Update app.py

app.py

@@ -136,105 +136,6 @@ def infer(f_in, interpolation, fps_output):
     return final_vid, files
 
 
-def remove_bg(fl, s, l, v, l_t):
-    frame = cv2.imread(fl).astype(np.uint8)
-    
-    b = 5
-    #subtract background (get scene with shadow)
-    bg = cv2.medianBlur(frame, 255)
-    
-    frame_ = ((bg.astype(np.int16)-frame.astype(np.int16))+127).astype(np.uint8)
-    frame_ = cv2.bilateralFilter(frame_, b*4, b*8, b*2)
-    frame_ = cv2.medianBlur(frame_, b)
-    
-    element = cv2.getStructuringElement(cv2.MORPH_RECT, (2*b+1, 2*b+1), (b,b))
-    frame_ = cv2.erode(cv2.dilate(frame_, element), element)
-
-    
-    #correct hue against light
-    bg_gray = cv2.cvtColor(cv2.cvtColor(bg, cv2.COLOR_BGR2GRAY), cv2.COLOR_GRAY2BGR)
-    bg_diff = (bg-bg_gray).astype(np.int16)
-    frame_c = (frame.astype(np.int16)-bg_diff).astype(np.uint8)
-
-
-    hsv_ = cv2.cvtColor(frame_c, cv2.COLOR_BGR2HSV)
-    edges = cv2.Laplacian(cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), cv2.CV_64F)
-    blur_s = np.zeros_like(edges)
-    for i in range(2, frame.shape[0]-2):
-        for j in range(2, frame.shape[1]-2):
-            d = edges[i-2:i+2, j-2:j+2].var()
-            blur_s[i,j] = d.astype(np.uint8)
-
-    print(fl)
-    print("detail")
-    print(np.average(blur_s))
-    print(np.median(blur_s))
-    print("saturation")
-    print(np.average(hsv_[:,:,1]))
-    print(np.median(hsv_[:,:,1]))
-    print("lightness")
-    print(np.average(hsv_[:,:,2]))
-    print(np.median(hsv_[:,:,2]))
-
-    #remove regions of low saturation, lightness and detail (get scene without shadow)
-    if l_t == "slider":
-        m = cv2.inRange(hsv_, np.array([0,0,0]), np.array([180,s,l]))
-        mask = cv2.inRange(blur_s, 0, v)
-    elif l_t == "average":
-        m = cv2.inRange(hsv_, np.array([0,0,0]), np.array([180, int(np.average(hsv_[:,:,1])), int(np.average(hsv_[:,:,2]))]))
-        mask = cv2.inRange(blur_s, 0, int(np.average(blur_s)))
-    elif l_t == "median":
-        m = cv2.inRange(hsv_, np.array([0,0,0]), np.array([180, int(np.median(hsv_[:,:,1])), int(np.median(hsv_[:,:,2]))]))
-        mask = cv2.inRange(blur_s, 0, int(np.median(blur_s)))
-        
-    masks = np.bitwise_and(m, mask)
-    frame_[masks==0] = (0,0,0)
-    
-    
-    m_ = frame_.reshape((-1,3))
-    # convert to np.float32
-    m_ = np.float32(m_)
-    # define criteria, number of clusters(K) and apply kmeans()
-    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 16, 1.0)
-    K = 3
-    ret,label,center=cv2.kmeans(m_, K, None, criteria, 16, cv2.KMEANS_PP_CENTERS)
-    # Now convert back into uint8, and make original image
-    center = np.uint8(center)
-    res = center[label.flatten()]
-    frame_ = res.reshape((frame_.shape))
-    
-    
-    #remove shadows at edges
-    m_ = cv2.inRange(frame_, np.array([128,128,128]), np.array([255,255,255]))
-    frame_[m_>0] = (255,255,255)
-    cv2.rectangle(frame_,(0,0),(frame_.shape[1]-1,frame_.shape[0]-1),(255,255,255),7)
-    mask = cv2.floodFill(frame_, None, (0, 0), 255, 0, 0, (4 | cv2.FLOODFILL_FIXED_RANGE))[2] #(4 | cv2.FLOODFILL_FIXED_RANGE | cv2.FLOODFILL_MASK_ONLY | 255 << 8)
-    # 255 << 8 tells to fill with the value 255)
-    mask = mask[1:mask.shape[0]-1, 1:mask.shape[1]-1]
-    frame_[mask>0] = (127,127,127)
-    m_ = cv2.inRange(frame_, np.array([1,1,1]), np.array([127,127,127]))
-    frame_[m_>0] = (127,127,127)
-
-
-    #shadow is black, bg is white, fg is gray
-    frame_ = 255 - cv2.cvtColor(frame_, cv2.COLOR_BGR2GRAY)
-    m_ = cv2.inRange(frame_, 255, 255)
-    frame_[m_>0] = 127
-    m_ = cv2.inRange(frame_, 128, 128)
-    frame_[m_>0] = 255
-
-
-    #apply mask to output
-    m = cv2.inRange(frame, np.array([240,240,240]), np.array([255,255,255]))
-    frame[m>0] = (239,239,239)
-    m = cv2.inRange(frame, np.array([0,0,0]), np.array([15,15,15]))
-    frame[m>0] = (16,16,16)
-    frame[frame_==0] = (frame[frame_==0] / 17).astype(np.uint8)
-    frame[frame_==255] = (255,255,255)
-
-    cv2.imwrite(fl, frame)
-    return fl
-
 def logscale(linear):
     return int(math.pow(2, linear))
 
@@ -278,7 +179,7 @@ def sortFiles(e):
     e = e.split('/')
     return e[len(e)-1]
 
-def loadf(f, s, l, v, l_t, r_bg):
+def loadf(f):
     if f != None and f[0] != None:
         f.sort(key=sortFiles)
         fnew = []
@@ -287,12 +188,8 @@ def loadf(f, s, l, v, l_t, r_bg):
             ftype = fl.split('/')
             if ftype[len(ftype)-1].split('.')[1] == 'mp4':
                 fl = sharpest(fl, i)
-
-            if r_bg == True:
-                fl = remove_bg(fl, s, l, v, l_t)
                 
             fnew.append(fl)
-
         return fnew, fnew
     else:
         return f, f
@@ -330,18 +227,7 @@ with gr.Blocks() as demo:
                     files_orig = gr.File(file_count="multiple", file_types=['image', '.mp4'])
                     files_input = gr.File(file_count="multiple", visible=False)
                 gallery_input = gr.Gallery(label="Slideshow", preview=True, columns=8192, interactive=False)
-                with gr.Accordion(label="Background removal settings", open=False):
-                    with gr.Tab(label="Shadow maximums"):
-                        max_s = gr.Slider(minimum=0, maximum=255, step=1, value=32, label="Saturation")
-                        max_l = gr.Slider(minimum=0, maximum=255, step=1, value=64, label="Lightness")
-                        max_v = gr.Slider(minimum=0, maximum=255, step=1, value=16, label="Detail")
-                    lt = gr.Radio(label="Maximum is", choices=["average", "median", "slider"], value="slider")
-                    rbg = gr.Checkbox(label="Remove background", value=True)
-                files_orig.upload(fn=loadf, inputs=[files_orig, max_s, max_l, max_v, lt, rbg], outputs=[files_input, gallery_input])
-                max_s.change(fn=loadf, inputs=[files_orig, max_s, max_l, max_v, lt, rbg], outputs=[files_input, gallery_input])
-                max_l.change(fn=loadf, inputs=[files_orig, max_s, max_l, max_v, lt, rbg], outputs=[files_input, gallery_input])
-                max_v.change(fn=loadf, inputs=[files_orig, max_s, max_l, max_v, lt, rbg], outputs=[files_input, gallery_input])
-                lt.change(fn=loadf, inputs=[files_orig, max_s, max_l, max_v, lt, rbg], outputs=[files_input, gallery_input])
+                files_orig.upload(fn=loadf, inputs=[files_orig], outputs=[files_input, gallery_input])
                 
                 with gr.Row():
                     interpolation_slider = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Interpolation Steps: ")
@@ -359,11 +245,10 @@ with gr.Blocks() as demo:
     
     gr.Examples(
         examples=[[
-            ["./examples/0.png", "./examples/1.png", "./examples/2.png", "./examples/3.png", "./examples/4.png"], 
-            32, 64, 16, "slider", True
+            ["./examples/0.png", "./examples/1.png", "./examples/2.png", "./examples/3.png", "./examples/4.png"]
         ]],
         fn=loadf,
-        inputs=[files_orig, max_s, max_l, max_v, lt, rbg],
+        inputs=[files_orig],
         outputs=[files_input, gallery_input],
         cache_examples=True
     )