)67        if circles is not None:68            circles = np.uint16(np.around(circles))69            for i in circles[0,:]:70                cv2.circle(frame_i, (i[0], i[1]), i[2], (0, 255, 0), 2)71        return frame_i72    data.imgs.transform_lazy(in_path,out_path,helper,single=True)73def find_tag(in_path,out_path):74    def helper(name_i,frame_i):75        frame_i[:,:,1][frame_i[:,:,0]!=0]=076        frame_i[:,:,1][frame_i[:,:,0]==0]=1 77        frame_i[:,:,2][frame_i[:,:,2]==0]=078        tag=frame_i[:,:,1]*frame_i[:,:,2]79        tag=cv2.medianBlur(tag,5)80        return tag81    data.imgs.transform_lazy(in_path,out_path,helper,82         recreate=True,single=True)83in_path="../../raw"84rgb_path="../../rgb"85#tag_path86box_path="../../box"87action_path="../../actions"88#to_rgb(in_path,out_path)89#simple(rgb_path,box_path)90subs_background(rgb_path,"../../cc/box")91#remove_noise(action_path,"../../actions2")...

background.py

Source:background.py

...23        action_i=backgrounds(name_i,action_i)24        action_i=remove_mc(action_i,k=20)25        action_i=largest_cc(action_i)26        return action_i27    data.actions.transform_lazy(action_path,out_path,helper)28def remove_mc(diff_i,k=20):29    u, c = np.unique(diff_i, return_counts=True)30    largest=c.argsort()[-k:]31    for l in largest:32        diff_i[diff_i==u[l]]=033    return diff_i34def largest_cc(segmentation):35    segmentation[segmentation>0]=136    labels = label(segmentation)37    largestCC = (labels == (np.argmax(np.bincount(labels.flat)[1:])+1))38    segmentation[  largestCC]=10039    segmentation[segmentation!=100]=040    return segmentation41def to_grey(frames):42    if(type(frames)==list):43        return [cv2.cvtColor(frame_i, cv2.COLOR_BGR2GRAY)44                    for frame_i in frames45                        if(not (frame_i is None))]46    return cv2.cvtColor(frames, cv2.COLOR_BGR2GRAY)47def final_preproc(in_path,out_path):48    def helper(name_i,img_i):49        img_i=cv2.resize(img_i,dsize=(64,128),50                    interpolation=cv2.INTER_CUBIC)51        return img_i52#        return to_grey(frames)53    data.imgs.transform_lazy(in_path,out_path,helper,single=True)54if __name__ == "__main__":55    in_path="../../Background/raw"56    action_path="../../actions"57    out_path="../../actions2"58    #substract(in_path,action_path,out_path)...

transform.py

Source:transform.py

...7        frames=to_grey(frames)8        size=len(frames)9        return [ np.abs(frames[i]-frames[i-1]) 10                    for i in range(1,size)]11    data.imgs.transform_lazy(in_path,out_path,fun,single=False)12def get_frames(in_path,out_path,fun=None):13    if(fun is None):14        fun=detect_edges15    data.actions.get_actions_lazy(in_path,out_path,fun,read=None)16def get_seqs(in_path,out_path,fun=None):17    if(fun is None):18        fun=get_edges    19    data.imgs.transform_lazy(in_path,out_path,fun,single=True)20def detect_edges(name,frames):21    final=[]22    for frame_i in frames:23        final.append(get_edges(name_i,frame_i))24    action_img=np.mean(final,axis=0)25    action_img[action_img!=0]=10026    return action_img27def get_edges(name_i,frame_i):28    import cv2    29    frame_i=cv2.cvtColor(frame_i, cv2.COLOR_BGR2GRAY)30    frame_i=cv2.medianBlur(frame_i,5)31    frame_i=cv2.Canny(frame_i , 100, 200)32    return frame_i33def to_grey(frames):34    return [cv2.cvtColor(frame_i, cv2.COLOR_BGR2GRAY)35                    for frame_i in frames36                        if(not (frame_i is None))]37def to_grey_transform(in_path,out_path):38    def helper(name_i,frames):39        print(name_i)40        return to_grey(frames)41    data.imgs.transform_lazy(in_path,out_path,helper,single=False)42in_path="../cc2/final"43out_path="../../2021_XII/final"...

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