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Use scale_factor instead of scale in find_features
* Updated all scripts.
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@ -2127,7 +2127,7 @@ static mp_obj_t py_image_find_features(uint n_args, const mp_obj_t *args, mp_map
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cascade_t *cascade = py_cascade_cobj(args[1]);
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cascade->threshold = py_helper_lookup_float(kw_args, MP_OBJ_NEW_QSTR(MP_QSTR_threshold), 0.5f);
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cascade->scale_factor = py_helper_lookup_float(kw_args, MP_OBJ_NEW_QSTR(MP_QSTR_scale), 1.5f);
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cascade->scale_factor = py_helper_lookup_float(kw_args, MP_OBJ_NEW_QSTR(MP_QSTR_scale_factor), 1.5f);
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rectangle_t arg_r;
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py_helper_lookup_rectangle(kw_args, arg_img, &arg_r);
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@ -39,7 +39,7 @@ while(True):
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# Threshold can be between 0.0 and 1.0. A higher threshold results in a
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# higher detection rate with more false positives. The scale value
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# controls the matching scale allowing you to detect smaller faces.
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faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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if faces:
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diff -= 1
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@ -43,7 +43,7 @@ while(True):
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# Threshold can be between 0.0 and 1.0. A higher threshold results in a
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# higher detection rate with more false positives. The scale value
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# controls the matching scale allowing you to detect smaller faces.
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faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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if faces:
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diff -= 1
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@ -44,7 +44,7 @@ while(True):
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# Threshold can be between 0.0 and 1.0. A higher threshold results in a
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# higher detection rate with more false positives. The scale value
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# controls the matching scale allowing you to detect smaller faces.
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faces = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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faces = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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if faces:
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diff -= 1
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@ -40,7 +40,7 @@ while (True):
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# Find objects.
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# Note: Lower scale factor scales-down the image more and detects smaller objects.
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# Higher threshold results in a higher detection rate, with more false positives.
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objects = img.find_features(face_cascade, threshold=0.75, scale=1.35)
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objects = img.find_features(face_cascade, threshold=0.75, scale_factor=1.35)
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# Draw objects
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for r in objects:
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@ -50,7 +50,7 @@ while (kpts1 == None):
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img = sensor.snapshot()
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img.draw_string(0, 0, "Looking for a face...")
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# Find faces
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objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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if objects:
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# Expand the ROI by 11 pixels in each direction (half the pattern scale)
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face = (objects[0][0]-22, objects[0][1]-22,objects[0][2]+22*2, objects[0][3]+22*2)
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@ -33,14 +33,14 @@ while (True):
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# Find a face !
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# Note: Lower scale factor scales-down the image more and detects smaller objects.
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# Higher threshold results in a higher detection rate, with more false positives.
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objects = img.find_features(face_cascade, threshold=0.5, scale=1.5)
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objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.5)
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# Draw faces
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for face in objects:
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img.draw_rectangle(face)
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# Now find eyes within each face.
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# Note: Use a higher threshold here (more detections) and lower scale (to find small objects)
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eyes = img.find_features(eyes_cascade, threshold=0.5, scale=1.2, roi=face)
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eyes = img.find_features(eyes_cascade, threshold=0.5, scale_factor=1.2, roi=face)
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for e in eyes:
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img.draw_rectangle(e)
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@ -39,7 +39,7 @@ while (True):
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# Find eyes !
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# Note: Lower scale factor scales-down the image more and detects smaller objects.
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# Higher threshold results in a higher detection rate, with more false positives.
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eyes = img.find_features(eyes_cascade, threshold=0.5, scale=1.5)
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eyes = img.find_features(eyes_cascade, threshold=0.5, scale_factor=1.5)
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# Find iris
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for e in eyes:
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@ -38,7 +38,7 @@ while (True):
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clock.tick()
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img = sensor.snapshot()
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objects = img.find_features(face_cascade, threshold=0.5, scale=1.25)
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objects = img.find_features(face_cascade, threshold=0.5, scale_factor=1.25)
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if objects:
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face = objects[0]
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d1 = img.find_lbp(face)
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